fix imports

.coderabbit.yaml

@@ -1,127 +0,0 @@
-# yaml-language-server: $schema=https://coderabbit.ai/integrations/schema.v2.json
-# .coderabbit.yaml
-language: en
-early_access: false
-enable_free_tier: true
-reviews:  
-  profile: chill
-  instructions: >-
-    # Code Review Instructions
-
-    - Ensure the code follows best practices and coding standards.
-    - For **Python** code, follow
-      [PEP 20](https://www.python.org/dev/peps/pep-0020/) and
-      [CEP-8](https://gist.github.com/reactive-firewall/b7ee98df9e636a51806e62ef9c4ab161)
-      standards.
-
-    # Documentation Review Instructions
-    - Verify that documentation and comments are clear and comprehensive.
-    - Verify that documentation and comments are free of spelling mistakes.
-
-    # Test Code Review Instructions
-    - Ensure that test code is automated, comprehensive, and follows testing best practices.
-    - Verify that all critical functionality is covered by tests.
-    - Ensure that test code follow
-      [CEP-8](https://gist.github.com/reactive-firewall/d840ee9990e65f302ce2a8d78ebe73f6)
-
-    # Misc.
-    - Confirm that the code meets the project's requirements and objectives.
-    - Confirm that copyright years are up-to date whenever a file is changed.
-  request_changes_workflow: false
-  high_level_summary: true
-  high_level_summary_placeholder: '@coderabbitai summary'
-  auto_title_placeholder: '@coderabbitai'
-  review_status: true
-  poem: false
-  collapse_walkthrough: false
-  sequence_diagrams: false
-  changed_files_summary: true
-  path_filters: ['!*.xc*/**', '!node_modules/**', '!dist/**', '!build/**', '!.git/**', '!venv/**', '!__pycache__/**']
-  path_instructions:
-    - path: README.md
-      instructions: >-
-        1. Consider the file 'README.md' the overview/introduction of the project.
-           Also consider the 'README.md' file the first place to look for project documentation.
-
-        2. When reviewing the file 'README.md' it should be linted with help
-           from the tools `markdownlint` and `languagetool`, pointing out any issues.
-
-        3. You may assume the file 'README.md' will contain GitHub flavor Markdown.
-    - path: '**/*.py'
-      instructions: >-
-        When reviewing Python code for this project:
-
-        1. Prioritize portability over clarity, especially when dealing with cross-Python compatibility. However, with the priority in mind, do still consider improvements to clarity when relevant.
-
-        2. As a general guideline, consider the code style advocated in the PEP 8 standard (excluding the use of spaces for indentation) and evaluate suggested changes for code style compliance.
-
-        3. As a style convention, consider the code style advocated in [CEP-8](https://gist.github.com/reactive-firewall/b7ee98df9e636a51806e62ef9c4ab161) and evaluate suggested changes for code style compliance.
-
-        4. As a general guideline, try to provide any relevant, official, and supporting documentation links to any tool's suggestions in review comments. This guideline is important for posterity.
-
-        5. As a general rule, undocumented function definitions and class definitions in the project's Python code are assumed incomplete. Please consider suggesting a short summary of the code for any of these incomplete definitions as docstrings when reviewing.
-    - path: cognee/tests/*
-      instructions: >-
-        When reviewing test code:
-
-        1. Prioritize portability over clarity, especially when dealing with cross-Python compatibility. However, with the priority in mind, do still consider improvements to clarity when relevant.
-
-        2. As a general guideline, consider the code style advocated in the PEP 8 standard (excluding the use of spaces for indentation) and evaluate suggested changes for code style compliance.
-
-        3. As a style convention, consider the code style advocated in [CEP-8](https://gist.github.com/reactive-firewall/b7ee98df9e636a51806e62ef9c4ab161) and evaluate suggested changes for code style compliance, pointing out any violations discovered.
-
-        4. As a general guideline, try to provide any relevant, official, and supporting documentation links to any tool's suggestions in review comments. This guideline is important for posterity.
-
-        5. As a project rule, Python source files with names prefixed by the string "test_" and located in the project's "tests" directory are the project's unit-testing code. It is safe, albeit a heuristic, to assume these are considered part of the project's minimal acceptance testing unless a justifying exception to this assumption is documented.
-
-        6. As a project rule, any files without extensions and with names prefixed by either the string "check_" or the string "test_", and located in the project's "tests" directory, are the project's non-unit test code. "Non-unit test" in this context refers to any type of testing other than unit testing, such as (but not limited to) functional testing, style linting, regression testing, etc. It can also be assumed that non-unit testing code is usually written as Bash shell scripts.
-    - path: requirements.txt
-      instructions: >-
-        * The project's own Python dependencies are recorded in 'requirements.txt' for production code.
-
-        * The project's testing-specific Python dependencies are recorded in 'tests/requirements.txt' and are used for testing the project.
-
-        * The project's documentation-specific Python dependencies are recorded in 'docs/requirements.txt' and are used only for generating Python-focused documentation for the project. 'docs/requirements.txt' may be absent if not applicable.
-
-        Consider these 'requirements.txt' files the records of truth regarding project dependencies.
-    - path: .github/**
-      instructions: >-
-        * When the project is hosted on GitHub: All GitHub-specific configurations, templates, and tools should be found in the '.github' directory tree.
-
-        * 'actionlint' erroneously generates false positives when dealing with GitHub's `${{ ... }}` syntax in conditionals.
-
-        * 'actionlint' erroneously generates incorrect solutions when suggesting the removal of valid `${{ ... }}` syntax.
-  abort_on_close: true
-  auto_review:
-    enabled: true
-    auto_incremental_review: true
-    ignore_title_keywords: []
-    labels: []
-    drafts: false
-    base_branches:
-      - dev
-      - main
-  tools:
-    shellcheck:
-      enabled: true
-    ruff:
-      enabled: true
-      configuration:
-        extend_select:
-          - E  # Pycodestyle errors (style issues)
-          - F  # PyFlakes codes (logical errors)
-          - W  # Pycodestyle warnings
-          - N  # PEP 8 naming conventions
-        ignore:
-          - W191
-          - W391
-          - E117
-          - D208
-        line_length: 100
-        dummy_variable_rgx: '^(_.*|junk|extra)$'  # Variables starting with '_' or named 'junk' or 'extras', are considered dummy variables        
-    markdownlint:
-      enabled: true
-    yamllint:
-      enabled: true
-chat:
-  auto_reply: true

.env.template

@@ -21,10 +21,6 @@ LLM_PROVIDER="openai"
 LLM_ENDPOINT=""
 LLM_API_VERSION=""
 LLM_MAX_TOKENS="16384"
-# Instructor's modes determine how structured data is requested from and extracted from LLM responses
-# You can change this type (i.e. mode) via this env variable
-# Each LLM has its own default value, e.g. gpt-5 models have "json_schema_mode"
-LLM_INSTRUCTOR_MODE=""
 
 EMBEDDING_PROVIDER="openai"
 EMBEDDING_MODEL="openai/text-embedding-3-large"
@@ -97,8 +93,6 @@ DB_NAME=cognee_db
 
 # Default (local file-based)
 GRAPH_DATABASE_PROVIDER="kuzu"
-# Handler for multi-user access control mode, it handles how should the mapping/creation of separate DBs be handled per Cognee dataset
-GRAPH_DATASET_DATABASE_HANDLER="kuzu"
 
 # -- To switch to Remote Kuzu uncomment and fill these: -------------------------------------------------------------
 #GRAPH_DATABASE_PROVIDER="kuzu"
@@ -123,8 +117,6 @@ VECTOR_DB_PROVIDER="lancedb"
 # Not needed if a cloud vector database is not used
 VECTOR_DB_URL=
 VECTOR_DB_KEY=
-# Handler for multi-user access control mode, it handles how should the mapping/creation of separate DBs be handled per Cognee dataset
-VECTOR_DATASET_DATABASE_HANDLER="lancedb"
 
 ################################################################################
 # 🧩 Ontology resolver settings
@@ -177,9 +169,8 @@ REQUIRE_AUTHENTICATION=False
 #       Vector: LanceDB
 #       Graph: KuzuDB
 #
-# It enforces creation of databases per Cognee user + dataset. Does not work with some graph and database providers.
-# Disable mode when using not supported graph/vector databases.
-ENABLE_BACKEND_ACCESS_CONTROL=True
+# It enforces LanceDB and KuzuDB use and uses them to create databases per Cognee user + dataset
+ENABLE_BACKEND_ACCESS_CONTROL=False
 
 ################################################################################
 # ☁️ Cloud Sync Settings
@@ -251,14 +242,13 @@ LITELLM_LOG="ERROR"
 
 ########## Local LLM via Ollama ###############################################
 
-
 #LLM_API_KEY ="ollama"
 #LLM_MODEL="llama3.1:8b"
 #LLM_PROVIDER="ollama"
 #LLM_ENDPOINT="http://localhost:11434/v1"
 #EMBEDDING_PROVIDER="ollama"
 #EMBEDDING_MODEL="nomic-embed-text:latest"
-#EMBEDDING_ENDPOINT="http://localhost:11434/api/embed"
+#EMBEDDING_ENDPOINT="http://localhost:11434/api/embeddings"
 #EMBEDDING_DIMENSIONS=768
 #HUGGINGFACE_TOKENIZER="nomic-ai/nomic-embed-text-v1.5"
 

.github/actions/cognee_setup/action.yml

@@ -10,10 +10,6 @@ inputs:
     description: "Additional extra dependencies to install (space-separated)"
     required: false
     default: ""
-  rebuild-lockfile:
-    description: "Whether to rebuild the uv lockfile"
-    required: false
-    default: "false"
 
 runs:
   using: "composite"
@@ -30,7 +26,6 @@ runs:
         enable-cache: true
 
     - name: Rebuild uv lockfile
-      if: ${{ inputs.rebuild-lockfile == 'true' }}
       shell: bash
       run: |
         rm uv.lock
@@ -47,8 +42,3 @@ runs:
           done
         fi
         uv sync --extra api --extra docs --extra evals --extra codegraph --extra ollama --extra dev --extra neo4j --extra redis $EXTRA_ARGS
-
-    - name: Add telemetry identifier for telemetry test and in case telemetry is enabled by accident
-      shell: bash
-      run: |
-        echo "test-machine" > .anon_id

.github/pull_request_template.md

@@ -6,14 +6,6 @@ Please provide a clear, human-generated description of the changes in this PR.
 DO NOT use AI-generated descriptions. We want to understand your thought process and reasoning.
 -->
 
-## Acceptance Criteria
-<!--
-* Key requirements to the new feature or modification;
-* Proof that the changes work and meet the requirements;
-* Include instructions on how to verify the changes. Describe how to test it locally;
-* Proof that it's sufficiently tested.
--->
-
 ## Type of Change
 <!-- Please check the relevant option -->
 - [ ] Bug fix (non-breaking change that fixes an issue)

.github/release-drafter.yml

@@ -1,20 +0,0 @@
-name-template: 'v$NEXT_PATCH_VERSION'
-tag-template: 'v$NEXT_PATCH_VERSION'
-
-categories:
-  - title: 'Features'
-    labels: ['feature', 'enhancement']    
-  - title: 'Bug Fixes'
-    labels: ['bug', 'fix']
-  - title: 'Maintenance'
-    labels: ['chore', 'refactor', 'ci']
-
-change-template: '- $TITLE (#$NUMBER) @$AUTHOR'
-template: |
-  ## What’s Changed
-
-  $CHANGES
-
-  ## Contributors
-
-  $CONTRIBUTORS

.github/workflows/basic_tests.yml

@@ -75,7 +75,6 @@ jobs:
     name: Run Unit Tests
     runs-on: ubuntu-22.04
     env:
-      ENV: 'dev'
       LLM_PROVIDER: openai
       LLM_MODEL: ${{ secrets.LLM_MODEL }}
       LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
@@ -105,7 +104,6 @@ jobs:
     name: Run Integration Tests
     runs-on: ubuntu-22.04
     env:
-      ENV: 'dev'
       LLM_PROVIDER: openai
       LLM_MODEL: ${{ secrets.LLM_MODEL }}
       LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
@@ -134,7 +132,6 @@ jobs:
     name: Run Simple Examples
     runs-on: ubuntu-22.04
     env:
-      ENV: 'dev'
       LLM_PROVIDER: openai
       LLM_MODEL: ${{ secrets.LLM_MODEL }}
       LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
@@ -164,7 +161,6 @@ jobs:
     name: Run Simple Examples BAML
     runs-on: ubuntu-22.04
     env:
-      ENV: 'dev'
       STRUCTURED_OUTPUT_FRAMEWORK: "BAML"
       BAML_LLM_PROVIDER: openai
       BAML_LLM_MODEL: ${{ secrets.OPENAI_MODEL }}
@@ -197,3 +193,32 @@ jobs:
 
       - name: Run Simple Examples
         run: uv run python ./examples/python/simple_example.py
+
+  graph-tests:
+    name: Run Basic Graph Tests
+    runs-on: ubuntu-22.04
+    env:
+      LLM_PROVIDER: openai
+      LLM_MODEL: ${{ secrets.LLM_MODEL }}
+      LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
+      LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
+      LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
+
+      EMBEDDING_PROVIDER: openai
+      EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
+      EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
+      EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
+      EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
+    steps:
+      - name: Check out repository
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: Cognee Setup
+        uses: ./.github/actions/cognee_setup
+        with:
+          python-version: ${{ inputs.python-version }}
+
+      - name: Run Graph Tests
+        run: uv run python ./examples/python/code_graph_example.py --repo_path ./cognee/tasks/graph

.github/workflows/cli_tests.yml

@@ -39,7 +39,6 @@ jobs:
     name: CLI Unit Tests
     runs-on: ubuntu-22.04
     env:
-      ENV: 'dev'
       LLM_PROVIDER: openai
       LLM_MODEL: ${{ secrets.LLM_MODEL }}
       LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
@@ -67,7 +66,6 @@ jobs:
     name: CLI Integration Tests
     runs-on: ubuntu-22.04
     env:
-      ENV: 'dev'
       LLM_PROVIDER: openai
       LLM_MODEL: ${{ secrets.LLM_MODEL }}
       LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
@@ -95,7 +93,6 @@ jobs:
     name: CLI Functionality Tests
     runs-on: ubuntu-22.04
     env:
-      ENV: 'dev'
       LLM_PROVIDER: openai
       LLM_MODEL: ${{ secrets.LLM_MODEL }}
       LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}

.github/workflows/db_examples_tests.yml

@@ -60,8 +60,7 @@ jobs:
 
       - name: Run Neo4j Example
         env:
-          ENV: 'dev'
-          ENABLE_BACKEND_ACCESS_CONTROL: 'false'
+          ENV: dev
           LLM_MODEL: ${{ secrets.LLM_MODEL }}
           LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
           LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
@@ -96,7 +95,7 @@ jobs:
 
       - name: Run Kuzu Example
         env:
-          ENV: 'dev'
+          ENV: dev
           LLM_MODEL: ${{ secrets.LLM_MODEL }}
           LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
           LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
@@ -142,8 +141,7 @@ jobs:
 
       - name: Run PGVector Example
         env:
-          ENV: 'dev'
-          ENABLE_BACKEND_ACCESS_CONTROL: 'false'
+          ENV: dev
           LLM_MODEL: ${{ secrets.LLM_MODEL }}
           LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
           LLM_API_KEY: ${{ secrets.LLM_API_KEY }}

.github/workflows/distributed_test.yml

@@ -47,7 +47,6 @@ jobs:
       - name: Run Distributed Cognee (Modal)
         env:
           ENV: 'dev'
-          ENABLE_BACKEND_ACCESS_CONTROL: 'false'
           LLM_MODEL: ${{ secrets.LLM_MODEL }}
           LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
           LLM_API_KEY: ${{ secrets.LLM_API_KEY }}

.github/workflows/dockerhub-mcp.yml

@@ -7,29 +7,14 @@ on:
 
 jobs:
   docker-build-and-push:
-    runs-on:
-      group: Default
-      labels:
-        - docker_build_runner
+    runs-on: ubuntu-latest
 
     steps:
-      - name: Check and free disk space before build
-        run: |
-          echo "=== Before cleanup ==="
-          df -h
-          echo "Removing unused preinstalled SDKs to free space..."
-          sudo rm -rf /usr/share/dotnet /usr/local/lib/android /opt/ghc || true
-          docker system prune -af || true
-          echo "=== After cleanup ==="
-          df -h
-
       - name: Checkout repository
         uses: actions/checkout@v4
 
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v3
-        with:
-          buildkitd-flags: --root /tmp/buildkit
 
       - name: Log in to Docker Hub
         uses: docker/login-action@v3
@@ -49,7 +34,7 @@ jobs:
 
       - name: Build and push
         id: build
-        uses: docker/build-push-action@v6
+        uses: docker/build-push-action@v5
         with:
           context: .
           platforms: linux/amd64,linux/arm64
@@ -60,6 +45,5 @@ jobs:
           cache-from: type=registry,ref=cognee/cognee-mcp:buildcache
           cache-to: type=registry,ref=cognee/cognee-mcp:buildcache,mode=max
 
-
       - name: Image digest
         run: echo ${{ steps.build.outputs.digest }}

.github/workflows/e2e_tests.yml

@@ -147,7 +147,6 @@ jobs:
       - name: Run Deduplication Example
         env:
           ENV: 'dev'
-          ENABLE_BACKEND_ACCESS_CONTROL: 'false'
           LLM_API_KEY: ${{ secrets.OPENAI_API_KEY }} # Test needs OpenAI endpoint to handle multimedia
           OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
           EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
@@ -212,56 +211,6 @@ jobs:
           EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
         run: uv run python ./cognee/tests/test_parallel_databases.py
 
-  test-dataset-database-handler:
-    name: Test dataset database handlers in Cognee
-    runs-on: ubuntu-22.04
-    steps:
-      - name: Check out repository
-        uses: actions/checkout@v4
-
-      - name: Cognee Setup
-        uses: ./.github/actions/cognee_setup
-        with:
-          python-version: '3.11.x'
-
-      - name: Run dataset databases handler test
-        env:
-          ENV: 'dev'
-          LLM_MODEL: ${{ secrets.LLM_MODEL }}
-          LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
-          LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
-          LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
-          EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
-          EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
-          EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
-          EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
-        run: uv run python ./cognee/tests/test_dataset_database_handler.py
-
-  test-dataset-database-deletion:
-    name: Test dataset database deletion in Cognee
-    runs-on: ubuntu-22.04
-    steps:
-      - name: Check out repository
-        uses: actions/checkout@v4
-
-      - name: Cognee Setup
-        uses: ./.github/actions/cognee_setup
-        with:
-          python-version: '3.11.x'
-
-      - name: Run dataset databases deletion test
-        env:
-          ENV: 'dev'
-          LLM_MODEL: ${{ secrets.LLM_MODEL }}
-          LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
-          LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
-          LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
-          EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
-          EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
-          EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
-          EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
-        run: uv run python ./cognee/tests/test_dataset_delete.py
-
   test-permissions:
     name: Test permissions with different situations in Cognee
     runs-on: ubuntu-22.04
@@ -277,7 +226,7 @@ jobs:
       - name: Dependencies already installed
         run: echo "Dependencies already installed in setup"
 
-      - name: Run permissions test
+      - name: Run parallel databases test
         env:
           ENV: 'dev'
           LLM_MODEL: ${{ secrets.LLM_MODEL }}
@@ -290,31 +239,6 @@ jobs:
           EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
         run: uv run python ./cognee/tests/test_permissions.py
 
-  test-multi-tenancy:
-    name: Test multi tenancy with different situations in Cognee
-    runs-on: ubuntu-22.04
-    steps:
-      - name: Check out repository
-        uses: actions/checkout@v4
-
-      - name: Cognee Setup
-        uses: ./.github/actions/cognee_setup
-        with:
-          python-version: '3.11.x'
-
-      - name: Run multi tenancy test
-        env:
-          ENV: 'dev'
-          LLM_MODEL: ${{ secrets.LLM_MODEL }}
-          LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
-          LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
-          LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
-          EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
-          EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
-          EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
-          EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
-        run: uv run python ./cognee/tests/test_multi_tenancy.py
-
   test-graph-edges:
     name: Test graph edge ingestion
     runs-on: ubuntu-22.04
@@ -384,7 +308,7 @@ jobs:
           python-version: '3.11.x'
           extra-dependencies: "postgres redis"
 
-      - name: Run Concurrent subprocess access test (Kuzu/Lancedb/Postgres/Redis)
+      - name: Run Concurrent subprocess access test (Kuzu/Lancedb/Postgres)
         env:
           ENV: dev
           LLM_MODEL: ${{ secrets.LLM_MODEL }}
@@ -397,7 +321,6 @@ jobs:
           EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
           GRAPH_DATABASE_PROVIDER: 'kuzu'
           CACHING: true
-          CACHE_BACKEND: 'redis'
           SHARED_KUZU_LOCK: true
           DB_PROVIDER: 'postgres'
           DB_NAME: 'cognee_db'
@@ -463,37 +386,8 @@ jobs:
           EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
         run: uv run python ./cognee/tests/test_feedback_enrichment.py
 
-  test-edge-centered-payload:
-    name: Test Cognify - Edge Centered Payload
-    runs-on: ubuntu-22.04
-    steps:
-      - name: Check out repository
-        uses: actions/checkout@v4
-
-      - name: Cognee Setup
-        uses: ./.github/actions/cognee_setup
-        with:
-          python-version: '3.11.x'
-
-      - name: Dependencies already installed
-        run: echo "Dependencies already installed in setup"
-
-      - name: Run Edge Centered Payload Test
-        env:
-          ENV: 'dev'
-          TRIPLET_EMBEDDING: True
-          LLM_MODEL: ${{ secrets.LLM_MODEL }}
-          LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
-          LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
-          LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
-          EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
-          EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
-          EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
-          EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
-        run: uv run python ./cognee/tests/test_edge_centered_payload.py
-
-  run_conversation_sessions_test_redis:
-    name: Conversation sessions test (Redis)
+  run_conversation_sessions_test:
+    name: Conversation sessions test
     runs-on: ubuntu-latest
     defaults:
       run:
@@ -533,60 +427,7 @@ jobs:
           python-version: '3.11.x'
           extra-dependencies: "postgres redis"
 
-      - name: Run Conversation session tests (Redis)
-        env:
-          ENV: 'dev'
-          LLM_MODEL: ${{ secrets.LLM_MODEL }}
-          LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
-          LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
-          LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
-          EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
-          EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
-          EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
-          EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
-          GRAPH_DATABASE_PROVIDER: 'kuzu'
-          CACHING: true
-          CACHE_BACKEND: 'redis'
-          DB_PROVIDER: 'postgres'
-          DB_NAME: 'cognee_db'
-          DB_HOST: '127.0.0.1'
-          DB_PORT: 5432
-          DB_USERNAME: cognee
-          DB_PASSWORD: cognee
-        run: uv run python ./cognee/tests/test_conversation_history.py
-
-  run_conversation_sessions_test_fs:
-    name: Conversation sessions test (FS)
-    runs-on: ubuntu-latest
-    defaults:
-      run:
-        shell: bash
-    services:
-      postgres:
-        image: pgvector/pgvector:pg17
-        env:
-          POSTGRES_USER: cognee
-          POSTGRES_PASSWORD: cognee
-          POSTGRES_DB: cognee_db
-        options: >-
-          --health-cmd pg_isready
-          --health-interval 10s
-          --health-timeout 5s
-          --health-retries 5
-        ports:
-          - 5432:5432
-
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-
-      - name: Cognee Setup
-        uses: ./.github/actions/cognee_setup
-        with:
-          python-version: '3.11.x'
-          extra-dependencies: "postgres"
-
-      - name: Run Conversation session tests (FS)
+      - name: Run Conversation session tests
         env:
           ENV: dev
           LLM_MODEL: ${{ secrets.LLM_MODEL }}
@@ -599,7 +440,6 @@ jobs:
           EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
           GRAPH_DATABASE_PROVIDER: 'kuzu'
           CACHING: true
-          CACHE_BACKEND: 'fs'
           DB_PROVIDER: 'postgres'
           DB_NAME: 'cognee_db'
           DB_HOST: '127.0.0.1'
@@ -607,30 +447,3 @@ jobs:
           DB_USERNAME: cognee
           DB_PASSWORD: cognee
         run: uv run python ./cognee/tests/test_conversation_history.py
-
-  run-pipeline-cache-test:
-    name: Test Pipeline Caching
-    runs-on: ubuntu-22.04
-    steps:
-      - name: Check out
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-
-      - name: Cognee Setup
-        uses: ./.github/actions/cognee_setup
-        with:
-          python-version: '3.11.x'
-
-      - name: Run Pipeline Cache Test
-        env:
-          ENV: 'dev'
-          LLM_MODEL: ${{ secrets.LLM_MODEL }}
-          LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
-          LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
-          LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
-          EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
-          EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
-          EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
-          EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
-        run: uv run python ./cognee/tests/test_pipeline_cache.py

.github/workflows/examples_tests.yml

@@ -21,7 +21,6 @@ jobs:
 
       - name: Run Multimedia Example
         env:
-          ENV: 'dev'
           LLM_API_KEY: ${{ secrets.OPENAI_API_KEY }}
           OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
         run: uv run python ./examples/python/multimedia_example.py
@@ -41,7 +40,6 @@ jobs:
 
       - name: Run Evaluation Framework Example
         env:
-          ENV: 'dev'
           LLM_MODEL: ${{ secrets.LLM_MODEL }}
           LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
           LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
@@ -71,8 +69,6 @@ jobs:
 
       - name: Run Descriptive Graph Metrics Example
         env:
-          ENV: 'dev'
-          ENABLE_BACKEND_ACCESS_CONTROL: 'false'
           LLM_MODEL: ${{ secrets.LLM_MODEL }}
           LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
           LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
@@ -103,7 +99,6 @@ jobs:
 
       - name: Run Dynamic Steps Tests
         env:
-          ENV: 'dev'
           OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
           LLM_MODEL: ${{ secrets.LLM_MODEL }}
           LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
@@ -129,7 +124,6 @@ jobs:
 
       - name: Run Temporal Example
         env:
-          ENV: 'dev'
           OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
           LLM_MODEL: ${{ secrets.LLM_MODEL }}
           LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
@@ -155,7 +149,6 @@ jobs:
 
       - name: Run Ontology Demo Example
         env:
-          ENV: 'dev'
           OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
           LLM_MODEL: ${{ secrets.LLM_MODEL }}
           LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
@@ -181,7 +174,6 @@ jobs:
 
       - name: Run Agentic Reasoning Example
         env:
-          ENV: 'dev'
           OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
           LLM_MODEL: ${{ secrets.LLM_MODEL }}
           LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
@@ -207,7 +199,6 @@ jobs:
 
       - name: Run Memify Tests
         env:
-          ENV: 'dev'
           OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
           LLM_MODEL: ${{ secrets.LLM_MODEL }}
           LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
@@ -219,32 +210,6 @@ jobs:
           EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
         run: uv run python ./examples/python/memify_coding_agent_example.py
 
-  test-custom-pipeline:
-    name: Run Custom Pipeline Example
-    runs-on: ubuntu-22.04
-    steps:
-      - name: Check out repository
-        uses: actions/checkout@v4
-
-      - name: Cognee Setup
-        uses: ./.github/actions/cognee_setup
-        with:
-          python-version: '3.11.x'
-
-      - name: Run Custom Pipeline Example
-        env:
-          ENV: 'dev'
-          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
-          LLM_MODEL: ${{ secrets.LLM_MODEL }}
-          LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
-          LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
-          LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
-          EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
-          EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
-          EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
-          EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
-        run: uv run python ./examples/python/run_custom_pipeline_example.py
-
   test-permissions-example:
     name: Run Permissions Example
     runs-on: ubuntu-22.04
@@ -259,7 +224,6 @@ jobs:
 
       - name: Run Memify Tests
         env:
-          ENV: 'dev'
           OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
           LLM_MODEL: ${{ secrets.LLM_MODEL }}
           LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
@@ -285,7 +249,6 @@ jobs:
 
       - name: Run Docling Test
         env:
-          ENV: 'dev'
           OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
           LLM_MODEL: ${{ secrets.LLM_MODEL }}
           LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}

.github/workflows/graph_db_tests.yml

@@ -78,7 +78,6 @@ jobs:
       - name: Run default Neo4j
         env:
           ENV: 'dev'
-          ENABLE_BACKEND_ACCESS_CONTROL: 'false'
           LLM_MODEL: ${{ secrets.LLM_MODEL }}
           LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
           LLM_API_KEY: ${{ secrets.LLM_API_KEY }}

.github/workflows/load_tests.yml

@@ -1,70 +0,0 @@
-name: Load tests
-
-permissions:
-  contents: read
-
-on:
-  workflow_dispatch:
-  workflow_call:
-    secrets:
-      LLM_MODEL:
-        required: true
-      LLM_ENDPOINT:
-        required: true
-      LLM_API_KEY:
-        required: true
-      LLM_API_VERSION:
-        required: true
-      EMBEDDING_MODEL:
-        required: true
-      EMBEDDING_ENDPOINT:
-        required: true
-      EMBEDDING_API_KEY:
-        required: true
-      EMBEDDING_API_VERSION:
-        required: true
-      OPENAI_API_KEY:
-        required: true
-      AWS_ACCESS_KEY_ID:
-        required: true
-      AWS_SECRET_ACCESS_KEY:
-        required: true
-
-jobs:
-  test-load:
-    name: Test Load
-    runs-on: ubuntu-22.04
-    timeout-minutes: 60
-    steps:
-      - name: Check out repository
-        uses: actions/checkout@v4
-
-      - name: Cognee Setup
-        uses: ./.github/actions/cognee_setup
-        with:
-          python-version: '3.11.x'
-          extra-dependencies: "aws"
-
-      - name: Verify File Descriptor Limit
-        run: ulimit -n
-
-      - name: Run Load Test
-        env:
-          ENV: 'dev'
-          ENABLE_BACKEND_ACCESS_CONTROL: True
-          LLM_MODEL: ${{ secrets.LLM_MODEL }}
-          LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
-          LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
-          LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
-          EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
-          EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
-          EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
-          EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
-          STORAGE_BACKEND: s3
-          AWS_REGION: eu-west-1
-          AWS_ENDPOINT_URL: https://s3-eu-west-1.amazonaws.com
-          AWS_ACCESS_KEY_ID: ${{ secrets.AWS_S3_DEV_USER_KEY_ID }}
-          AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_S3_DEV_USER_SECRET_KEY }}
-        run: uv run python ./cognee/tests/test_load.py
-  
-    

.github/workflows/pre_test.yml

@@ -1,22 +0,0 @@
-on:
-  workflow_call:
-
-permissions:
-  contents: read
-jobs:
-  check-uv-lock:
-    name: Validate uv lockfile and project metadata
-    runs-on: ubuntu-22.04
-    steps:
-      - name: Check out repository
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-
-      - name: Install uv
-        uses: astral-sh/setup-uv@v4
-        with:
-          enable-cache: true
-      
-      - name: Validate uv lockfile and project metadata
-        run: uv lock --check || { echo "'uv lock --check' failed."; echo "Run 'uv lock' and push your changes."; exit 1; }

.github/workflows/release.yml

@@ -1,138 +0,0 @@
-name: release.yml
-on:
-  workflow_dispatch:
-    inputs:
-      flavour:
-        required: true
-        default: dev
-        type: choice
-        options:
-          - dev
-          - main
-        description: Dev or Main release
-
-jobs:
-  release-github:
-    name: Create GitHub Release from ${{ inputs.flavour }}
-    outputs:
-      tag: ${{ steps.create_tag.outputs.tag }}
-      version: ${{ steps.create_tag.outputs.version }}
-    permissions:
-      contents: write
-    runs-on: ubuntu-latest
-
-    steps:
-      - name: Check out ${{ inputs.flavour }}
-        uses: actions/checkout@v4
-        with:
-          ref: ${{ inputs.flavour }}
-      - name: Install uv
-        uses: astral-sh/setup-uv@v7
-
-      - name: Create and push git tag
-        id: create_tag
-        run: |
-          VERSION="$(uv version --short)"
-          TAG="v${VERSION}"
-
-          echo "Tag to create: ${TAG}"
-
-          git config user.name "github-actions[bot]"
-          git config user.email "41898282+github-actions[bot]@users.noreply.github.com"
-
-          echo "tag=${TAG}" >> "$GITHUB_OUTPUT"
-          echo "version=${VERSION}" >> "$GITHUB_OUTPUT"
-          
-          git tag "${TAG}"
-          git push origin "${TAG}"
-          
-
-      - name: Create GitHub Release
-        uses: softprops/action-gh-release@v2
-        with:
-          tag_name: ${{ steps.create_tag.outputs.tag }}
-          generate_release_notes: true
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-  
-  release-pypi-package:    
-    needs: release-github
-    name: Release PyPI Package from ${{ inputs.flavour }}
-    permissions:
-      contents: read
-    runs-on: ubuntu-latest
-
-    steps:
-      - name: Check out ${{ inputs.flavour }}
-        uses: actions/checkout@v4
-        with:
-          ref: ${{ inputs.flavour }}
-      
-      - name: Install uv
-        uses: astral-sh/setup-uv@v7
-      
-      - name: Install Python
-        run: uv python install
-      
-      - name: Install dependencies
-        run: uv sync --locked --all-extras
-      
-      - name: Build distributions
-        run: uv build
-      
-      - name: Publish ${{ inputs.flavour }} release to PyPI
-        env:
-          UV_PUBLISH_TOKEN: ${{ secrets.PYPI_TOKEN }}
-        run: uv publish
-  
-  release-docker-image:    
-    needs: release-github
-    name: Release Docker Image from ${{ inputs.flavour }}
-    permissions:
-      contents: read
-    runs-on: ubuntu-latest
-
-    steps:
-      - name: Check out ${{ inputs.flavour }}
-        uses: actions/checkout@v4
-        with:
-          ref: ${{ inputs.flavour }}
-
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-
-      - name: Log in to Docker Hub
-        uses: docker/login-action@v3
-        with:
-          username: ${{ secrets.DOCKER_USERNAME }}
-          password: ${{ secrets.DOCKER_PASSWORD }}
-
-      - name: Build and push Dev Docker Image
-        if: ${{ inputs.flavour == 'dev' }}
-        uses: docker/build-push-action@v5
-        with:
-          context: .
-          platforms: linux/amd64,linux/arm64
-          push: true
-          tags: cognee/cognee:${{ needs.release-github.outputs.version }}
-          labels: |
-            version=${{ needs.release-github.outputs.version }}
-            flavour=${{ inputs.flavour }}
-          cache-from: type=registry,ref=cognee/cognee:buildcache
-          cache-to: type=registry,ref=cognee/cognee:buildcache,mode=max
-
-      - name: Build and push Main Docker Image
-        if: ${{ inputs.flavour == 'main' }}
-        uses: docker/build-push-action@v5
-        with:
-          context: .
-          platforms: linux/amd64,linux/arm64
-          push: true
-          tags: | 
-            cognee/cognee:${{ needs.release-github.outputs.version }}
-            cognee/cognee:latest
-          labels: |
-            version=${{ needs.release-github.outputs.version }}
-            flavour=${{ inputs.flavour }}
-          cache-from: type=registry,ref=cognee/cognee:buildcache
-          cache-to: type=registry,ref=cognee/cognee:buildcache,mode=max

.github/workflows/release_test.yml

@@ -1,17 +0,0 @@
-# Long-running, heavy and resource-consuming tests for release validation
-name: Release Test Workflow
-
-permissions:
-  contents: read
-
-on:
-  workflow_dispatch:
-  pull_request:
-    branches:
-      - main
-
-jobs:
-  load-tests:
-    name: Load Tests
-    uses: ./.github/workflows/load_tests.yml
-    secrets: inherit

.github/workflows/search_db_tests.yml

@@ -84,7 +84,6 @@ jobs:
           GRAPH_DATABASE_PROVIDER: 'neo4j'
           VECTOR_DB_PROVIDER: 'lancedb'
           DB_PROVIDER: 'sqlite'
-          ENABLE_BACKEND_ACCESS_CONTROL: 'false'
           GRAPH_DATABASE_URL: ${{ steps.neo4j.outputs.neo4j-url }}
           GRAPH_DATABASE_USERNAME: ${{ steps.neo4j.outputs.neo4j-username }}
           GRAPH_DATABASE_PASSWORD: ${{ steps.neo4j.outputs.neo4j-password }}
@@ -136,7 +135,6 @@ jobs:
             EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
             GRAPH_DATABASE_PROVIDER: 'kuzu'
             VECTOR_DB_PROVIDER: 'pgvector'
-            ENABLE_BACKEND_ACCESS_CONTROL: 'false'
             DB_PROVIDER: 'postgres'
             DB_NAME: 'cognee_db'
             DB_HOST: '127.0.0.1'
@@ -199,7 +197,6 @@ jobs:
           GRAPH_DATABASE_URL: ${{ steps.neo4j.outputs.neo4j-url }}
           GRAPH_DATABASE_USERNAME: ${{ steps.neo4j.outputs.neo4j-username }}
           GRAPH_DATABASE_PASSWORD: ${{ steps.neo4j.outputs.neo4j-password }}
-          ENABLE_BACKEND_ACCESS_CONTROL: 'false'
           DB_NAME: cognee_db
           DB_HOST: 127.0.0.1
           DB_PORT: 5432

.github/workflows/temporal_graph_tests.yml

@@ -72,7 +72,6 @@ jobs:
       - name: Run Temporal Graph with Neo4j (lancedb + sqlite)
         env:
           ENV: 'dev'
-          ENABLE_BACKEND_ACCESS_CONTROL: 'false'
           LLM_MODEL: ${{ secrets.OPENAI_MODEL }}
           LLM_ENDPOINT: ${{ secrets.OPENAI_ENDPOINT }}
           LLM_API_KEY: ${{ secrets.OPENAI_API_KEY }}
@@ -124,7 +123,6 @@ jobs:
         - name: Run Temporal Graph with Kuzu (postgres + pgvector)
           env:
             ENV: dev
-            ENABLE_BACKEND_ACCESS_CONTROL: 'false'
             LLM_MODEL: ${{ secrets.OPENAI_MODEL }}
             LLM_ENDPOINT: ${{ secrets.OPENAI_ENDPOINT }}
             LLM_API_KEY: ${{ secrets.OPENAI_API_KEY }}
@@ -191,7 +189,6 @@ jobs:
       - name: Run Temporal Graph with Neo4j (postgres + pgvector)
         env:
           ENV: dev
-          ENABLE_BACKEND_ACCESS_CONTROL: 'false'
           LLM_MODEL: ${{ secrets.OPENAI_MODEL }}
           LLM_ENDPOINT: ${{ secrets.OPENAI_ENDPOINT }}
           LLM_API_KEY: ${{ secrets.OPENAI_API_KEY }}

.github/workflows/test_different_operating_systems.yml

@@ -10,10 +10,6 @@ on:
         required: false
         type: string
         default: '["3.10.x", "3.12.x", "3.13.x"]'
-      os: 
-        required: false
-        type: string
-        default: '["ubuntu-22.04", "macos-15", "windows-latest"]'
     secrets:
       LLM_PROVIDER:
         required: true
@@ -44,11 +40,10 @@ jobs:
   run-unit-tests:
     name: Unit tests ${{ matrix.python-version }} on ${{ matrix.os }}
     runs-on: ${{ matrix.os }}
-    timeout-minutes: 60
     strategy:
       matrix:
         python-version: ${{ fromJSON(inputs.python-versions) }}
-        os: ${{ fromJSON(inputs.os) }}
+        os: [ubuntu-22.04, macos-15, windows-latest]
       fail-fast: false
     steps:
       - name: Check out
@@ -81,11 +76,10 @@ jobs:
   run-integration-tests:
     name: Integration tests ${{ matrix.python-version }} on ${{ matrix.os }}
     runs-on: ${{ matrix.os }}
-    timeout-minutes: 60
     strategy:
       matrix:
         python-version: ${{ fromJSON(inputs.python-versions) }}
-        os: ${{ fromJSON(inputs.os) }}
+        os: [ ubuntu-22.04, macos-15, windows-latest ]
       fail-fast: false
     steps:
       - name: Check out
@@ -118,11 +112,10 @@ jobs:
   run-library-test:
     name: Library test ${{ matrix.python-version }} on ${{ matrix.os }}
     runs-on: ${{ matrix.os }}
-    timeout-minutes: 60
     strategy:
       matrix:
         python-version: ${{ fromJSON(inputs.python-versions) }}
-        os: ${{ fromJSON(inputs.os) }}
+        os: [ ubuntu-22.04, macos-15, windows-latest ]
       fail-fast: false
     steps:
       - name: Check out
@@ -155,11 +148,10 @@ jobs:
   run-build-test:
     name: Build test ${{ matrix.python-version }} on ${{ matrix.os }}
     runs-on: ${{ matrix.os }}
-    timeout-minutes: 60
     strategy:
       matrix:
         python-version: ${{ fromJSON(inputs.python-versions) }}
-        os: ${{ fromJSON(inputs.os) }}
+        os: [ ubuntu-22.04, macos-15, windows-latest ]
       fail-fast: false
     steps:
       - name: Check out
@@ -185,11 +177,10 @@ jobs:
   run-soft-deletion-test:
     name: Soft Delete test ${{ matrix.python-version }} on ${{ matrix.os }}
     runs-on: ${{ matrix.os }}
-    timeout-minutes: 60
     strategy:
       matrix:
         python-version: ${{ fromJSON(inputs.python-versions) }}
-        os: ${{ fromJSON(inputs.os) }}
+        os: [ ubuntu-22.04, macos-15, windows-latest ]
       fail-fast: false
     steps:
       - name: Check out
@@ -223,11 +214,10 @@ jobs:
   run-hard-deletion-test:
     name: Hard Delete test ${{ matrix.python-version }} on ${{ matrix.os }}
     runs-on: ${{ matrix.os }}
-    timeout-minutes: 60
     strategy:
       matrix:
         python-version: ${{ fromJSON(inputs.python-versions) }}
-        os: ${{ fromJSON(inputs.os) }}
+        os: [ ubuntu-22.04, macos-15, windows-latest ]
       fail-fast: false
     steps:
       - name: Check out

.github/workflows/test_llms.yml

@@ -84,93 +84,3 @@ jobs:
           EMBEDDING_DIMENSIONS: "3072"
           EMBEDDING_MAX_TOKENS: "8191"
         run: uv run python ./examples/python/simple_example.py
-
-  test-bedrock-api-key:
-    name: Run Bedrock API Key Test
-    runs-on: ubuntu-22.04
-    steps:
-      - name: Check out repository
-        uses: actions/checkout@v4
-
-      - name: Cognee Setup
-        uses: ./.github/actions/cognee_setup
-        with:
-          python-version: '3.11.x'
-          extra-dependencies: "aws"
-
-      - name: Run Bedrock API Key Simple Example
-        env:
-          LLM_PROVIDER: "bedrock"
-          LLM_API_KEY: ${{ secrets.BEDROCK_API_KEY }}
-          LLM_MODEL: "eu.anthropic.claude-sonnet-4-5-20250929-v1:0"
-          LLM_MAX_TOKENS: "16384"
-          AWS_REGION_NAME: "eu-west-1"
-          EMBEDDING_PROVIDER: "bedrock"
-          EMBEDDING_API_KEY: ${{ secrets.BEDROCK_API_KEY }}
-          EMBEDDING_MODEL: "amazon.titan-embed-text-v2:0"
-          EMBEDDING_DIMENSIONS: "1024"
-          EMBEDDING_MAX_TOKENS: "8191"
-        run: uv run python ./examples/python/simple_example.py
-
-  test-bedrock-aws-credentials:
-    name: Run Bedrock AWS Credentials Test
-    runs-on: ubuntu-22.04
-    steps:
-      - name: Check out repository
-        uses: actions/checkout@v4
-
-      - name: Cognee Setup
-        uses: ./.github/actions/cognee_setup
-        with:
-          python-version: '3.11.x'
-          extra-dependencies: "aws"
-
-      - name: Run Bedrock AWS Credentials Simple Example
-        env:
-          LLM_PROVIDER: "bedrock"
-          LLM_MODEL: "eu.anthropic.claude-sonnet-4-5-20250929-v1:0"
-          LLM_MAX_TOKENS: "16384"
-          AWS_REGION_NAME: "eu-west-1"
-          AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
-          AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
-          EMBEDDING_PROVIDER: "bedrock"
-          EMBEDDING_API_KEY: ${{ secrets.BEDROCK_API_KEY }}
-          EMBEDDING_MODEL: "amazon.titan-embed-text-v2:0"
-          EMBEDDING_DIMENSIONS: "1024"
-          EMBEDDING_MAX_TOKENS: "8191"
-        run: uv run python ./examples/python/simple_example.py
-
-  test-bedrock-aws-profile:
-    name: Run Bedrock AWS Profile Test
-    runs-on: ubuntu-22.04
-    steps:
-      - name: Check out repository
-        uses: actions/checkout@v4
-
-      - name: Cognee Setup
-        uses: ./.github/actions/cognee_setup
-        with:
-          python-version: '3.11.x'
-          extra-dependencies: "aws"
-
-      - name: Configure AWS Profile
-        run: |
-          mkdir -p ~/.aws
-          cat > ~/.aws/credentials << EOF
-          [bedrock-test]
-          aws_access_key_id = ${{ secrets.AWS_ACCESS_KEY_ID }}
-          aws_secret_access_key = ${{ secrets.AWS_SECRET_ACCESS_KEY }}
-          EOF
-
-      - name: Run Bedrock AWS Profile Simple Example
-        env:
-          LLM_PROVIDER: "bedrock"
-          LLM_MODEL: "eu.anthropic.claude-sonnet-4-5-20250929-v1:0"
-          LLM_MAX_TOKENS: "16384"
-          AWS_PROFILE_NAME: "bedrock-test"
-          AWS_REGION_NAME: "eu-west-1"
-          EMBEDDING_PROVIDER: "bedrock"
-          EMBEDDING_MODEL: "amazon.titan-embed-text-v2:0"
-          EMBEDDING_DIMENSIONS: "1024"
-          EMBEDDING_MAX_TOKENS: "8191"
-        run: uv run python ./examples/python/simple_example.py

.github/workflows/test_ollama.yml

@@ -7,8 +7,13 @@ jobs:
 
   run_ollama_test:
 
-    # needs 32 Gb RAM for phi4 in a container
-    runs-on: buildjet-8vcpu-ubuntu-2204
+    # needs 16 Gb RAM for phi4
+    runs-on: buildjet-4vcpu-ubuntu-2204
+#    services:
+#      ollama:
+#        image: ollama/ollama
+#        ports:
+#          - 11434:11434
 
     steps:
       - name: Checkout repository
@@ -23,6 +28,14 @@ jobs:
         run: |
           uv add torch
 
+#      - name: Install ollama
+#        run: curl -fsSL https://ollama.com/install.sh | sh
+#      - name: Run ollama
+#        run: |
+#          ollama serve --openai &
+#          ollama pull llama3.2 &
+#          ollama pull avr/sfr-embedding-mistral:latest
+
       - name: Start Ollama container
         run: |
           docker run -d --name ollama -p 11434:11434 ollama/ollama
@@ -62,7 +75,7 @@ jobs:
                 { "role": "user", "content": "Whatever I say, answer with Yes." }
               ]
             }'
-          curl -X POST http://127.0.0.1:11434/api/embed \
+          curl -X POST http://127.0.0.1:11434/v1/embeddings \
             -H "Content-Type: application/json" \
              -d '{
               "model": "avr/sfr-embedding-mistral:latest",
@@ -85,7 +98,7 @@ jobs:
           LLM_MODEL: "phi4"
           EMBEDDING_PROVIDER: "ollama"
           EMBEDDING_MODEL: "avr/sfr-embedding-mistral:latest"
-          EMBEDDING_ENDPOINT: "http://localhost:11434/api/embed"
+          EMBEDDING_ENDPOINT: "http://localhost:11434/api/embeddings"
           EMBEDDING_DIMENSIONS: "4096"
           HUGGINGFACE_TOKENIZER: "Salesforce/SFR-Embedding-Mistral"
         run: uv run python ./examples/python/simple_example.py

.github/workflows/test_suites.yml

@@ -1,6 +1,4 @@
 name: Test Suites
-permissions:
-  contents: read
 
 on:
   push:
@@ -18,21 +16,15 @@ env:
   RUNTIME__LOG_LEVEL: ERROR
   ENV: 'dev'
 
-jobs: 
-  pre-test:
-    name: basic checks
-    uses: ./.github/workflows/pre_test.yml
-    
+jobs:
   basic-tests:
     name: Basic Tests
     uses: ./.github/workflows/basic_tests.yml
-    needs: [ pre-test ]
     secrets: inherit
 
   e2e-tests:
     name: End-to-End Tests
     uses: ./.github/workflows/e2e_tests.yml
-    needs: [ pre-test ]
     secrets: inherit
 
   distributed-tests:
@@ -88,22 +80,12 @@ jobs:
     uses: ./.github/workflows/notebooks_tests.yml
     secrets: inherit
 
-  different-os-tests-basic:
-    name: OS and Python Tests Ubuntu
+  different-operating-systems-tests:
+    name: Operating System and Python Tests
     needs: [basic-tests, e2e-tests]
     uses: ./.github/workflows/test_different_operating_systems.yml
     with:
       python-versions: '["3.10.x", "3.11.x", "3.12.x", "3.13.x"]'
-      os: '["ubuntu-22.04"]'
-    secrets: inherit
-
-  different-os-tests-extended:
-    name: OS and Python Tests Extended
-    needs: [basic-tests, e2e-tests]
-    uses: ./.github/workflows/test_different_operating_systems.yml
-    with:
-      python-versions: '["3.13.x"]'
-      os: '["macos-15", "windows-latest"]'
     secrets: inherit
 
   # Matrix-based vector database tests
@@ -153,8 +135,7 @@ jobs:
       e2e-tests,
       graph-db-tests,
       notebook-tests,
-      different-os-tests-basic,
-      different-os-tests-extended,
+      different-operating-systems-tests,
       vector-db-tests,
       example-tests,
       llm-tests,
@@ -174,8 +155,7 @@ jobs:
       cli-tests,
       graph-db-tests,
       notebook-tests,
-      different-os-tests-basic,
-      different-os-tests-extended,
+      different-operating-systems-tests,
       vector-db-tests,
       example-tests,
       db-examples-tests,
@@ -196,8 +176,7 @@ jobs:
                 "${{ needs.cli-tests.result }}" == "success" &&
                 "${{ needs.graph-db-tests.result }}" == "success" &&
                 "${{ needs.notebook-tests.result }}" == "success" &&
-                "${{ needs.different-os-tests-basic.result }}" == "success" &&
-                "${{ needs.different-os-tests-extended.result }}" == "success" &&
+                "${{ needs.different-operating-systems-tests.result }}" == "success" &&
                 "${{ needs.vector-db-tests.result }}" == "success" &&
                 "${{ needs.example-tests.result }}" == "success" &&
                 "${{ needs.db-examples-tests.result }}" == "success" &&

.github/workflows/vector_db_tests.yml

@@ -92,7 +92,6 @@ jobs:
       - name: Run PGVector Tests
         env:
           ENV: 'dev'
-          ENABLE_BACKEND_ACCESS_CONTROL: 'false'
           LLM_MODEL: ${{ secrets.LLM_MODEL }}
           LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
           LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
@@ -128,4 +127,4 @@ jobs:
           EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
           EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
           EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
-        run: uv run python ./cognee/tests/test_lancedb.py
+        run: uv run python ./cognee/tests/test_lancedb.py

.github/workflows/weighted_edges_tests.yml

@@ -2,7 +2,7 @@ name: Weighted Edges Tests
 
 on:
   push:
-    branches: [ main, dev, weighted_edges ]
+    branches: [ main, weighted_edges ]
     paths:
       - 'cognee/modules/graph/utils/get_graph_from_model.py'
       - 'cognee/infrastructure/engine/models/Edge.py'
@@ -10,7 +10,7 @@ on:
       - 'examples/python/weighted_edges_example.py'
       - '.github/workflows/weighted_edges_tests.yml'
   pull_request:
-    branches: [ main, dev ]
+    branches: [ main ]
     paths:
       - 'cognee/modules/graph/utils/get_graph_from_model.py'
       - 'cognee/infrastructure/engine/models/Edge.py'
@@ -32,7 +32,7 @@ jobs:
     env:
       LLM_PROVIDER: openai
       LLM_MODEL: gpt-5-mini
-      LLM_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+      LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
 
     steps:
       - name: Check out repository
@@ -67,13 +67,14 @@ jobs:
     env:
       LLM_PROVIDER: openai
       LLM_MODEL: gpt-5-mini
-      LLM_ENDPOINT: https://api.openai.com/v1
-      LLM_API_KEY: ${{ secrets.OPENAI_API_KEY  }}
+      LLM_ENDPOINT: https://api.openai.com/v1/
+      LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
       LLM_API_VERSION: "2024-02-01"
-      EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
-      EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
-      EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
-      EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
+      EMBEDDING_PROVIDER: openai
+      EMBEDDING_MODEL: text-embedding-3-small
+      EMBEDDING_ENDPOINT: https://api.openai.com/v1/
+      EMBEDDING_API_KEY: ${{ secrets.LLM_API_KEY }}
+      EMBEDDING_API_VERSION: "2024-02-01"
     steps:
       - name: Check out repository
         uses: actions/checkout@v4
@@ -94,7 +95,6 @@ jobs:
 
       - name: Run Weighted Edges Tests
         env:
-          ENABLE_BACKEND_ACCESS_CONTROL: 'false'
           GRAPH_DATABASE_PROVIDER: ${{ matrix.graph_db_provider }}
           GRAPH_DATABASE_URL: ${{ matrix.graph_db_provider == 'neo4j' && steps.neo4j.outputs.neo4j-url || '' }}
           GRAPH_DATABASE_USERNAME: ${{ matrix.graph_db_provider == 'neo4j' && steps.neo4j.outputs.neo4j-username || '' }}
@@ -108,14 +108,14 @@ jobs:
     env:
       LLM_PROVIDER: openai
       LLM_MODEL: gpt-5-mini
-      LLM_ENDPOINT: https://api.openai.com/v1
-      LLM_API_KEY: ${{ secrets.OPENAI_API_KEY  }}
+      LLM_ENDPOINT: https://api.openai.com/v1/
+      LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
       LLM_API_VERSION: "2024-02-01"
-      EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
-      EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
-      EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
-      EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
-
+      EMBEDDING_PROVIDER: openai
+      EMBEDDING_MODEL: text-embedding-3-small
+      EMBEDDING_ENDPOINT: https://api.openai.com/v1/
+      EMBEDDING_API_KEY: ${{ secrets.LLM_API_KEY }}
+      EMBEDDING_API_VERSION: "2024-02-01"
     steps:
       - name: Check out repository
         uses: actions/checkout@v4
@@ -166,3 +166,5 @@ jobs:
         uses: astral-sh/ruff-action@v2
         with:
           args: "format --check cognee/modules/graph/utils/get_graph_from_model.py cognee/tests/unit/interfaces/graph/test_weighted_edges.py examples/python/weighted_edges_example.py"
+
+ 

.mergify.yml

@@ -1,9 +0,0 @@
-pull_request_rules:
-  - name: Backport to main when backport_main label is set
-    conditions:
-      - label=backport_main
-      - base=dev
-    actions:
-      backport:
-        branches:
-          - main

AGENTS.md

@@ -1,132 +0,0 @@
-## Repository Guidelines
-
-This document summarizes how to work with the cognee repository: how it’s organized, how to build, test, lint, and contribute. It mirrors our actual tooling and CI while providing quick commands for local development.
-
-## Project Structure & Module Organization
-
-- `cognee/`: Core Python library and API.
-  - `api/`: FastAPI application and versioned routers (add, cognify, memify, search, delete, users, datasets, responses, visualize, settings, sync, update, checks).
-  - `cli/`: CLI entry points and subcommands invoked via `cognee` / `cognee-cli`.
-  - `infrastructure/`: Databases, LLM providers, embeddings, loaders, and storage adapters.
-  - `modules/`: Domain logic (graph, retrieval, ontology, users, processing, observability, etc.).
-  - `tasks/`: Reusable tasks (e.g., code graph, web scraping, storage). Extend with new tasks here.
-  - `eval_framework/`: Evaluation utilities and adapters.
-  - `shared/`: Cross-cutting helpers (logging, settings, utils).
-  - `tests/`: Unit, integration, CLI, and end-to-end tests organized by feature.
-  - `__main__.py`: Entrypoint to route to CLI.
-- `cognee-mcp/`: Model Context Protocol server exposing cognee as MCP tools (SSE/HTTP/stdio). Contains its own README and Dockerfile.
-- `cognee-frontend/`: Next.js UI for local development and demos.
-- `distributed/`: Utilities for distributed execution (Modal, workers, queues).
-- `examples/`: Example scripts demonstrating the public APIs and features (graph, code graph, multimodal, permissions, etc.).
-- `notebooks/`: Jupyter notebooks for demos and tutorials.
-- `alembic/`: Database migrations for relational backends.
-
-Notes:
-- Co-locate feature-specific helpers under their respective package (`modules/`, `infrastructure/`, or `tasks/`).
-- Extend the system by adding new tasks, loaders, or retrievers rather than modifying core pipeline mechanisms.
-
-## Build, Test, and Development Commands
-
-Python (root) – requires Python >= 3.10 and < 3.14. We recommend `uv` for speed and reproducibility.
-
-- Create/refresh env and install dev deps:
-```bash
-uv sync --dev --all-extras --reinstall
-```
-
-- Run the CLI (examples):
-```bash
-uv run cognee-cli add "Cognee turns documents into AI memory."
-uv run cognee-cli cognify
-uv run cognee-cli search "What does cognee do?"
-uv run cognee-cli -ui   # Launches UI, backend API, and MCP server together
-```
-
-- Start the FastAPI server directly:
-```bash
-uv run python -m cognee.api.client
-```
-
-- Run tests (CI mirrors these commands):
-```bash
-uv run pytest cognee/tests/unit/ -v
-uv run pytest cognee/tests/integration/ -v
-```
-
-- Lint and format (ruff):
-```bash
-uv run ruff check .
-uv run ruff format .
-```
-
-- Optional static type checks (mypy):
-```bash
-uv run mypy cognee/
-```
-
-MCP Server (`cognee-mcp/`):
-
-- Install and run locally:
-```bash
-cd cognee-mcp
-uv sync --dev --all-extras --reinstall
-uv run python src/server.py               # stdio (default)
-uv run python src/server.py --transport sse
-uv run python src/server.py --transport http --host 127.0.0.1 --port 8000 --path /mcp
-```
-
-- API Mode (connect to a running Cognee API):
-```bash
-uv run python src/server.py --transport sse --api-url http://localhost:8000 --api-token YOUR_TOKEN
-```
-
-- Docker quickstart (examples): see `cognee-mcp/README.md` for full details
-```bash
-docker run -e TRANSPORT_MODE=http --env-file ./.env -p 8000:8000 --rm -it cognee/cognee-mcp:main
-```
-
-Frontend (`cognee-frontend/`):
-```bash
-cd cognee-frontend
-npm install
-npm run dev     # Next.js dev server
-npm run lint    # ESLint
-npm run build && npm start
-```
-
-## Coding Style & Naming Conventions
-
-Python:
-- 4-space indentation, modules and functions in `snake_case`, classes in `PascalCase`.
-- Public APIs should be type-annotated where practical.
-- Use `ruff format` before committing; `ruff check` enforces import hygiene and style (line-length 100 configured in `pyproject.toml`).
-- Prefer explicit, structured error handling. Use shared logging utilities in `cognee.shared.logging_utils`.
-
-MCP server and Frontend:
-- Follow the local `README.md` and ESLint/TypeScript configuration in `cognee-frontend/`.
-
-## Testing Guidelines
-
-- Place Python tests under `cognee/tests/`.
-  - Unit tests: `cognee/tests/unit/`
-  - Integration tests: `cognee/tests/integration/`
-  - CLI tests: `cognee/tests/cli_tests/`
-- Name test files `test_*.py`. Use `pytest.mark.asyncio` for async tests.
-- Avoid external state; rely on test fixtures and the CI-provided env vars when LLM/embedding providers are required. See CI workflows under `.github/workflows/` for expected environment variables.
-- When adding public APIs, provide/update targeted examples under `examples/python/`.
-
-## Commit & Pull Request Guidelines
-
-- Use clear, imperative subjects (≤ 72 chars) and conventional commit styling in PR titles. Our CI validates semantic PR titles (see `.github/workflows/pr_lint`). Examples:
-  - `feat(graph): add temporal edge weighting`
-  - `fix(api): handle missing auth cookie`
-  - `docs: update installation instructions`
-- Reference related issues/discussions in the PR body and provide brief context.
-- PRs should describe scope, list local test commands run, and mention any impacts on MCP server or UI if applicable.
-- Sign commits and affirm the DCO (see `CONTRIBUTING.md`).
-
-## CI Mirrors Local Commands
-
-Our GitHub Actions run the same ruff checks and pytest suites shown above (`.github/workflows/basic_tests.yml` and related workflows). Use the commands in this document locally to minimize CI surprises.
-
-

CONTRIBUTING.md

@@ -71,7 +71,7 @@ git clone https://github.com/<your-github-username>/cognee.git
 cd cognee
 ```
 In case you are working on Vector and Graph Adapters
-1. Fork the [**cognee-community**](https://github.com/topoteretes/cognee-community) repository
+1. Fork the [**cognee**](https://github.com/topoteretes/cognee-community) repository
 2. Clone your fork:
 ```shell
 git clone https://github.com/<your-github-username>/cognee-community.git
@@ -97,21 +97,6 @@ git checkout -b feature/your-feature-name
 python cognee/cognee/tests/test_library.py
 ```
 
-### Running Simple Example
-
-Change .env.example into .env and provide your OPENAI_API_KEY as LLM_API_KEY
-
-Make sure to run ```shell uv sync ``` in the root cloned folder or set up a virtual environment to run cognee
-
-```shell
-python cognee/cognee/examples/python/simple_example.py
-```
-or 
-
-```shell
-uv run python cognee/cognee/examples/python/simple_example.py
-```
-
 ## 4. 📤 Submitting Changes
 
 1. Install ruff on your system

README.md

@@ -5,27 +5,27 @@
 
   <br />
 
-  Cognee - Accurate and Persistent AI Memory
+  cognee - Memory for AI Agents in 6 lines of code
 
   <p align="center">
   <a href="https://www.youtube.com/watch?v=1bezuvLwJmw&t=2s">Demo</a>
   .
-  <a href="https://docs.cognee.ai/">Docs</a>
-  .
-  <a href="https://cognee.ai">Learn More</a>
+  <a href="https://cognee.ai">Learn more</a>
   ·
   <a href="https://discord.gg/NQPKmU5CCg">Join Discord</a>
   ·
   <a href="https://www.reddit.com/r/AIMemory/">Join r/AIMemory</a>
   .
-  <a href="https://github.com/topoteretes/cognee-community">Community Plugins & Add-ons</a>
+  <a href="https://docs.cognee.ai/">Docs</a>
+  .
+  <a href="https://github.com/topoteretes/cognee-community">cognee community repo</a>
   </p>
 
 
   [![GitHub forks](https://img.shields.io/github/forks/topoteretes/cognee.svg?style=social&label=Fork&maxAge=2592000)](https://GitHub.com/topoteretes/cognee/network/)
   [![GitHub stars](https://img.shields.io/github/stars/topoteretes/cognee.svg?style=social&label=Star&maxAge=2592000)](https://GitHub.com/topoteretes/cognee/stargazers/)
   [![GitHub commits](https://badgen.net/github/commits/topoteretes/cognee)](https://GitHub.com/topoteretes/cognee/commit/)
-  [![GitHub tag](https://badgen.net/github/tag/topoteretes/cognee)](https://github.com/topoteretes/cognee/tags/)
+  [![Github tag](https://badgen.net/github/tag/topoteretes/cognee)](https://github.com/topoteretes/cognee/tags/)
   [![Downloads](https://static.pepy.tech/badge/cognee)](https://pepy.tech/project/cognee)
   [![License](https://img.shields.io/github/license/topoteretes/cognee?colorA=00C586&colorB=000000)](https://github.com/topoteretes/cognee/blob/main/LICENSE)
   [![Contributors](https://img.shields.io/github/contributors/topoteretes/cognee?colorA=00C586&colorB=000000)](https://github.com/topoteretes/cognee/graphs/contributors)
@@ -41,7 +41,11 @@
   </a>
 </p>
 
-Use your data to build personalized and dynamic memory for AI Agents. Cognee lets you replace RAG with scalable and modular ECL (Extract, Cognify, Load) pipelines.
+
+
+
+
+Build dynamic memory for Agents and replace RAG using scalable, modular ECL (Extract, Cognify, Load) pipelines.
 
   <p align="center">
   🌐 Available Languages
@@ -49,7 +53,7 @@ Use your data to build personalized and dynamic memory for AI Agents. Cognee let
   <!-- Keep these links. Translations will automatically update with the README. -->
   <a href="https://www.readme-i18n.com/topoteretes/cognee?lang=de">Deutsch</a> |
   <a href="https://www.readme-i18n.com/topoteretes/cognee?lang=es">Español</a> |
-  <a href="https://www.readme-i18n.com/topoteretes/cognee?lang=fr">Français</a> |
+  <a href="https://www.readme-i18n.com/topoteretes/cognee?lang=fr">français</a> |
   <a href="https://www.readme-i18n.com/topoteretes/cognee?lang=ja">日本語</a> |
   <a href="https://www.readme-i18n.com/topoteretes/cognee?lang=ko">한국어</a> |
   <a href="https://www.readme-i18n.com/topoteretes/cognee?lang=pt">Português</a> |
@@ -63,62 +67,73 @@ Use your data to build personalized and dynamic memory for AI Agents. Cognee let
 </div>
 </div>
 
-## About Cognee
-
-Cognee is an open-source tool and platform that transforms your raw data into persistent and dynamic AI memory for Agents. It combines vector search with graph databases to make your documents both searchable by meaning and connected by relationships. 
-Cognee offers default memory creation and search which we describe bellow. But with Cognee you can build your own!
 
 
-### Cognee Open Source:
+## Get Started
 
-- Interconnects any type of data — including past conversations, files, images, and audio transcriptions
-- Replaces traditional RAG systems with a unified memory layer built on graphs and vectors
-- Reduces developer effort and infrastructure cost while improving quality and precision
-- Provides Pythonic data pipelines for ingestion from 30+ data sources
-- Offers high customizability through user-defined tasks, modular pipelines, and built-in search endpoints
+Get started quickly with a Google Colab  <a href="https://colab.research.google.com/drive/12Vi9zID-M3fpKpKiaqDBvkk98ElkRPWy?usp=sharing">notebook</a> , <a href="https://deepnote.com/workspace/cognee-382213d0-0444-4c89-8265-13770e333c02/project/cognee-demo-78ffacb9-5832-4611-bb1a-560386068b30/notebook/Notebook-1-75b24cda566d4c24ab348f7150792601?utm_source=share-modal&utm_medium=product-shared-content&utm_campaign=notebook&utm_content=78ffacb9-5832-4611-bb1a-560386068b30">Deepnote notebook</a> or  <a href="https://github.com/topoteretes/cognee/tree/main/cognee-starter-kit">starter repo</a>
 
 
-## Basic Usage & Feature Guide
+## About cognee
 
-To learn more, [check out this short, end-to-end Colab walkthrough](https://colab.research.google.com/drive/12Vi9zID-M3fpKpKiaqDBvkk98ElkRPWy?usp=sharing) of Cognee's core features.
+cognee works locally and stores your data on your device.
+Our hosted solution is just our deployment of OSS cognee on Modal, with the goal of making development and productionization easier.
 
-[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/12Vi9zID-M3fpKpKiaqDBvkk98ElkRPWy?usp=sharing)
+Self-hosted package:
 
-## Quickstart
+- Interconnects any kind of documents: past conversations, files, images, and audio transcriptions
+- Replaces RAG systems with a memory layer based on graphs and vectors
+- Reduces developer effort and cost, while increasing quality and precision
+- Provides Pythonic data pipelines that manage data ingestion from 30+ data sources
+- Is highly customizable with custom tasks, pipelines, and a set of built-in search endpoints
 
-Let’s try Cognee in just a few lines of code. For detailed setup and configuration, see the [Cognee Docs](https://docs.cognee.ai/getting-started/installation#environment-configuration).
+Hosted platform:
+- Includes a managed UI and a [hosted solution](https://www.cognee.ai)
 
-### Prerequisites
 
-- Python 3.10 to 3.13
 
-### Step 1: Install Cognee
+## Self-Hosted (Open Source)
 
-You can install Cognee with **pip**, **poetry**, **uv**, or your preferred Python package manager.
+
+### 📦 Installation
+
+You can install Cognee using either **pip**, **poetry**, **uv** or any other python package manager.
+
+Cognee supports Python 3.10 to 3.12
+
+#### With uv
 
 ```bash
 uv pip install cognee
 ```
 
-### Step 2: Configure the LLM
-```python
+Detailed instructions can be found in our [docs](https://docs.cognee.ai/getting-started/installation#environment-configuration)
+
+### 💻 Basic Usage
+
+#### Setup
+
+```
 import os
 os.environ["LLM_API_KEY"] = "YOUR OPENAI_API_KEY"
+
 ```
-Alternatively, create a `.env` file using our [template](https://github.com/topoteretes/cognee/blob/main/.env.template).
 
-To integrate other LLM providers, see our [LLM Provider Documentation](https://docs.cognee.ai/setup-configuration/llm-providers).
+You can also set the variables by creating .env file, using our <a href="https://github.com/topoteretes/cognee/blob/main/.env.template">template.</a>
+To use different LLM providers, for more info check out our <a href="https://docs.cognee.ai/setup-configuration/llm-providers">documentation</a>
 
-### Step 3: Run the Pipeline
 
-Cognee will take your documents, generate a knowledge graph from them and then query the graph based on combined relationships. 
+#### Simple example
 
-Now, run a minimal pipeline:
+
+
+##### Python
+
+This script will run the default pipeline:
 
 ```python
 import cognee
 import asyncio
-from pprint import pprint
 
 
 async def main():
@@ -132,72 +147,80 @@ async def main():
     await cognee.memify()
 
     # Query the knowledge graph
-    results = await cognee.search("What does Cognee do?")
+    results = await cognee.search("What does cognee do?")
 
     # Display the results
     for result in results:
-        pprint(result)
+        print(result)
 
 
 if __name__ == '__main__':
     asyncio.run(main())
 
 ```
-
-As you can see, the output is generated from the document we previously stored in Cognee:
-
-```bash
-  Cognee turns documents into AI memory.
+Example output:
 ```
+  Cognee turns documents into AI memory.
 
-### Use the Cognee CLI 
+```
+##### Via CLI
 
-As an alternative, you can get started with these essential commands:
+Let's get the basics covered
 
-```bash
+```
 cognee-cli add "Cognee turns documents into AI memory."
 
 cognee-cli cognify
 
-cognee-cli search "What does Cognee do?"
+cognee-cli search "What does cognee do?"
 cognee-cli delete --all
 
 ```
-
-To open the local UI, run:
-```bash
+or run
+```
 cognee-cli -ui
 ```
 
-## Demos & Examples
 
-See Cognee in action:
-
-### Persistent Agent Memory
-
-[Cognee Memory for LangGraph Agents](https://github.com/user-attachments/assets/e113b628-7212-4a2b-b288-0be39a93a1c3)
-
-### Simple GraphRAG
-
-[Watch Demo](https://github.com/user-attachments/assets/f2186b2e-305a-42b0-9c2d-9f4473f15df8)
-
-### Cognee with Ollama
-
-[Watch Demo](https://github.com/user-attachments/assets/39672858-f774-4136-b957-1e2de67b8981)
+</div>
 
 
-## Community & Support
+### Hosted Platform
 
-### Contributing
-We welcome contributions from the community! Your input helps make Cognee better for everyone. See [`CONTRIBUTING.md`](CONTRIBUTING.md) to get started.
+Get up and running in minutes with automatic updates, analytics, and enterprise security.
 
-### Code of Conduct
+1. Sign up on [cogwit](https://www.cognee.ai)
+2. Add your API key to local UI and sync your data to Cogwit
 
-We're committed to fostering an inclusive and respectful community. Read our [Code of Conduct](https://github.com/topoteretes/cognee/blob/main/CODE_OF_CONDUCT.md) for guidelines.
 
-## Research & Citation
 
-We recently published a research paper on optimizing knowledge graphs for LLM reasoning:
+
+## Demos
+
+1. Cogwit Beta demo:
+
+[Cogwit Beta](https://github.com/user-attachments/assets/fa520cd2-2913-4246-a444-902ea5242cb0)
+
+2. Simple GraphRAG demo
+
+[Simple GraphRAG demo](https://github.com/user-attachments/assets/d80b0776-4eb9-4b8e-aa22-3691e2d44b8f)
+
+3. cognee with Ollama
+
+[cognee with local models](https://github.com/user-attachments/assets/8621d3e8-ecb8-4860-afb2-5594f2ee17db)
+
+
+## Contributing
+Your contributions are at the core of making this a true open source project. Any contributions you make are **greatly appreciated**. See [`CONTRIBUTING.md`](CONTRIBUTING.md) for more information.
+
+
+## Code of Conduct
+
+We are committed to making open source an enjoyable and respectful experience for our community. See <a href="https://github.com/topoteretes/cognee/blob/main/CODE_OF_CONDUCT.md"><code>CODE_OF_CONDUCT</code></a> for more information.
+
+## Citation
+
+We now have a paper you can cite:
 
 ```bibtex
 @misc{markovic2025optimizinginterfaceknowledgegraphs,

alembic/env.py

@@ -87,6 +87,11 @@ db_engine = get_relational_engine()
 
 print("Using database:", db_engine.db_uri)
 
+if "sqlite" in db_engine.db_uri:
+    from cognee.infrastructure.utils.run_sync import run_sync
+
+    run_sync(db_engine.create_database())
+
 config.set_section_option(
     config.config_ini_section,
     "SQLALCHEMY_DATABASE_URI",

alembic/versions/211ab850ef3d_add_sync_operations_table.py

@@ -10,7 +10,6 @@ from typing import Sequence, Union
 
 from alembic import op
 import sqlalchemy as sa
-from sqlalchemy.dialects import postgresql
 
 
 # revision identifiers, used by Alembic.
@@ -27,34 +26,7 @@ def upgrade() -> None:
     connection = op.get_bind()
     inspector = sa.inspect(connection)
 
-    if op.get_context().dialect.name == "postgresql":
-        syncstatus_enum = postgresql.ENUM(
-            "STARTED", "IN_PROGRESS", "COMPLETED", "FAILED", "CANCELLED", name="syncstatus"
-        )
-        syncstatus_enum.create(op.get_bind(), checkfirst=True)
-
     if "sync_operations" not in inspector.get_table_names():
-        if op.get_context().dialect.name == "postgresql":
-            syncstatus = postgresql.ENUM(
-                "STARTED",
-                "IN_PROGRESS",
-                "COMPLETED",
-                "FAILED",
-                "CANCELLED",
-                name="syncstatus",
-                create_type=False,
-            )
-        else:
-            syncstatus = sa.Enum(
-                "STARTED",
-                "IN_PROGRESS",
-                "COMPLETED",
-                "FAILED",
-                "CANCELLED",
-                name="syncstatus",
-                create_type=False,
-            )
-
         # Table doesn't exist, create it normally
         op.create_table(
             "sync_operations",
@@ -62,7 +34,15 @@ def upgrade() -> None:
             sa.Column("run_id", sa.Text(), nullable=True),
             sa.Column(
                 "status",
-                syncstatus,
+                sa.Enum(
+                    "STARTED",
+                    "IN_PROGRESS",
+                    "COMPLETED",
+                    "FAILED",
+                    "CANCELLED",
+                    name="syncstatus",
+                    create_type=False,
+                ),
                 nullable=True,
             ),
             sa.Column("progress_percentage", sa.Integer(), nullable=True),

alembic/versions/46a6ce2bd2b2_expand_dataset_database_with_json_.py

@@ -1,333 +0,0 @@
-"""Expand dataset database with json connection field
-
-Revision ID: 46a6ce2bd2b2
-Revises: 76625596c5c3
-Create Date: 2025-11-25 17:56:28.938931
-
-"""
-
-from typing import Sequence, Union
-
-from alembic import op
-import sqlalchemy as sa
-
-
-# revision identifiers, used by Alembic.
-revision: str = "46a6ce2bd2b2"
-down_revision: Union[str, None] = "76625596c5c3"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-graph_constraint_name = "dataset_database_graph_database_name_key"
-vector_constraint_name = "dataset_database_vector_database_name_key"
-TABLE_NAME = "dataset_database"
-
-
-def _get_column(inspector, table, name, schema=None):
-    for col in inspector.get_columns(table, schema=schema):
-        if col["name"] == name:
-            return col
-    return None
-
-
-def _recreate_table_without_unique_constraint_sqlite(op, insp):
-    """
-    SQLite cannot drop unique constraints on individual columns. We must:
-    1. Create a new table without the unique constraints.
-    2. Copy data from the old table.
-    3. Drop the old table.
-    4. Rename the new table.
-    """
-    conn = op.get_bind()
-
-    # Create new table definition (without unique constraints)
-    op.create_table(
-        f"{TABLE_NAME}_new",
-        sa.Column("owner_id", sa.UUID()),
-        sa.Column("dataset_id", sa.UUID(), primary_key=True, nullable=False),
-        sa.Column("vector_database_name", sa.String(), nullable=False),
-        sa.Column("graph_database_name", sa.String(), nullable=False),
-        sa.Column("vector_database_provider", sa.String(), nullable=False),
-        sa.Column("graph_database_provider", sa.String(), nullable=False),
-        sa.Column(
-            "vector_dataset_database_handler",
-            sa.String(),
-            unique=False,
-            nullable=False,
-            server_default="lancedb",
-        ),
-        sa.Column(
-            "graph_dataset_database_handler",
-            sa.String(),
-            unique=False,
-            nullable=False,
-            server_default="kuzu",
-        ),
-        sa.Column("vector_database_url", sa.String()),
-        sa.Column("graph_database_url", sa.String()),
-        sa.Column("vector_database_key", sa.String()),
-        sa.Column("graph_database_key", sa.String()),
-        sa.Column(
-            "graph_database_connection_info",
-            sa.JSON(),
-            nullable=False,
-            server_default=sa.text("'{}'"),
-        ),
-        sa.Column(
-            "vector_database_connection_info",
-            sa.JSON(),
-            nullable=False,
-            server_default=sa.text("'{}'"),
-        ),
-        sa.Column("created_at", sa.DateTime()),
-        sa.Column("updated_at", sa.DateTime()),
-        sa.ForeignKeyConstraint(["dataset_id"], ["datasets.id"], ondelete="CASCADE"),
-        sa.ForeignKeyConstraint(["owner_id"], ["principals.id"], ondelete="CASCADE"),
-    )
-
-    # Copy data into new table
-    conn.execute(
-        sa.text(f"""
-        INSERT INTO {TABLE_NAME}_new
-        SELECT
-            owner_id,
-            dataset_id,
-            vector_database_name,
-            graph_database_name,
-            vector_database_provider,
-            graph_database_provider,
-            vector_dataset_database_handler,
-            graph_dataset_database_handler,
-            vector_database_url,
-            graph_database_url,
-            vector_database_key,
-            graph_database_key,
-            COALESCE(graph_database_connection_info, '{{}}'),
-            COALESCE(vector_database_connection_info, '{{}}'),
-            created_at,
-            updated_at
-        FROM {TABLE_NAME}
-    """)
-    )
-
-    # Drop old table
-    op.drop_table(TABLE_NAME)
-
-    # Rename new table
-    op.rename_table(f"{TABLE_NAME}_new", TABLE_NAME)
-
-
-def _recreate_table_with_unique_constraint_sqlite(op, insp):
-    """
-    SQLite cannot drop unique constraints on individual columns. We must:
-    1. Create a new table without the unique constraints.
-    2. Copy data from the old table.
-    3. Drop the old table.
-    4. Rename the new table.
-    """
-    conn = op.get_bind()
-
-    # Create new table definition (without unique constraints)
-    op.create_table(
-        f"{TABLE_NAME}_new",
-        sa.Column("owner_id", sa.UUID()),
-        sa.Column("dataset_id", sa.UUID(), primary_key=True, nullable=False),
-        sa.Column("vector_database_name", sa.String(), nullable=False, unique=True),
-        sa.Column("graph_database_name", sa.String(), nullable=False, unique=True),
-        sa.Column("vector_database_provider", sa.String(), nullable=False),
-        sa.Column("graph_database_provider", sa.String(), nullable=False),
-        sa.Column(
-            "vector_dataset_database_handler",
-            sa.String(),
-            unique=False,
-            nullable=False,
-            server_default="lancedb",
-        ),
-        sa.Column(
-            "graph_dataset_database_handler",
-            sa.String(),
-            unique=False,
-            nullable=False,
-            server_default="kuzu",
-        ),
-        sa.Column("vector_database_url", sa.String()),
-        sa.Column("graph_database_url", sa.String()),
-        sa.Column("vector_database_key", sa.String()),
-        sa.Column("graph_database_key", sa.String()),
-        sa.Column(
-            "graph_database_connection_info",
-            sa.JSON(),
-            nullable=False,
-            server_default=sa.text("'{}'"),
-        ),
-        sa.Column(
-            "vector_database_connection_info",
-            sa.JSON(),
-            nullable=False,
-            server_default=sa.text("'{}'"),
-        ),
-        sa.Column("created_at", sa.DateTime()),
-        sa.Column("updated_at", sa.DateTime()),
-        sa.ForeignKeyConstraint(["dataset_id"], ["datasets.id"], ondelete="CASCADE"),
-        sa.ForeignKeyConstraint(["owner_id"], ["principals.id"], ondelete="CASCADE"),
-    )
-
-    # Copy data into new table
-    conn.execute(
-        sa.text(f"""
-        INSERT INTO {TABLE_NAME}_new
-        SELECT
-            owner_id,
-            dataset_id,
-            vector_database_name,
-            graph_database_name,
-            vector_database_provider,
-            graph_database_provider,
-            vector_dataset_database_handler,
-            graph_dataset_database_handler,
-            vector_database_url,
-            graph_database_url,
-            vector_database_key,
-            graph_database_key,
-            COALESCE(graph_database_connection_info, '{{}}'),
-            COALESCE(vector_database_connection_info, '{{}}'),
-            created_at,
-            updated_at
-        FROM {TABLE_NAME}
-    """)
-    )
-
-    # Drop old table
-    op.drop_table(TABLE_NAME)
-
-    # Rename new table
-    op.rename_table(f"{TABLE_NAME}_new", TABLE_NAME)
-
-
-def upgrade() -> None:
-    conn = op.get_bind()
-    insp = sa.inspect(conn)
-
-    unique_constraints = insp.get_unique_constraints(TABLE_NAME)
-
-    vector_database_connection_info_column = _get_column(
-        insp, "dataset_database", "vector_database_connection_info"
-    )
-    if not vector_database_connection_info_column:
-        op.add_column(
-            "dataset_database",
-            sa.Column(
-                "vector_database_connection_info",
-                sa.JSON(),
-                unique=False,
-                nullable=False,
-                server_default=sa.text("'{}'"),
-            ),
-        )
-
-    vector_dataset_database_handler = _get_column(
-        insp, "dataset_database", "vector_dataset_database_handler"
-    )
-    if not vector_dataset_database_handler:
-        # Add LanceDB as the default graph dataset database handler
-        op.add_column(
-            "dataset_database",
-            sa.Column(
-                "vector_dataset_database_handler",
-                sa.String(),
-                unique=False,
-                nullable=False,
-                server_default="lancedb",
-            ),
-        )
-
-    graph_database_connection_info_column = _get_column(
-        insp, "dataset_database", "graph_database_connection_info"
-    )
-    if not graph_database_connection_info_column:
-        op.add_column(
-            "dataset_database",
-            sa.Column(
-                "graph_database_connection_info",
-                sa.JSON(),
-                unique=False,
-                nullable=False,
-                server_default=sa.text("'{}'"),
-            ),
-        )
-
-    graph_dataset_database_handler = _get_column(
-        insp, "dataset_database", "graph_dataset_database_handler"
-    )
-    if not graph_dataset_database_handler:
-        # Add Kuzu as the default graph dataset database handler
-        op.add_column(
-            "dataset_database",
-            sa.Column(
-                "graph_dataset_database_handler",
-                sa.String(),
-                unique=False,
-                nullable=False,
-                server_default="kuzu",
-            ),
-        )
-
-    with op.batch_alter_table("dataset_database", schema=None) as batch_op:
-        # Drop the unique constraint to make unique=False
-        graph_constraint_to_drop = None
-        for uc in unique_constraints:
-            # Check if the constraint covers ONLY the target column
-            if uc["name"] == graph_constraint_name:
-                graph_constraint_to_drop = uc["name"]
-                break
-
-        vector_constraint_to_drop = None
-        for uc in unique_constraints:
-            # Check if the constraint covers ONLY the target column
-            if uc["name"] == vector_constraint_name:
-                vector_constraint_to_drop = uc["name"]
-                break
-
-        if (
-            vector_constraint_to_drop
-            and graph_constraint_to_drop
-            and op.get_context().dialect.name == "postgresql"
-        ):
-            # PostgreSQL
-            batch_op.drop_constraint(graph_constraint_name, type_="unique")
-            batch_op.drop_constraint(vector_constraint_name, type_="unique")
-
-        if op.get_context().dialect.name == "sqlite":
-            conn = op.get_bind()
-            # Fun fact: SQLite has hidden auto indexes for unique constraints that can't be dropped or accessed directly
-            #           So we need to check for them and drop them by recreating the table (altering column also won't work)
-            result = conn.execute(sa.text("PRAGMA index_list('dataset_database')"))
-            rows = result.fetchall()
-            unique_auto_indexes = [row for row in rows if row[3] == "u"]
-            for row in unique_auto_indexes:
-                result = conn.execute(sa.text(f"PRAGMA index_info('{row[1]}')"))
-                index_info = result.fetchall()
-                if index_info[0][2] == "vector_database_name":
-                    # In case a unique index exists on vector_database_name, drop it and the graph_database_name one
-                    _recreate_table_without_unique_constraint_sqlite(op, insp)
-
-
-def downgrade() -> None:
-    conn = op.get_bind()
-    insp = sa.inspect(conn)
-
-    if op.get_context().dialect.name == "sqlite":
-        _recreate_table_with_unique_constraint_sqlite(op, insp)
-    elif op.get_context().dialect.name == "postgresql":
-        with op.batch_alter_table("dataset_database", schema=None) as batch_op:
-            # Re-add the unique constraint to return to unique=True
-            batch_op.create_unique_constraint(graph_constraint_name, ["graph_database_name"])
-
-        with op.batch_alter_table("dataset_database", schema=None) as batch_op:
-            # Re-add the unique constraint to return to unique=True
-            batch_op.create_unique_constraint(vector_constraint_name, ["vector_database_name"])
-
-    op.drop_column("dataset_database", "vector_database_connection_info")
-    op.drop_column("dataset_database", "graph_database_connection_info")
-    op.drop_column("dataset_database", "vector_dataset_database_handler")
-    op.drop_column("dataset_database", "graph_dataset_database_handler")

alembic/versions/482cd6517ce4_add_default_user.py

@@ -23,8 +23,11 @@ depends_on: Union[str, Sequence[str], None] = "8057ae7329c2"
 
 
 def upgrade() -> None:
-    pass
+    try:
+        await_only(create_default_user())
+    except UserAlreadyExists:
+        pass  # It's fine if the default user already exists
 
 
 def downgrade() -> None:
-    pass
+    await_only(delete_user("default_user@example.com"))

alembic/versions/76625596c5c3_expand_dataset_database_for_multi_user.py

@@ -1,98 +0,0 @@
-"""Expand dataset database for multi user
-
-Revision ID: 76625596c5c3
-Revises: 211ab850ef3d
-Create Date: 2025-10-30 12:55:20.239562
-
-"""
-
-from typing import Sequence, Union
-
-from alembic import op
-import sqlalchemy as sa
-
-
-# revision identifiers, used by Alembic.
-revision: str = "76625596c5c3"
-down_revision: Union[str, None] = "c946955da633"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def _get_column(inspector, table, name, schema=None):
-    for col in inspector.get_columns(table, schema=schema):
-        if col["name"] == name:
-            return col
-    return None
-
-
-def upgrade() -> None:
-    conn = op.get_bind()
-    insp = sa.inspect(conn)
-
-    vector_database_provider_column = _get_column(
-        insp, "dataset_database", "vector_database_provider"
-    )
-    if not vector_database_provider_column:
-        op.add_column(
-            "dataset_database",
-            sa.Column(
-                "vector_database_provider",
-                sa.String(),
-                unique=False,
-                nullable=False,
-                server_default="lancedb",
-            ),
-        )
-
-    graph_database_provider_column = _get_column(
-        insp, "dataset_database", "graph_database_provider"
-    )
-    if not graph_database_provider_column:
-        op.add_column(
-            "dataset_database",
-            sa.Column(
-                "graph_database_provider",
-                sa.String(),
-                unique=False,
-                nullable=False,
-                server_default="kuzu",
-            ),
-        )
-
-    vector_database_url_column = _get_column(insp, "dataset_database", "vector_database_url")
-    if not vector_database_url_column:
-        op.add_column(
-            "dataset_database",
-            sa.Column("vector_database_url", sa.String(), unique=False, nullable=True),
-        )
-
-    graph_database_url_column = _get_column(insp, "dataset_database", "graph_database_url")
-    if not graph_database_url_column:
-        op.add_column(
-            "dataset_database",
-            sa.Column("graph_database_url", sa.String(), unique=False, nullable=True),
-        )
-
-    vector_database_key_column = _get_column(insp, "dataset_database", "vector_database_key")
-    if not vector_database_key_column:
-        op.add_column(
-            "dataset_database",
-            sa.Column("vector_database_key", sa.String(), unique=False, nullable=True),
-        )
-
-    graph_database_key_column = _get_column(insp, "dataset_database", "graph_database_key")
-    if not graph_database_key_column:
-        op.add_column(
-            "dataset_database",
-            sa.Column("graph_database_key", sa.String(), unique=False, nullable=True),
-        )
-
-
-def downgrade() -> None:
-    op.drop_column("dataset_database", "vector_database_provider")
-    op.drop_column("dataset_database", "graph_database_provider")
-    op.drop_column("dataset_database", "vector_database_url")
-    op.drop_column("dataset_database", "graph_database_url")
-    op.drop_column("dataset_database", "vector_database_key")
-    op.drop_column("dataset_database", "graph_database_key")

alembic/versions/8057ae7329c2_initial_migration.py

@@ -18,8 +18,11 @@ depends_on: Union[str, Sequence[str], None] = None
 
 
 def upgrade() -> None:
-    pass
+    db_engine = get_relational_engine()
+    # we might want to delete this
+    await_only(db_engine.create_database())
 
 
 def downgrade() -> None:
-    pass
+    db_engine = get_relational_engine()
+    await_only(db_engine.delete_database())

alembic/versions/ab7e313804ae_permission_system_rework.py

@@ -144,58 +144,44 @@ def _create_data_permission(conn, user_id, data_id, permission_name):
     )
 
 
-def _get_column(inspector, table, name, schema=None):
-    for col in inspector.get_columns(table, schema=schema):
-        if col["name"] == name:
-            return col
-    return None
-
-
 def upgrade() -> None:
     conn = op.get_bind()
-    insp = sa.inspect(conn)
 
-    dataset_id_column = _get_column(insp, "acls", "dataset_id")
-    if not dataset_id_column:
-        # Recreate ACLs table with default permissions set to datasets instead of documents
-        op.drop_table("acls")
+    # Recreate ACLs table with default permissions set to datasets instead of documents
+    op.drop_table("acls")
 
-        acls_table = op.create_table(
-            "acls",
-            sa.Column("id", UUID, primary_key=True, default=uuid4),
-            sa.Column(
-                "created_at", sa.DateTime(timezone=True), default=lambda: datetime.now(timezone.utc)
-            ),
-            sa.Column(
-                "updated_at",
-                sa.DateTime(timezone=True),
-                onupdate=lambda: datetime.now(timezone.utc),
-            ),
-            sa.Column("principal_id", UUID, sa.ForeignKey("principals.id")),
-            sa.Column("permission_id", UUID, sa.ForeignKey("permissions.id")),
-            sa.Column("dataset_id", UUID, sa.ForeignKey("datasets.id", ondelete="CASCADE")),
-        )
+    acls_table = op.create_table(
+        "acls",
+        sa.Column("id", UUID, primary_key=True, default=uuid4),
+        sa.Column(
+            "created_at", sa.DateTime(timezone=True), default=lambda: datetime.now(timezone.utc)
+        ),
+        sa.Column(
+            "updated_at", sa.DateTime(timezone=True), onupdate=lambda: datetime.now(timezone.utc)
+        ),
+        sa.Column("principal_id", UUID, sa.ForeignKey("principals.id")),
+        sa.Column("permission_id", UUID, sa.ForeignKey("permissions.id")),
+        sa.Column("dataset_id", UUID, sa.ForeignKey("datasets.id", ondelete="CASCADE")),
+    )
 
-        # Note: We can't use any Cognee model info to gather data (as it can change) in database so we must use our own table
-        #       definition or load what is in the database
-        dataset_table = _define_dataset_table()
-        datasets = conn.execute(sa.select(dataset_table)).fetchall()
+    # Note: We can't use any Cognee model info to gather data (as it can change) in database so we must use our own table
+    #       definition or load what is in the database
+    dataset_table = _define_dataset_table()
+    datasets = conn.execute(sa.select(dataset_table)).fetchall()
 
-        if not datasets:
-            return
+    if not datasets:
+        return
 
-        acl_list = []
+    acl_list = []
 
-        for dataset in datasets:
-            acl_list.append(_create_dataset_permission(conn, dataset.owner_id, dataset.id, "read"))
-            acl_list.append(_create_dataset_permission(conn, dataset.owner_id, dataset.id, "write"))
-            acl_list.append(_create_dataset_permission(conn, dataset.owner_id, dataset.id, "share"))
-            acl_list.append(
-                _create_dataset_permission(conn, dataset.owner_id, dataset.id, "delete")
-            )
+    for dataset in datasets:
+        acl_list.append(_create_dataset_permission(conn, dataset.owner_id, dataset.id, "read"))
+        acl_list.append(_create_dataset_permission(conn, dataset.owner_id, dataset.id, "write"))
+        acl_list.append(_create_dataset_permission(conn, dataset.owner_id, dataset.id, "share"))
+        acl_list.append(_create_dataset_permission(conn, dataset.owner_id, dataset.id, "delete"))
 
-        if acl_list:
-            op.bulk_insert(acls_table, acl_list)
+    if acl_list:
+        op.bulk_insert(acls_table, acl_list)
 
 
 def downgrade() -> None:

alembic/versions/c946955da633_multi_tenant_support.py

@@ -1,137 +0,0 @@
-"""Multi Tenant Support
-
-Revision ID: c946955da633
-Revises: 211ab850ef3d
-Create Date: 2025-11-04 18:11:09.325158
-
-"""
-
-from typing import Sequence, Union
-from datetime import datetime, timezone
-from uuid import uuid4
-
-from alembic import op
-import sqlalchemy as sa
-
-# revision identifiers, used by Alembic.
-revision: str = "c946955da633"
-down_revision: Union[str, None] = "211ab850ef3d"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def _now():
-    return datetime.now(timezone.utc)
-
-
-def _define_user_table() -> sa.Table:
-    table = sa.Table(
-        "users",
-        sa.MetaData(),
-        sa.Column(
-            "id",
-            sa.UUID,
-            sa.ForeignKey("principals.id", ondelete="CASCADE"),
-            primary_key=True,
-            nullable=False,
-        ),
-        sa.Column("tenant_id", sa.UUID, sa.ForeignKey("tenants.id"), index=True, nullable=True),
-    )
-    return table
-
-
-def _define_dataset_table() -> sa.Table:
-    # Note: We can't use any Cognee model info to gather data (as it can change) in database so we must use our own table
-    #       definition or load what is in the database
-    table = sa.Table(
-        "datasets",
-        sa.MetaData(),
-        sa.Column("id", sa.UUID, primary_key=True, default=uuid4),
-        sa.Column("name", sa.Text),
-        sa.Column(
-            "created_at",
-            sa.DateTime(timezone=True),
-            default=lambda: datetime.now(timezone.utc),
-        ),
-        sa.Column(
-            "updated_at",
-            sa.DateTime(timezone=True),
-            onupdate=lambda: datetime.now(timezone.utc),
-        ),
-        sa.Column("owner_id", sa.UUID(), sa.ForeignKey("principals.id"), index=True),
-        sa.Column("tenant_id", sa.UUID(), sa.ForeignKey("tenants.id"), index=True, nullable=True),
-    )
-
-    return table
-
-
-def _get_column(inspector, table, name, schema=None):
-    for col in inspector.get_columns(table, schema=schema):
-        if col["name"] == name:
-            return col
-    return None
-
-
-def upgrade() -> None:
-    conn = op.get_bind()
-    insp = sa.inspect(conn)
-
-    dataset = _define_dataset_table()
-    user = _define_user_table()
-
-    if "user_tenants" not in insp.get_table_names():
-        # Define table with all necessary columns including primary key
-        user_tenants = op.create_table(
-            "user_tenants",
-            sa.Column("user_id", sa.UUID, sa.ForeignKey("users.id"), primary_key=True),
-            sa.Column("tenant_id", sa.UUID, sa.ForeignKey("tenants.id"), primary_key=True),
-            sa.Column(
-                "created_at", sa.DateTime(timezone=True), default=lambda: datetime.now(timezone.utc)
-            ),
-        )
-
-        # Get all users with their tenant_id
-        user_data = conn.execute(
-            sa.select(user.c.id, user.c.tenant_id).where(user.c.tenant_id.isnot(None))
-        ).fetchall()
-
-        # Insert into user_tenants table
-        if user_data:
-            op.bulk_insert(
-                user_tenants,
-                [
-                    {"user_id": user_id, "tenant_id": tenant_id, "created_at": _now()}
-                    for user_id, tenant_id in user_data
-                ],
-            )
-
-    tenant_id_column = _get_column(insp, "datasets", "tenant_id")
-    if not tenant_id_column:
-        op.add_column("datasets", sa.Column("tenant_id", sa.UUID(), nullable=True))
-
-        # Build subquery, select users.tenant_id for each dataset.owner_id
-        tenant_id_from_dataset_owner = (
-            sa.select(user.c.tenant_id).where(user.c.id == dataset.c.owner_id).scalar_subquery()
-        )
-
-        if op.get_context().dialect.name == "sqlite":
-            # If column doesn't exist create new original_extension column and update from values of extension column
-            with op.batch_alter_table("datasets") as batch_op:
-                batch_op.execute(
-                    dataset.update().values(
-                        tenant_id=tenant_id_from_dataset_owner,
-                    )
-                )
-        else:
-            conn = op.get_bind()
-            conn.execute(dataset.update().values(tenant_id=tenant_id_from_dataset_owner))
-
-        op.create_index(op.f("ix_datasets_tenant_id"), "datasets", ["tenant_id"])
-
-
-def downgrade() -> None:
-    # ### commands auto generated by Alembic - please adjust! ###
-    op.drop_table("user_tenants")
-    op.drop_index(op.f("ix_datasets_tenant_id"), table_name="datasets")
-    op.drop_column("datasets", "tenant_id")
-    # ### end Alembic commands ###

cognee-frontend/package.json

@@ -9,13 +9,13 @@
     "lint": "next lint"
   },
   "dependencies": {
-    "@auth0/nextjs-auth0": "^4.13.1",
+    "@auth0/nextjs-auth0": "^4.6.0",
     "classnames": "^2.5.1",
     "culori": "^4.0.1",
     "d3-force-3d": "^3.0.6",
-    "next": "16.1.1",
-    "react": "^19.2.0",
-    "react-dom": "^19.2.0",
+    "next": "15.3.3",
+    "react": "^19.0.0",
+    "react-dom": "^19.0.0",
     "react-force-graph-2d": "^1.27.1",
     "uuid": "^9.0.1"
   },
@@ -24,11 +24,11 @@
     "@tailwindcss/postcss": "^4.1.7",
     "@types/culori": "^4.0.0",
     "@types/node": "^20",
-    "@types/react": "^19",
-    "@types/react-dom": "^19",
+    "@types/react": "^18",
+    "@types/react-dom": "^18",
     "@types/uuid": "^9.0.8",
     "eslint": "^9",
-    "eslint-config-next": "^16.0.4",
+    "eslint-config-next": "^15.3.3",
     "eslint-config-prettier": "^10.1.5",
     "tailwindcss": "^4.1.7",
     "typescript": "^5"

cognee-frontend/src/app/(graph)/CrewAITrigger.tsx

@@ -0,0 +1,119 @@
+import { useState } from "react";
+import { fetch } from "@/utils";
+import { v4 as uuid4 } from "uuid";
+import { LoadingIndicator } from "@/ui/App";
+import { CTAButton, Input } from "@/ui/elements";
+
+interface CrewAIFormPayload extends HTMLFormElement {
+  username1: HTMLInputElement;
+  username2: HTMLInputElement;
+}
+
+interface CrewAITriggerProps {
+  // eslint-disable-next-line @typescript-eslint/no-explicit-any
+  onData: (data: any) => void;
+  // eslint-disable-next-line @typescript-eslint/no-explicit-any
+  onActivity: (activities: any) => void;
+}
+
+export default function CrewAITrigger({ onData, onActivity }: CrewAITriggerProps) {
+  const [isCrewAIRunning, setIsCrewAIRunning] = useState(false);
+  
+  const handleRunCrewAI = (event: React.FormEvent<CrewAIFormPayload>) => {
+    event.preventDefault();
+    const formElements = event.currentTarget;
+
+    const crewAIConfig = {
+      username1: formElements.username1.value,
+      username2: formElements.username2.value,
+    };
+
+    const backendApiUrl = process.env.NEXT_PUBLIC_BACKEND_API_URL;
+    const wsUrl = backendApiUrl.replace(/^http(s)?/, "ws");
+
+    const websocket = new WebSocket(`${wsUrl}/v1/crewai/subscribe`);
+
+    onActivity([{ id: uuid4(), timestamp: Date.now(), activity: "Dispatching hiring crew agents" }]);
+
+    websocket.onmessage = (event) => {
+      const data = JSON.parse(event.data);
+
+      if (data.status === "PipelineRunActivity") {
+        onActivity([data.payload]);
+        return;
+      }
+
+      onData({
+        nodes: data.payload.nodes,
+        links: data.payload.edges,
+      });
+
+      const nodes_type_map: { [key: string]: number } = {};
+
+      for (let i = 0; i < data.payload.nodes.length; i++) {
+        const node = data.payload.nodes[i];
+        if (!nodes_type_map[node.type]) {
+          nodes_type_map[node.type] = 0;
+        }
+        nodes_type_map[node.type] += 1;
+      }
+
+      const activityMessage = Object.entries(nodes_type_map).reduce((message, [type, count]) => {
+        return `${message}\n | ${type}: ${count}`;
+      }, "Graph updated:");
+
+      onActivity([{
+        id: uuid4(),
+        timestamp: Date.now(),
+        activity: activityMessage,
+      }]);
+
+      if (data.status === "PipelineRunCompleted") {
+        websocket.close();
+      }
+    };
+
+    onData(null);
+    setIsCrewAIRunning(true);
+
+    return fetch("/v1/crewai/run", {
+      method: "POST",
+      body: JSON.stringify(crewAIConfig),
+      headers: {
+        "Content-Type": "application/json",
+      },
+    })
+      .then(response => response.json())
+      .then(() => {
+        onActivity([{ id: uuid4(), timestamp: Date.now(), activity: "Hiring crew agents made a decision" }]);
+      })
+      .catch(() => {
+        onActivity([{ id: uuid4(), timestamp: Date.now(), activity: "Hiring crew agents had problems while executing" }]);
+      })
+      .finally(() => {
+        websocket.close();
+        setIsCrewAIRunning(false);
+      });
+  };
+
+  return (
+    <form className="w-full flex flex-col gap-2" onSubmit={handleRunCrewAI}>
+      <h1 className="text-2xl text-white">Cognee Dev Mexican Standoff</h1>
+      <span className="text-white">Agents compare GitHub profiles, and make a decision who is a better developer</span>
+      <div className="flex flex-row gap-2">
+        <div className="flex flex-col w-full flex-1/2">
+          <label className="block mb-1 text-white" htmlFor="username1">GitHub username</label>
+          <Input name="username1" type="text" placeholder="Github Username" required defaultValue="hajdul88" />
+        </div>
+        <div className="flex flex-col w-full flex-1/2">
+          <label className="block mb-1 text-white" htmlFor="username2">GitHub username</label>
+          <Input name="username2" type="text" placeholder="Github Username" required defaultValue="lxobr" />
+        </div>
+      </div>
+      <CTAButton type="submit" disabled={isCrewAIRunning} className="whitespace-nowrap">
+        Start Mexican Standoff
+        {isCrewAIRunning && <LoadingIndicator />}
+      </CTAButton>
+    </form>
+  );
+}

cognee-frontend/src/app/(graph)/GraphControls.tsx

@@ -6,6 +6,7 @@ import { NodeObject, LinkObject } from "react-force-graph-2d";
 import { ChangeEvent, useEffect, useImperativeHandle, useRef, useState } from "react";
 
 import { DeleteIcon } from "@/ui/Icons";
+// import { FeedbackForm } from "@/ui/Partials";
 import { CTAButton, Input, NeutralButton, Select } from "@/ui/elements";
 
 interface GraphControlsProps {
@@ -110,7 +111,7 @@ export default function GraphControls({ data, isAddNodeFormOpen, onGraphShapeCha
   };
 
   const [isAuthShapeChangeEnabled, setIsAuthShapeChangeEnabled] = useState(true);
-  const shapeChangeTimeout = useRef<number | null>(null);
+  const shapeChangeTimeout = useRef<number | null>();
   
   useEffect(() => {
     onGraphShapeChange(DEFAULT_GRAPH_SHAPE);
@@ -229,6 +230,12 @@ export default function GraphControls({ data, isAddNodeFormOpen, onGraphShapeCha
           )}
         </>
         {/* )} */}
+
+        {/* {selectedTab === "feedback" && (
+          <div className="flex flex-col gap-2">
+            <FeedbackForm onSuccess={() => {}} />
+          </div>
+        )} */}
       </div>
     </>
   );

cognee-frontend/src/app/(graph)/GraphView.tsx

@@ -1,6 +1,6 @@
 "use client";
 
-import { useCallback, useRef, useState, RefObject } from "react";
+import { useCallback, useRef, useState, MutableRefObject } from "react";
 
 import Link from "next/link";
 import { TextLogo } from "@/ui/App";
@@ -47,11 +47,11 @@ export default function GraphView() {
     updateData(newData);
   }, []);
 
-  const graphRef = useRef<GraphVisualizationAPI>(null);
+  const graphRef = useRef<GraphVisualizationAPI>();
 
-  const graphControls = useRef<GraphControlsAPI>(null);
+  const graphControls = useRef<GraphControlsAPI>();
 
-  const activityLog = useRef<ActivityLogAPI>(null);
+  const activityLog = useRef<ActivityLogAPI>();
 
   return (
     <main className="flex flex-col h-full">
@@ -74,18 +74,21 @@ export default function GraphView() {
       <div className="w-full h-full relative overflow-hidden">
         <GraphVisualization
           key={data?.nodes.length}
-          ref={graphRef as RefObject<GraphVisualizationAPI>}
+          ref={graphRef as MutableRefObject<GraphVisualizationAPI>}
           data={data}
-          graphControls={graphControls as RefObject<GraphControlsAPI>}
+          graphControls={graphControls as MutableRefObject<GraphControlsAPI>}
         />
 
         <div className="absolute top-2 left-2 flex flex-col gap-2">
           <div className="bg-gray-500 pt-4 pr-4 pb-4 pl-4 rounded-md w-sm">
             <CogneeAddWidget onData={onDataChange} />
           </div>
+          {/* <div className="bg-gray-500 pt-4 pr-4 pb-4 pl-4 rounded-md w-sm">
+            <CrewAITrigger onData={onDataChange} onActivity={(activities) => activityLog.current?.updateActivityLog(activities)} />
+          </div> */}
           <div className="bg-gray-500 pt-4 pr-4 pb-4 pl-4 rounded-md w-sm">
             <h2 className="text-xl text-white mb-4">Activity Log</h2>
-            <ActivityLog ref={activityLog as RefObject<ActivityLogAPI>} />
+            <ActivityLog ref={activityLog as MutableRefObject<ActivityLogAPI>} />
           </div>
         </div>
 
@@ -93,7 +96,7 @@ export default function GraphView() {
           <div className="bg-gray-500 pt-4 pr-4 pb-4 pl-4 rounded-md w-110">
             <GraphControls
               data={data}
-              ref={graphControls as RefObject<GraphControlsAPI>}
+              ref={graphControls as MutableRefObject<GraphControlsAPI>}
               isAddNodeFormOpen={isAddNodeFormOpen}
               onFitIntoView={() => graphRef.current!.zoomToFit(1000, 50)}
               onGraphShapeChange={(shape) => graphRef.current!.setGraphShape(shape)}

cognee-frontend/src/app/(graph)/GraphVisualization.tsx

@@ -1,7 +1,7 @@
 "use client";
 
 import classNames from "classnames";
-import { RefObject, useEffect, useImperativeHandle, useRef, useState, useCallback } from "react";
+import { MutableRefObject, useEffect, useImperativeHandle, useRef, useState, useCallback } from "react";
 import { forceCollide, forceManyBody } from "d3-force-3d";
 import dynamic from "next/dynamic";
 import { GraphControlsAPI } from "./GraphControls";
@@ -16,9 +16,9 @@ const ForceGraph = dynamic(() => import("react-force-graph-2d"), {
 import type { ForceGraphMethods, GraphData, LinkObject, NodeObject } from "react-force-graph-2d";
 
 interface GraphVisuzaliationProps {
-  ref: RefObject<GraphVisualizationAPI>;
+  ref: MutableRefObject<GraphVisualizationAPI>;
   data?: GraphData<NodeObject, LinkObject>;
-  graphControls: RefObject<GraphControlsAPI>;
+  graphControls: MutableRefObject<GraphControlsAPI>;
   className?: string;
 }
 
@@ -205,7 +205,7 @@ export default function GraphVisualization({ ref, data, graphControls, className
   // eslint-disable-next-line @typescript-eslint/no-unused-vars
   function handleDagError(loopNodeIds: (string | number)[]) {}
 
-  const graphRef = useRef<ForceGraphMethods>(null);
+  const graphRef = useRef<ForceGraphMethods>();
 
   useEffect(() => {
     if (data && graphRef.current) {
@@ -224,7 +224,6 @@ export default function GraphVisualization({ ref, data, graphControls, className
   ) => {
     if (!graphRef.current) {
       console.warn("GraphVisualization: graphRef not ready yet");
-      // eslint-disable-next-line @typescript-eslint/no-explicit-any
       return undefined as any;
     }
   
@@ -240,7 +239,7 @@ export default function GraphVisualization({ ref, data, graphControls, className
   return (
     <div ref={containerRef} className={classNames("w-full h-full", className)} id="graph-container">
       <ForceGraph
-        ref={graphRef as RefObject<ForceGraphMethods>}
+        ref={graphRef}
         width={dimensions.width}
         height={dimensions.height}
         dagMode={graphShape as unknown as undefined}

cognee-frontend/src/app/dashboard/page.tsx

@@ -1,8 +1,8 @@
-"use client";
+"use server";
 
 import Dashboard from "./Dashboard";
 
-export default function Page() {
+export default async function Page() {
   const accessToken = "";
 
   return (

cognee-frontend/src/app/page.tsx

@@ -1,3 +1,3 @@
 export { default } from "./dashboard/page";
 
-export const dynamic = "force-dynamic";
+// export const dynamic = "force-dynamic";

cognee-frontend/src/middleware.ts

@@ -2,7 +2,7 @@ import { NextResponse, type NextRequest } from "next/server";
 // import { auth0 } from "./modules/auth/auth0";
 
 // eslint-disable-next-line @typescript-eslint/no-unused-vars
-export async function proxy(request: NextRequest) {
+export async function middleware(request: NextRequest) {
   // if (process.env.USE_AUTH0_AUTHORIZATION?.toLowerCase() === "true") {
   //   if (request.nextUrl.pathname === "/auth/token") {
   //       return NextResponse.next();

cognee-frontend/src/modules/ingestion/useDatasets.ts

@@ -13,6 +13,7 @@ export interface Dataset {
 
 function useDatasets(useCloud = false) {
   const [datasets, setDatasets] = useState<Dataset[]>([]);
+  // eslint-disable-next-line @typescript-eslint/no-explicit-any
   // const statusTimeout = useRef<any>(null);
 
   // const fetchDatasetStatuses = useCallback((datasets: Dataset[]) => {

cognee-frontend/src/ui/Partials/FeedbackForm.tsx

@@ -0,0 +1,69 @@
+"use client";
+
+import { useState } from "react";
+import { LoadingIndicator } from "@/ui/App";
+import { fetch, useBoolean } from "@/utils";
+import { CTAButton, TextArea } from "@/ui/elements";
+
+interface SignInFormPayload extends HTMLFormElement {
+  feedback: HTMLTextAreaElement;
+}
+
+interface FeedbackFormProps {
+  onSuccess: () => void;
+}
+
+export default function FeedbackForm({ onSuccess }: FeedbackFormProps) {
+  const {
+    value: isSubmittingFeedback,
+    setTrue: disableFeedbackSubmit,
+    setFalse: enableFeedbackSubmit,
+  } = useBoolean(false);
+
+  const [feedbackError, setFeedbackError] = useState<string | null>(null);
+
+  const signIn = (event: React.FormEvent<SignInFormPayload>) => {
+    event.preventDefault();
+    const formElements = event.currentTarget;
+
+    setFeedbackError(null);
+    disableFeedbackSubmit();
+
+    fetch("/v1/crewai/feedback", {
+      method: "POST",
+      body: JSON.stringify({
+        feedback: formElements.feedback.value,
+      }),
+      headers: {
+        "Content-Type": "application/json",
+      },
+    })
+      .then(response => response.json())
+      .then(() => {
+        onSuccess();
+        formElements.feedback.value = "";
+      })
+      .catch(error => setFeedbackError(error.detail))
+      .finally(() => enableFeedbackSubmit());
+  };
+
+  return (
+    <form onSubmit={signIn} className="flex flex-col gap-2">
+      <div className="flex flex-col gap-2">
+        <div className="mb-4">
+          <label className="block text-white" htmlFor="feedback">Feedback on agent&apos;s reasoning</label>
+          <TextArea id="feedback" name="feedback" type="text" placeholder="Your feedback" />
+        </div>
+      </div>
+
+      <CTAButton type="submit">
+        <span>Submit feedback</span>
+        {isSubmittingFeedback && <LoadingIndicator />}
+      </CTAButton>
+
+      {feedbackError && (
+        <span className="text-s text-white">{feedbackError}</span>
+      )}
+    </form>
+  )
+}

cognee-frontend/src/ui/Partials/index.ts

@@ -3,3 +3,4 @@ export { default as Footer } from "./Footer/Footer";
 export { default as SearchView } from "./SearchView/SearchView";
 export { default as IFrameView } from "./IFrameView/IFrameView";
 // export { default as Explorer } from "./Explorer/Explorer";
+export { default as FeedbackForm } from "./FeedbackForm";

cognee-frontend/src/ui/elements/Notebook/Notebook.tsx

@@ -2,7 +2,7 @@
 
 import { v4 as uuid4 } from "uuid";
 import classNames from "classnames";
-import { Fragment, MouseEvent, RefObject, useCallback, useEffect, useRef, useState } from "react";
+import { Fragment, MouseEvent, MutableRefObject, useCallback, useEffect, useRef, useState } from "react";
 
 import { useModal } from "@/ui/elements/Modal";
 import { CaretIcon, CloseIcon, PlusIcon } from "@/ui/Icons";
@@ -282,7 +282,7 @@ export default function Notebook({ notebook, updateNotebook, runCell }: Notebook
 function CellResult({ content }: { content: [] }) {
   const parsedContent = [];
 
-  const graphRef = useRef<GraphVisualizationAPI>(null);
+  const graphRef = useRef<GraphVisualizationAPI>();
   const graphControls = useRef<GraphControlsAPI>({
     setSelectedNode: () => {},
     getSelectedNode: () => null,
@@ -298,7 +298,7 @@ function CellResult({ content }: { content: [] }) {
               <span className="text-sm pl-2 mb-4">reasoning graph</span>
               <GraphVisualization
                 data={transformInsightsGraphData(line)}
-                ref={graphRef as RefObject<GraphVisualizationAPI>}
+                ref={graphRef as MutableRefObject<GraphVisualizationAPI>}
                 graphControls={graphControls}
                 className="min-h-80"
               />
@@ -346,7 +346,7 @@ function CellResult({ content }: { content: [] }) {
                   <span className="text-sm pl-2 mb-4">reasoning graph (datasets: {datasetName})</span>
                   <GraphVisualization
                     data={transformToVisualizationData(graph)}
-                    ref={graphRef as RefObject<GraphVisualizationAPI>}
+                    ref={graphRef as MutableRefObject<GraphVisualizationAPI>}
                     graphControls={graphControls}
                     className="min-h-80"
                   />
@@ -377,7 +377,7 @@ function CellResult({ content }: { content: [] }) {
               <span className="text-sm pl-2 mb-4">reasoning graph (datasets: {datasetName})</span>
               <GraphVisualization
                 data={transformToVisualizationData(graph)}
-                ref={graphRef as RefObject<GraphVisualizationAPI>}
+                ref={graphRef as MutableRefObject<GraphVisualizationAPI>}
                 graphControls={graphControls}
                 className="min-h-80"
               />

cognee-frontend/src/ui/elements/Notebook/NotebookCellHeader.tsx

@@ -33,7 +33,7 @@ export default function NotebookCellHeader({
     setFalse: setIsNotRunningCell,
   } = useBoolean(false);
 
-  const [runInstance] = useState<string>(isCloudEnvironment() ? "cloud" : "local");
+  const [runInstance, setRunInstance] = useState<string>(isCloudEnvironment() ? "cloud" : "local");
 
   const handleCellRun = () => {
     if (runCell) {

cognee-frontend/tsconfig.json

@@ -14,7 +14,7 @@
     "moduleResolution": "bundler",
     "resolveJsonModule": true,
     "isolatedModules": true,
-    "jsx": "react-jsx",
+    "jsx": "preserve",
     "incremental": true,
     "plugins": [
       {
@@ -32,8 +32,7 @@
     "next-env.d.ts",
     "**/*.ts",
     "**/*.tsx",
-    ".next/types/**/*.ts",
-    ".next/dev/types/**/*.ts"
+    ".next/types/**/*.ts"
   ],
   "exclude": [
     "node_modules"

cognee-mcp/README.md

@@ -110,47 +110,6 @@ If you'd rather run cognee-mcp in a container, you have two options:
       # For stdio transport (default)
       docker run -e TRANSPORT_MODE=stdio --env-file ./.env --rm -it cognee/cognee-mcp:main
       ```
-      
-      **Installing optional dependencies at runtime:**
-      
-      You can install optional dependencies when running the container by setting the `EXTRAS` environment variable:
-      ```bash
-      # Install a single optional dependency group at runtime
-      docker run \
-        -e TRANSPORT_MODE=http \
-        -e EXTRAS=aws \
-        --env-file ./.env \
-        -p 8000:8000 \
-        --rm -it cognee/cognee-mcp:main
-      
-      # Install multiple optional dependency groups at runtime (comma-separated)
-      docker run \
-        -e TRANSPORT_MODE=sse \
-        -e EXTRAS=aws,postgres,neo4j \
-        --env-file ./.env \
-        -p 8000:8000 \
-        --rm -it cognee/cognee-mcp:main
-      ```
-      
-      **Available optional dependency groups:**
-      - `aws` - S3 storage support
-      - `postgres` / `postgres-binary` - PostgreSQL database support
-      - `neo4j` - Neo4j graph database support
-      - `neptune` - AWS Neptune support
-      - `chromadb` - ChromaDB vector store support
-      - `scraping` - Web scraping capabilities
-      - `distributed` - Modal distributed execution
-      - `langchain` - LangChain integration
-      - `llama-index` - LlamaIndex integration
-      - `anthropic` - Anthropic models
-      - `groq` - Groq models
-      - `mistral` - Mistral models
-      - `ollama` / `huggingface` - Local model support
-      - `docs` - Document processing
-      - `codegraph` - Code analysis
-      - `monitoring` - Sentry & Langfuse monitoring
-      - `redis` - Redis support
-      - And more (see [pyproject.toml](https://github.com/topoteretes/cognee/blob/main/pyproject.toml) for full list)
 2. **Pull from Docker Hub** (no build required):
    ```bash
    # With HTTP transport (recommended for web deployments)
@@ -160,17 +119,6 @@ If you'd rather run cognee-mcp in a container, you have two options:
    # With stdio transport (default)
    docker run -e TRANSPORT_MODE=stdio --env-file ./.env --rm -it cognee/cognee-mcp:main
    ```
-   
-   **With runtime installation of optional dependencies:**
-   ```bash
-   # Install optional dependencies from Docker Hub image
-   docker run \
-     -e TRANSPORT_MODE=http \
-     -e EXTRAS=aws,postgres \
-     --env-file ./.env \
-     -p 8000:8000 \
-     --rm -it cognee/cognee-mcp:main
-   ```
 
 ### **Important: Docker vs Direct Usage**
 **Docker uses environment variables**, not command line arguments:
@@ -445,22 +393,16 @@ The MCP server exposes its functionality through tools. Call them from any MCP c
 
 - **cognify**: Turns your data into a structured knowledge graph and stores it in memory
 
-- **cognee_add_developer_rules**: Ingest core developer rule files into memory
-
 - **codify**: Analyse a code repository, build a code graph, stores it in memory
 
-- **delete**: Delete specific data from a dataset (supports soft/hard deletion modes)
-
-- **get_developer_rules**: Retrieve all developer rules that were generated based on previous interactions
+- **search**: Query memory – supports GRAPH_COMPLETION, RAG_COMPLETION, CODE, CHUNKS
 
 - **list_data**: List all datasets and their data items with IDs for deletion operations
 
-- **save_interaction**: Logs user-agent interactions and query-answer pairs
+- **delete**: Delete specific data from a dataset (supports soft/hard deletion modes)
 
 - **prune**: Reset cognee for a fresh start (removes all data)
 
-- **search**: Query memory – supports GRAPH_COMPLETION, RAG_COMPLETION, CODE, CHUNKS, SUMMARIES, CYPHER, and FEELING_LUCKY
-
 - **cognify_status / codify_status**: Track pipeline progress
 
 **Data Management Examples:**

cognee-mcp/entrypoint.sh

@@ -4,42 +4,6 @@ set -e  # Exit on error
 echo "Debug mode: $DEBUG"
 echo "Environment: $ENVIRONMENT"
 
-# Install optional dependencies if EXTRAS is set
-if [ -n "$EXTRAS" ]; then
-    echo "Installing optional dependencies: $EXTRAS"
-    
-    # Get the cognee version that's currently installed
-    COGNEE_VERSION=$(uv pip show cognee | grep "Version:" | awk '{print $2}')
-    echo "Current cognee version: $COGNEE_VERSION"
-    
-    # Build the extras list for cognee
-    IFS=',' read -ra EXTRA_ARRAY <<< "$EXTRAS"
-    # Combine base extras from pyproject.toml with requested extras
-    ALL_EXTRAS=""
-    for extra in "${EXTRA_ARRAY[@]}"; do
-        # Trim whitespace
-        extra=$(echo "$extra" | xargs)
-        # Add to extras list if not already present
-        if [[ ! "$ALL_EXTRAS" =~ (^|,)"$extra"(,|$) ]]; then
-            if [ -z "$ALL_EXTRAS" ]; then
-                ALL_EXTRAS="$extra"
-            else
-                ALL_EXTRAS="$ALL_EXTRAS,$extra"
-            fi
-        fi
-    done
-    
-    echo "Installing cognee with extras: $ALL_EXTRAS"
-    echo "Running: uv pip install 'cognee[$ALL_EXTRAS]==$COGNEE_VERSION'"
-    uv pip install "cognee[$ALL_EXTRAS]==$COGNEE_VERSION"
-    
-    # Verify installation
-    echo ""
-    echo "✓ Optional dependencies installation completed"
-else
-    echo "No optional dependencies specified"
-fi
-
 # Set default transport mode if not specified
 TRANSPORT_MODE=${TRANSPORT_MODE:-"stdio"}
 echo "Transport mode: $TRANSPORT_MODE"

cognee-mcp/pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "cognee-mcp"
-version = "0.5.0"
+version = "0.4.0"
 description = "Cognee MCP server"
 readme = "README.md"
 requires-python = ">=3.10"
@@ -9,11 +9,12 @@ dependencies = [
     # For local cognee repo usage remove comment bellow and add absolute path to cognee. Then run `uv sync --reinstall` in the mcp folder on local cognee changes.
     #"cognee[postgres,codegraph,gemini,huggingface,docs,neo4j] @ file:/Users/igorilic/Desktop/cognee",
     # TODO: Remove gemini from optional dependecnies for new Cognee version after 0.3.4
-    "cognee[postgres,docs,neo4j]==0.5.0",
+    "cognee[postgres,codegraph,huggingface,docs,neo4j]==0.3.7",
     "fastmcp>=2.10.0,<3.0.0",
     "mcp>=1.12.0,<2.0.0",
     "uv>=0.6.3,<1.0.0",
     "httpx>=0.27.0,<1.0.0",
+    "pydantic>=2.12.1",
 ]
 
 authors = [
@@ -32,6 +33,8 @@ packages = ["src"]
 [dependency-groups]
 dev = [
     "debugpy>=1.8.12,<2.0.0",
+    "mypy>=1.18.2",
+    "ruff>=0.14.2",
 ]
 
 [tool.hatch.metadata]

cognee-mcp/src/__init__.py

@@ -1,7 +1,4 @@
-try:
-    from .server import main as server_main
-except ImportError:
-    from server import main as server_main
+from .server import main as server_main
 import warnings
 import sys
 

cognee-mcp/src/client.py

@@ -37,10 +37,12 @@ async def run():
 
             toolResult = await session.call_tool("prune", arguments={})
 
-            toolResult = await session.call_tool("cognify", arguments={})
+            toolResult = await session.call_tool(
+                "codify", arguments={"repo_path": "SOME_REPO_PATH"}
+            )
 
             toolResult = await session.call_tool(
-                "search", arguments={"search_type": "GRAPH_COMPLETION"}
+                "search", arguments={"search_type": "CODE", "search_query": "exceptions"}
             )
 
             print(f"Cognify result: {toolResult.content}")

cognee-mcp/src/clients/__init__.py

@@ -0,0 +1,3 @@
+from .cognee_client import CogneeClient
+
+__all__ = ["CogneeClient"]

cognee-mcp/src/clients/cognee_client.py

@@ -151,7 +151,7 @@ class CogneeClient:
         query_type: str,
         datasets: Optional[List[str]] = None,
         system_prompt: Optional[str] = None,
-        top_k: int = 5,
+        top_k: int = 10,
     ) -> Any:
         """
         Search the knowledge graph.
@@ -192,7 +192,7 @@ class CogneeClient:
 
             with redirect_stdout(sys.stderr):
                 results = await self.cognee.search(
-                    query_type=SearchType[query_type.upper()], query_text=query_text, top_k=top_k
+                    query_type=SearchType[query_type.upper()], query_text=query_text
                 )
                 return results
 

cognee-mcp/src/models/MCPServerCommandConfig.py

@@ -0,0 +1,7 @@
+from typing import List
+from pydantic import BaseModel
+
+
+class MCPServerCommandConfig(BaseModel):
+    command: str
+    args: List[str]

cognee-mcp/src/models/MCPServerUrlConfig.py

@@ -0,0 +1,7 @@
+from typing import Literal
+from pydantic import BaseModel
+
+
+class MCPServerUrlConfig(BaseModel):
+    url: str
+    transport: Literal["http", "sse"]

cognee-mcp/src/models/__init__.py

@@ -0,0 +1,7 @@
+from .MCPServerCommandConfig import MCPServerCommandConfig
+from .MCPServerUrlConfig import MCPServerUrlConfig
+
+__all__ = [
+    "MCPServerCommandConfig",
+    "MCPServerUrlConfig",
+]

cognee-mcp/src/server.py

@@ -1,46 +1,41 @@
-import json
-import os
+"""
+MCP Registry Server
+
+A personal registry for discovering and remembering MCP servers.
+
+Available Tools:
+- remember_mcp_server: Store MCP server information
+- find_mcp_server: Search for servers by requirements
+- list_mcp_servers: View all stored servers
+- clear_registry: Clear the registry
+"""
+
 import sys
 import argparse
 import asyncio
 import subprocess
 from pathlib import Path
-from typing import Optional
+from typing import Any
 
-from cognee.shared.logging_utils import get_logger, setup_logging, get_log_file_location
-import importlib.util
-from contextlib import redirect_stdout
-import mcp.types as types
+from cognee.shared.logging_utils import get_logger, setup_logging
 from mcp.server import FastMCP
-from cognee.modules.storage.utils import JSONEncoder
 from starlette.responses import JSONResponse
-from starlette.middleware import Middleware
 from starlette.middleware.cors import CORSMiddleware
 import uvicorn
+from fastmcp import Client as MCPClient
 
-try:
-    from .cognee_client import CogneeClient
-except ImportError:
-    from cognee_client import CogneeClient
+from src.utils.context import set_cognee_client
+from src.clients import CogneeClient
+from src.tools import clear_registry, find_mcp_server, list_mcp_servers, remember_mcp_server
 
-
-try:
-    from cognee.tasks.codingagents.coding_rule_associations import (
-        add_rule_associations,
-        get_existing_rules,
-    )
-except ModuleNotFoundError:
-    from .codingagents.coding_rule_associations import (
-        add_rule_associations,
-        get_existing_rules,
-    )
-
-
-mcp = FastMCP("Cognee")
+mcp = FastMCP[Any]("MCP-Registry")
 
 logger = get_logger()
 
-cognee_client: Optional[CogneeClient] = None
+mcp.tool()(clear_registry)
+mcp.tool()(find_mcp_server)
+mcp.tool()(list_mcp_servers)
+mcp.tool()(remember_mcp_server)
 
 
 async def run_sse_with_cors():
@@ -90,726 +85,6 @@ async def health_check(request):
     return JSONResponse({"status": "ok"})
 
 
-@mcp.tool()
-async def cognify(
-    data: str, graph_model_file: str = None, graph_model_name: str = None, custom_prompt: str = None
-) -> list:
-    """
-    Transform ingested data into a structured knowledge graph.
-
-    This is the core processing step in Cognee that converts raw text and documents
-    into an intelligent knowledge graph. It analyzes content, extracts entities and
-    relationships, and creates semantic connections for enhanced search and reasoning.
-
-    Prerequisites:
-        - **LLM_API_KEY**: Must be configured (required for entity extraction and graph generation)
-        - **Vector Database**: Must be accessible for embeddings storage
-        - **Graph Database**: Must be accessible for relationship storage
-
-    Input Requirements:
-        - **Content Types**: Works with any text-extractable content including:
-            * Natural language documents
-            * Structured data (CSV, JSON)
-            * Code repositories
-            * Academic papers and technical documentation
-            * Mixed multimedia content (with text extraction)
-
-    Processing Pipeline:
-        1. **Document Classification**: Identifies document types and structures
-        2. **Permission Validation**: Ensures user has processing rights
-        3. **Text Chunking**: Breaks content into semantically meaningful segments
-        4. **Entity Extraction**: Identifies key concepts, people, places, organizations
-        5. **Relationship Detection**: Discovers connections between entities
-        6. **Graph Construction**: Builds semantic knowledge graph with embeddings
-        7. **Content Summarization**: Creates hierarchical summaries for navigation
-
-    Parameters
-    ----------
-    data : str
-        The data to be processed and transformed into structured knowledge.
-        This can include natural language, file location, or any text-based information
-        that should become part of the agent's memory.
-
-    graph_model_file : str, optional
-        Path to a custom schema file that defines the structure of the generated knowledge graph.
-        If provided, this file will be loaded using importlib to create a custom graph model.
-        Default is None, which uses Cognee's built-in KnowledgeGraph model.
-
-    graph_model_name : str, optional
-        Name of the class within the graph_model_file to instantiate as the graph model.
-        Required if graph_model_file is specified.
-        Default is None, which uses the default KnowledgeGraph class.
-
-    custom_prompt : str, optional
-        Custom prompt string to use for entity extraction and graph generation.
-        If provided, this prompt will be used instead of the default prompts for
-        knowledge graph extraction. The prompt should guide the LLM on how to
-        extract entities and relationships from the text content.
-
-    Returns
-    -------
-    list
-        A list containing a single TextContent object with information about the
-        background task launch and how to check its status.
-
-    Next Steps:
-        After successful cognify processing, use search functions to query the knowledge:
-
-        ```python
-        import cognee
-        from cognee import SearchType
-
-        # Process your data into knowledge graph
-        await cognee.cognify()
-
-        # Query for insights using different search types:
-
-        # 1. Natural language completion with graph context
-        insights = await cognee.search(
-            "What are the main themes?",
-            query_type=SearchType.GRAPH_COMPLETION
-        )
-
-        # 2. Get entity relationships and connections
-        relationships = await cognee.search(
-            "connections between concepts",
-            query_type=SearchType.GRAPH_COMPLETION
-        )
-
-        # 3. Find relevant document chunks
-        chunks = await cognee.search(
-            "specific topic",
-            query_type=SearchType.CHUNKS
-        )
-        ```
-
-    Environment Variables:
-        Required:
-        - LLM_API_KEY: API key for your LLM provider
-
-        Optional:
-        - LLM_PROVIDER, LLM_MODEL, VECTOR_DB_PROVIDER, GRAPH_DATABASE_PROVIDER
-        - LLM_RATE_LIMIT_ENABLED: Enable rate limiting (default: False)
-        - LLM_RATE_LIMIT_REQUESTS: Max requests per interval (default: 60)
-
-    Notes
-    -----
-    - The function launches a background task and returns immediately
-    - The actual cognify process may take significant time depending on text length
-    - Use the cognify_status tool to check the progress of the operation
-
-    """
-
-    async def cognify_task(
-        data: str,
-        graph_model_file: str = None,
-        graph_model_name: str = None,
-        custom_prompt: str = None,
-    ) -> str:
-        """Build knowledge graph from the input text"""
-        # NOTE: MCP uses stdout to communicate, we must redirect all output
-        #       going to stdout ( like the print function ) to stderr.
-        with redirect_stdout(sys.stderr):
-            logger.info("Cognify process starting.")
-
-            graph_model = None
-            if graph_model_file and graph_model_name:
-                if cognee_client.use_api:
-                    logger.warning("Custom graph models are not supported in API mode, ignoring.")
-                else:
-                    from cognee.shared.data_models import KnowledgeGraph
-
-                    graph_model = load_class(graph_model_file, graph_model_name)
-
-            await cognee_client.add(data)
-
-            try:
-                await cognee_client.cognify(custom_prompt=custom_prompt, graph_model=graph_model)
-                logger.info("Cognify process finished.")
-            except Exception as e:
-                logger.error("Cognify process failed.")
-                raise ValueError(f"Failed to cognify: {str(e)}")
-
-    asyncio.create_task(
-        cognify_task(
-            data=data,
-            graph_model_file=graph_model_file,
-            graph_model_name=graph_model_name,
-            custom_prompt=custom_prompt,
-        )
-    )
-
-    log_file = get_log_file_location()
-    text = (
-        f"Background process launched due to MCP timeout limitations.\n"
-        f"To check current cognify status use the cognify_status tool\n"
-        f"or check the log file at: {log_file}"
-    )
-
-    return [
-        types.TextContent(
-            type="text",
-            text=text,
-        )
-    ]
-
-
-@mcp.tool(
-    name="save_interaction", description="Logs user-agent interactions and query-answer pairs"
-)
-async def save_interaction(data: str) -> list:
-    """
-    Transform and save a user-agent interaction into structured knowledge.
-
-    Parameters
-    ----------
-    data : str
-        The input string containing user queries and corresponding agent answers.
-
-    Returns
-    -------
-    list
-        A list containing a single TextContent object with information about the background task launch.
-    """
-
-    async def save_user_agent_interaction(data: str) -> None:
-        """Build knowledge graph from the interaction data"""
-        with redirect_stdout(sys.stderr):
-            logger.info("Save interaction process starting.")
-
-            await cognee_client.add(data, node_set=["user_agent_interaction"])
-
-            try:
-                await cognee_client.cognify()
-                logger.info("Save interaction process finished.")
-
-                # Rule associations only work in direct mode
-                if not cognee_client.use_api:
-                    logger.info("Generating associated rules from interaction data.")
-                    await add_rule_associations(data=data, rules_nodeset_name="coding_agent_rules")
-                    logger.info("Associated rules generated from interaction data.")
-                else:
-                    logger.warning("Rule associations are not available in API mode, skipping.")
-
-            except Exception as e:
-                logger.error("Save interaction process failed.")
-                raise ValueError(f"Failed to Save interaction: {str(e)}")
-
-    asyncio.create_task(
-        save_user_agent_interaction(
-            data=data,
-        )
-    )
-
-    log_file = get_log_file_location()
-    text = (
-        f"Background process launched to process the user-agent interaction.\n"
-        f"To check the current status, use the cognify_status tool or check the log file at: {log_file}"
-    )
-
-    return [
-        types.TextContent(
-            type="text",
-            text=text,
-        )
-    ]
-
-
-@mcp.tool()
-async def search(search_query: str, search_type: str, top_k: int = 10) -> list:
-    """
-    Search and query the knowledge graph for insights, information, and connections.
-
-    This is the final step in the Cognee workflow that retrieves information from the
-    processed knowledge graph. It supports multiple search modes optimized for different
-    use cases - from simple fact retrieval to complex reasoning and code analysis.
-
-    Search Prerequisites:
-        - **LLM_API_KEY**: Required for GRAPH_COMPLETION and RAG_COMPLETION search types
-        - **Data Added**: Must have data previously added via `cognee.add()`
-        - **Knowledge Graph Built**: Must have processed data via `cognee.cognify()`
-        - **Vector Database**: Must be accessible for semantic search functionality
-
-    Search Types & Use Cases:
-
-        **GRAPH_COMPLETION** (Recommended):
-            Natural language Q&A using full graph context and LLM reasoning.
-            Best for: Complex questions, analysis, summaries, insights.
-            Returns: Conversational AI responses with graph-backed context.
-
-        **RAG_COMPLETION**:
-            Traditional RAG using document chunks without graph structure.
-            Best for: Direct document retrieval, specific fact-finding.
-            Returns: LLM responses based on relevant text chunks.
-
-        **CHUNKS**:
-            Raw text segments that match the query semantically.
-            Best for: Finding specific passages, citations, exact content.
-            Returns: Ranked list of relevant text chunks with metadata.
-
-        **SUMMARIES**:
-            Pre-generated hierarchical summaries of content.
-            Best for: Quick overviews, document abstracts, topic summaries.
-            Returns: Multi-level summaries from detailed to high-level.
-
-        **CODE**:
-            Code-specific search with syntax and semantic understanding.
-            Best for: Finding functions, classes, implementation patterns.
-            Returns: Structured code information with context and relationships.
-
-        **CYPHER**:
-            Direct graph database queries using Cypher syntax.
-            Best for: Advanced users, specific graph traversals, debugging.
-            Returns: Raw graph query results.
-
-        **FEELING_LUCKY**:
-            Intelligently selects and runs the most appropriate search type.
-            Best for: General-purpose queries or when you're unsure which search type is best.
-            Returns: The results from the automatically selected search type.
-
-    Parameters
-    ----------
-    search_query : str
-        Your question or search query in natural language.
-        Examples:
-        - "What are the main themes in this research?"
-        - "How do these concepts relate to each other?"
-        - "Find information about machine learning algorithms"
-        - "What functions handle user authentication?"
-
-    search_type : str
-        The type of search to perform. Valid options include:
-        - "GRAPH_COMPLETION": Returns an LLM response based on the search query and Cognee's memory
-        - "RAG_COMPLETION": Returns an LLM response based on the search query and standard RAG data
-        - "CODE": Returns code-related knowledge in JSON format
-        - "CHUNKS": Returns raw text chunks from the knowledge graph
-        - "SUMMARIES": Returns pre-generated hierarchical summaries
-        - "CYPHER": Direct graph database queries
-        - "FEELING_LUCKY": Automatically selects best search type
-
-        The search_type is case-insensitive and will be converted to uppercase.
-
-    top_k : int, optional
-        Maximum number of results to return (default: 10).
-        Controls the amount of context retrieved from the knowledge graph.
-        - Lower values (3-5): Faster, more focused results
-        - Higher values (10-20): More comprehensive, but slower and more context-heavy
-        Helps manage response size and context window usage in MCP clients.
-
-    Returns
-    -------
-    list
-        A list containing a single TextContent object with the search results.
-        The format of the result depends on the search_type:
-        - **GRAPH_COMPLETION/RAG_COMPLETION**: Conversational AI response strings
-        - **CHUNKS**: Relevant text passages with source metadata
-        - **SUMMARIES**: Hierarchical summaries from general to specific
-        - **CODE**: Structured code information with context
-        - **FEELING_LUCKY**: Results in format of automatically selected search type
-        - **CYPHER**: Raw graph query results
-
-    Performance & Optimization:
-        - **GRAPH_COMPLETION**: Slower but most intelligent, uses LLM + graph context
-        - **RAG_COMPLETION**: Medium speed, uses LLM + document chunks (no graph traversal)
-        - **CHUNKS**: Fastest, pure vector similarity search without LLM
-        - **SUMMARIES**: Fast, returns pre-computed summaries
-        - **CODE**: Medium speed, specialized for code understanding
-        - **FEELING_LUCKY**: Variable speed, uses LLM + search type selection intelligently
-
-    Environment Variables:
-        Required for LLM-based search types (GRAPH_COMPLETION, RAG_COMPLETION):
-        - LLM_API_KEY: API key for your LLM provider
-
-        Optional:
-        - LLM_PROVIDER, LLM_MODEL: Configure LLM for search responses
-        - VECTOR_DB_PROVIDER: Must match what was used during cognify
-        - GRAPH_DATABASE_PROVIDER: Must match what was used during cognify
-
-    Notes
-    -----
-    - Different search types produce different output formats
-    - The function handles the conversion between Cognee's internal result format and MCP's output format
-
-    """
-
-    async def search_task(search_query: str, search_type: str, top_k: int) -> str:
-        """
-        Internal task to execute knowledge graph search with result formatting.
-
-        Handles the actual search execution and formats results appropriately
-        for MCP clients based on the search type and execution mode (API vs direct).
-
-        Parameters
-        ----------
-        search_query : str
-            The search query in natural language
-        search_type : str
-            Type of search to perform (GRAPH_COMPLETION, CHUNKS, etc.)
-        top_k : int
-            Maximum number of results to return
-
-        Returns
-        -------
-        str
-            Formatted search results as a string, with format depending on search_type
-        """
-        # NOTE: MCP uses stdout to communicate, we must redirect all output
-        #       going to stdout ( like the print function ) to stderr.
-        with redirect_stdout(sys.stderr):
-            search_results = await cognee_client.search(
-                query_text=search_query, query_type=search_type, top_k=top_k
-            )
-
-            # Handle different result formats based on API vs direct mode
-            if cognee_client.use_api:
-                # API mode returns JSON-serialized results
-                if isinstance(search_results, str):
-                    return search_results
-                elif isinstance(search_results, list):
-                    if (
-                        search_type.upper() in ["GRAPH_COMPLETION", "RAG_COMPLETION"]
-                        and len(search_results) > 0
-                    ):
-                        return str(search_results[0])
-                    return str(search_results)
-                else:
-                    return json.dumps(search_results, cls=JSONEncoder)
-            else:
-                # Direct mode processing
-                if search_type.upper() == "CODE":
-                    return json.dumps(search_results, cls=JSONEncoder)
-                elif (
-                    search_type.upper() == "GRAPH_COMPLETION"
-                    or search_type.upper() == "RAG_COMPLETION"
-                ):
-                    return str(search_results[0])
-                elif search_type.upper() == "CHUNKS":
-                    return str(search_results)
-                elif search_type.upper() == "INSIGHTS":
-                    results = retrieved_edges_to_string(search_results)
-                    return results
-                else:
-                    return str(search_results)
-
-    search_results = await search_task(search_query, search_type, top_k)
-    return [types.TextContent(type="text", text=search_results)]
-
-
-@mcp.tool()
-async def list_data(dataset_id: str = None) -> list:
-    """
-    List all datasets and their data items with IDs for deletion operations.
-
-    This function helps users identify data IDs and dataset IDs that can be used
-    with the delete tool. It provides a comprehensive view of available data.
-
-    Parameters
-    ----------
-    dataset_id : str, optional
-        If provided, only list data items from this specific dataset.
-        If None, lists all datasets and their data items.
-        Should be a valid UUID string.
-
-    Returns
-    -------
-    list
-        A list containing a single TextContent object with formatted information
-        about datasets and data items, including their IDs for deletion.
-
-    Notes
-    -----
-    - Use this tool to identify data_id and dataset_id values for the delete tool
-    - The output includes both dataset information and individual data items
-    - UUIDs are displayed in a format ready for use with other tools
-    """
-    from uuid import UUID
-
-    with redirect_stdout(sys.stderr):
-        try:
-            output_lines = []
-
-            if dataset_id:
-                # Detailed data listing for specific dataset is only available in direct mode
-                if cognee_client.use_api:
-                    return [
-                        types.TextContent(
-                            type="text",
-                            text="❌ Detailed data listing for specific datasets is not available in API mode.\nPlease use the API directly or use direct mode.",
-                        )
-                    ]
-
-                from cognee.modules.users.methods import get_default_user
-                from cognee.modules.data.methods import get_dataset, get_dataset_data
-
-                logger.info(f"Listing data for dataset: {dataset_id}")
-                dataset_uuid = UUID(dataset_id)
-                user = await get_default_user()
-
-                dataset = await get_dataset(user.id, dataset_uuid)
-
-                if not dataset:
-                    return [
-                        types.TextContent(type="text", text=f"❌ Dataset not found: {dataset_id}")
-                    ]
-
-                # Get data items in the dataset
-                data_items = await get_dataset_data(dataset.id)
-
-                output_lines.append(f"📁 Dataset: {dataset.name}")
-                output_lines.append(f"   ID: {dataset.id}")
-                output_lines.append(f"   Created: {dataset.created_at}")
-                output_lines.append(f"   Data items: {len(data_items)}")
-                output_lines.append("")
-
-                if data_items:
-                    for i, data_item in enumerate(data_items, 1):
-                        output_lines.append(f"   📄 Data item #{i}:")
-                        output_lines.append(f"      Data ID: {data_item.id}")
-                        output_lines.append(f"      Name: {data_item.name or 'Unnamed'}")
-                        output_lines.append(f"      Created: {data_item.created_at}")
-                        output_lines.append("")
-                else:
-                    output_lines.append("   (No data items in this dataset)")
-
-            else:
-                # List all datasets - works in both modes
-                logger.info("Listing all datasets")
-                datasets = await cognee_client.list_datasets()
-
-                if not datasets:
-                    return [
-                        types.TextContent(
-                            type="text",
-                            text="📂 No datasets found.\nUse the cognify tool to create your first dataset!",
-                        )
-                    ]
-
-                output_lines.append("📂 Available Datasets:")
-                output_lines.append("=" * 50)
-                output_lines.append("")
-
-                for i, dataset in enumerate(datasets, 1):
-                    # In API mode, dataset is a dict; in direct mode, it's formatted as dict
-                    if isinstance(dataset, dict):
-                        output_lines.append(f"{i}. 📁 {dataset.get('name', 'Unnamed')}")
-                        output_lines.append(f"   Dataset ID: {dataset.get('id')}")
-                        output_lines.append(f"   Created: {dataset.get('created_at', 'N/A')}")
-                    else:
-                        output_lines.append(f"{i}. 📁 {dataset.name}")
-                        output_lines.append(f"   Dataset ID: {dataset.id}")
-                        output_lines.append(f"   Created: {dataset.created_at}")
-                    output_lines.append("")
-
-                if not cognee_client.use_api:
-                    output_lines.append("💡 To see data items in a specific dataset, use:")
-                    output_lines.append('   list_data(dataset_id="your-dataset-id-here")')
-                    output_lines.append("")
-                output_lines.append("🗑️  To delete specific data, use:")
-                output_lines.append('   delete(data_id="data-id", dataset_id="dataset-id")')
-
-            result_text = "\n".join(output_lines)
-            logger.info("List data operation completed successfully")
-
-            return [types.TextContent(type="text", text=result_text)]
-
-        except ValueError as e:
-            error_msg = f"❌ Invalid UUID format: {str(e)}"
-            logger.error(error_msg)
-            return [types.TextContent(type="text", text=error_msg)]
-
-        except Exception as e:
-            error_msg = f"❌ Failed to list data: {str(e)}"
-            logger.error(f"List data error: {str(e)}")
-            return [types.TextContent(type="text", text=error_msg)]
-
-
-@mcp.tool()
-async def delete(data_id: str, dataset_id: str, mode: str = "soft") -> list:
-    """
-    Delete specific data from a dataset in the Cognee knowledge graph.
-
-    This function removes a specific data item from a dataset while keeping the
-    dataset itself intact. It supports both soft and hard deletion modes.
-
-    Parameters
-    ----------
-    data_id : str
-        The UUID of the data item to delete from the knowledge graph.
-        This should be a valid UUID string identifying the specific data item.
-
-    dataset_id : str
-        The UUID of the dataset containing the data to be deleted.
-        This should be a valid UUID string identifying the dataset.
-
-    mode : str, optional
-        The deletion mode to use. Options are:
-        - "soft" (default): Removes the data but keeps related entities that might be shared
-        - "hard": Also removes degree-one entity nodes that become orphaned after deletion
-        Default is "soft" for safer deletion that preserves shared knowledge.
-
-    Returns
-    -------
-    list
-        A list containing a single TextContent object with the deletion results,
-        including status, deleted node counts, and confirmation details.
-
-    Notes
-    -----
-    - This operation cannot be undone. The specified data will be permanently removed.
-    - Hard mode may remove additional entity nodes that become orphaned
-    - The function provides detailed feedback about what was deleted
-    - Use this for targeted deletion instead of the prune tool which removes everything
-    """
-    from uuid import UUID
-
-    with redirect_stdout(sys.stderr):
-        try:
-            logger.info(
-                f"Starting delete operation for data_id: {data_id}, dataset_id: {dataset_id}, mode: {mode}"
-            )
-
-            # Convert string UUIDs to UUID objects
-            data_uuid = UUID(data_id)
-            dataset_uuid = UUID(dataset_id)
-
-            # Call the cognee delete function via client
-            result = await cognee_client.delete(
-                data_id=data_uuid, dataset_id=dataset_uuid, mode=mode
-            )
-
-            logger.info(f"Delete operation completed successfully: {result}")
-
-            # Format the result for MCP response
-            formatted_result = json.dumps(result, indent=2, cls=JSONEncoder)
-
-            return [
-                types.TextContent(
-                    type="text",
-                    text=f"✅ Delete operation completed successfully!\n\n{formatted_result}",
-                )
-            ]
-
-        except ValueError as e:
-            # Handle UUID parsing errors
-            error_msg = f"❌ Invalid UUID format: {str(e)}"
-            logger.error(error_msg)
-            return [types.TextContent(type="text", text=error_msg)]
-
-        except Exception as e:
-            # Handle all other errors (DocumentNotFoundError, DatasetNotFoundError, etc.)
-            error_msg = f"❌ Delete operation failed: {str(e)}"
-            logger.error(f"Delete operation error: {str(e)}")
-            return [types.TextContent(type="text", text=error_msg)]
-
-
-@mcp.tool()
-async def prune():
-    """
-    Reset the Cognee knowledge graph by removing all stored information.
-
-    This function performs a complete reset of both the data layer and system layer
-    of the Cognee knowledge graph, removing all nodes, edges, and associated metadata.
-    It is typically used during development or when needing to start fresh with a new
-    knowledge base.
-
-    Returns
-    -------
-    list
-        A list containing a single TextContent object with confirmation of the prune operation.
-
-    Notes
-    -----
-    - This operation cannot be undone. All memory data will be permanently deleted.
-    - The function prunes both data content (using prune_data) and system metadata (using prune_system)
-    - This operation is not available in API mode
-    """
-    with redirect_stdout(sys.stderr):
-        try:
-            await cognee_client.prune_data()
-            await cognee_client.prune_system(metadata=True)
-            return [types.TextContent(type="text", text="Pruned")]
-        except NotImplementedError:
-            error_msg = "❌ Prune operation is not available in API mode"
-            logger.error(error_msg)
-            return [types.TextContent(type="text", text=error_msg)]
-        except Exception as e:
-            error_msg = f"❌ Prune operation failed: {str(e)}"
-            logger.error(error_msg)
-            return [types.TextContent(type="text", text=error_msg)]
-
-
-@mcp.tool()
-async def cognify_status():
-    """
-    Get the current status of the cognify pipeline.
-
-    This function retrieves information about current and recently completed cognify operations
-    in the main_dataset. It provides details on progress, success/failure status, and statistics
-    about the processed data.
-
-    Returns
-    -------
-    list
-        A list containing a single TextContent object with the status information as a string.
-        The status includes information about active and completed jobs for the cognify_pipeline.
-
-    Notes
-    -----
-    - The function retrieves pipeline status specifically for the "cognify_pipeline" on the "main_dataset"
-    - Status information includes job progress, execution time, and completion status
-    - The status is returned in string format for easy reading
-    - This operation is not available in API mode
-    """
-    with redirect_stdout(sys.stderr):
-        try:
-            from cognee.modules.data.methods.get_unique_dataset_id import get_unique_dataset_id
-            from cognee.modules.users.methods import get_default_user
-
-            user = await get_default_user()
-            status = await cognee_client.get_pipeline_status(
-                [await get_unique_dataset_id("main_dataset", user)], "cognify_pipeline"
-            )
-            return [types.TextContent(type="text", text=str(status))]
-        except NotImplementedError:
-            error_msg = "❌ Pipeline status is not available in API mode"
-            logger.error(error_msg)
-            return [types.TextContent(type="text", text=error_msg)]
-        except Exception as e:
-            error_msg = f"❌ Failed to get cognify status: {str(e)}"
-            logger.error(error_msg)
-            return [types.TextContent(type="text", text=error_msg)]
-
-
-def node_to_string(node):
-    node_data = ", ".join(
-        [f'{key}: "{value}"' for key, value in node.items() if key in ["id", "name"]]
-    )
-
-    return f"Node({node_data})"
-
-
-def retrieved_edges_to_string(search_results):
-    edge_strings = []
-    for triplet in search_results:
-        node1, edge, node2 = triplet
-        relationship_type = edge["relationship_name"]
-        edge_str = f"{node_to_string(node1)} {relationship_type} {node_to_string(node2)}"
-        edge_strings.append(edge_str)
-
-    return "\n".join(edge_strings)
-
-
-def load_class(model_file, model_name):
-    model_file = os.path.abspath(model_file)
-    spec = importlib.util.spec_from_file_location("graph_model", model_file)
-    module = importlib.util.module_from_spec(spec)
-    spec.loader.exec_module(module)
-
-    model_class = getattr(module, model_name)
-
-    return model_class
-
-
 async def main():
     global cognee_client
 
@@ -874,16 +149,13 @@ async def main():
 
     # Initialize the global CogneeClient
     cognee_client = CogneeClient(api_url=args.api_url, api_token=args.api_token)
+    set_cognee_client(cognee_client)
 
     mcp.settings.host = args.host
     mcp.settings.port = args.port
 
     # Skip migrations when in API mode (the API server handles its own database)
     if not args.no_migration and not args.api_url:
-        from cognee.modules.engine.operations.setup import setup
-
-        await setup()
-
         # Run Alembic migrations from the main cognee directory where alembic.ini is located
         logger.info("Running database migrations...")
         migration_result = subprocess.run(
@@ -909,7 +181,7 @@ async def main():
     elif args.api_url:
         logger.info("Skipping database migrations (using API mode)")
 
-    logger.info(f"Starting MCP server with transport: {args.transport}")
+    logger.info(f"Starting MCP Registry server with transport: {args.transport}")
     if args.transport == "stdio":
         await mcp.run_stdio_async()
     elif args.transport == "sse":
@@ -928,5 +200,5 @@ if __name__ == "__main__":
     try:
         asyncio.run(main())
     except Exception as e:
-        logger.error(f"Error initializing Cognee MCP server: {str(e)}")
+        logger.error(f"Error initializing MCP Registry server: {str(e)}")
         raise

cognee-mcp/src/test_client.py

@@ -3,7 +3,7 @@
 Test client for Cognee MCP Server functionality.
 
 This script tests all the tools and functions available in the Cognee MCP server,
-including cognify, search, prune, status checks, and utility functions.
+including cognify, codify, search, prune, status checks, and utility functions.
 
 Usage:
     # Set your OpenAI API key first
@@ -23,7 +23,6 @@ import tempfile
 import time
 from contextlib import asynccontextmanager
 from cognee.shared.logging_utils import setup_logging
-from logging import ERROR, INFO
 
 from mcp import ClientSession, StdioServerParameters
 from mcp.client.stdio import stdio_client
@@ -36,7 +35,7 @@ from src.server import (
     load_class,
 )
 
-# Set timeout for cognify to complete in
+# Set timeout for cognify/codify to complete in
 TIMEOUT = 5 * 60  # 5 min  in seconds
 
 
@@ -152,9 +151,12 @@ DEBUG = True
 
                 expected_tools = {
                     "cognify",
+                    "codify",
                     "search",
                     "prune",
                     "cognify_status",
+                    "codify_status",
+                    "cognee_add_developer_rules",
                     "list_data",
                     "delete",
                 }
@@ -245,6 +247,106 @@ DEBUG = True
             }
             print(f"❌ {test_name} test failed: {e}")
 
+    async def test_codify(self):
+        """Test the codify functionality using MCP client."""
+        print("\n🧪 Testing codify functionality...")
+        try:
+            async with self.mcp_server_session() as session:
+                codify_result = await session.call_tool(
+                    "codify", arguments={"repo_path": self.test_repo_dir}
+                )
+
+                start = time.time()  # mark the start
+                while True:
+                    try:
+                        # Wait a moment
+                        await asyncio.sleep(5)
+
+                        # Check if codify processing is finished
+                        status_result = await session.call_tool("codify_status", arguments={})
+                        if hasattr(status_result, "content") and status_result.content:
+                            status_text = (
+                                status_result.content[0].text
+                                if status_result.content
+                                else str(status_result)
+                            )
+                        else:
+                            status_text = str(status_result)
+
+                        if str(PipelineRunStatus.DATASET_PROCESSING_COMPLETED) in status_text:
+                            break
+                        elif time.time() - start > TIMEOUT:
+                            raise TimeoutError("Codify did not complete in 5min")
+                    except DatabaseNotCreatedError:
+                        if time.time() - start > TIMEOUT:
+                            raise TimeoutError("Database was not created in 5min")
+
+                self.test_results["codify"] = {
+                    "status": "PASS",
+                    "result": codify_result,
+                    "message": "Codify executed successfully",
+                }
+                print("✅ Codify test passed")
+
+        except Exception as e:
+            self.test_results["codify"] = {
+                "status": "FAIL",
+                "error": str(e),
+                "message": "Codify test failed",
+            }
+            print(f"❌ Codify test failed: {e}")
+
+    async def test_cognee_add_developer_rules(self):
+        """Test the cognee_add_developer_rules functionality using MCP client."""
+        print("\n🧪 Testing cognee_add_developer_rules functionality...")
+        try:
+            async with self.mcp_server_session() as session:
+                result = await session.call_tool(
+                    "cognee_add_developer_rules", arguments={"base_path": self.test_data_dir}
+                )
+
+                start = time.time()  # mark the start
+                while True:
+                    try:
+                        # Wait a moment
+                        await asyncio.sleep(5)
+
+                        # Check if developer rule cognify processing is finished
+                        status_result = await session.call_tool("cognify_status", arguments={})
+                        if hasattr(status_result, "content") and status_result.content:
+                            status_text = (
+                                status_result.content[0].text
+                                if status_result.content
+                                else str(status_result)
+                            )
+                        else:
+                            status_text = str(status_result)
+
+                        if str(PipelineRunStatus.DATASET_PROCESSING_COMPLETED) in status_text:
+                            break
+                        elif time.time() - start > TIMEOUT:
+                            raise TimeoutError(
+                                "Cognify of developer rules did not complete in 5min"
+                            )
+                    except DatabaseNotCreatedError:
+                        if time.time() - start > TIMEOUT:
+                            raise TimeoutError("Database was not created in 5min")
+
+                self.test_results["cognee_add_developer_rules"] = {
+                    "status": "PASS",
+                    "result": result,
+                    "message": "Developer rules addition executed successfully",
+                }
+                print("✅ Developer rules test passed")
+
+        except Exception as e:
+            self.test_results["cognee_add_developer_rules"] = {
+                "status": "FAIL",
+                "error": str(e),
+                "message": "Developer rules test failed",
+            }
+            print(f"❌ Developer rules test failed: {e}")
+
     async def test_search_functionality(self):
         """Test the search functionality with different search types using MCP client."""
         print("\n🧪 Testing search functionality...")
@@ -257,11 +359,7 @@ DEBUG = True
         # Go through all Cognee search types
         for search_type in SearchType:
             # Don't test these search types
-            if search_type in [
-                SearchType.NATURAL_LANGUAGE,
-                SearchType.CYPHER,
-                SearchType.TRIPLET_COMPLETION,
-            ]:
+            if search_type in [SearchType.NATURAL_LANGUAGE, SearchType.CYPHER]:
                 break
             try:
                 async with self.mcp_server_session() as session:
@@ -583,6 +681,9 @@ class TestModel:
             test_name="Cognify2",
         )
 
+        await self.test_codify()
+        await self.test_cognee_add_developer_rules()
+
         # Test list_data and delete functionality
         await self.test_list_data()
         await self.test_delete()
@@ -638,5 +739,7 @@ async def main():
 
 
 if __name__ == "__main__":
+    from logging import ERROR
+
     logger = setup_logging(log_level=ERROR)
     asyncio.run(main())

cognee-mcp/src/tools/__init__.py

@@ -0,0 +1,11 @@
+from .clear_registry import clear_registry
+from .find_mcp_server import find_mcp_server
+from .list_mcp_servers import list_mcp_servers
+from .remember_mcp_server import remember_mcp_server
+
+__all__ = [
+    "clear_registry",
+    "find_mcp_server",
+    "list_mcp_servers",
+    "remember_mcp_server",
+]

cognee-mcp/src/tools/clear_registry.py

@@ -0,0 +1,40 @@
+from contextlib import redirect_stdout
+import sys
+from src.utils import context
+from cognee.shared.logging_utils import get_logger
+import mcp.types as types
+
+logger = get_logger()
+
+
+async def clear_registry() -> list:
+    """
+    Clear all stored MCP server information from the registry.
+
+    Removes all MCP servers you've stored. Use with caution as this cannot be undone.
+
+    Returns
+    -------
+    list
+        A TextContent object confirming the registry was cleared.
+    """
+
+    with redirect_stdout(sys.stderr):
+        try:
+            await context.cognee_client.prune_data()
+            await context.cognee_client.prune_system(metadata=True)
+            logger.info("MCP server registry cleared")
+            return [
+                types.TextContent(
+                    type="text",
+                    text="✅ MCP server registry has been cleared. All stored servers removed.",
+                )
+            ]
+        except NotImplementedError:
+            error_msg = "❌ Clear operation is not available in API mode"
+            logger.error(error_msg)
+            return [types.TextContent(type="text", text=error_msg)]
+        except Exception as e:
+            error_msg = f"❌ Failed to clear registry: {str(e)}"
+            logger.error(error_msg)
+            return [types.TextContent(type="text", text=error_msg)]

cognee-mcp/src/tools/find_mcp_server.py

@@ -0,0 +1,85 @@
+from contextlib import redirect_stdout
+import json
+import sys
+from cognee.shared.logging_utils import get_logger
+import mcp.types as types
+
+from src.utils import context
+
+logger = get_logger()
+
+
+async def find_mcp_server(requirements: str, max_results: int = 5) -> list:
+    """
+    Search for MCP servers that match your requirements.
+
+    Searches through stored MCP servers and returns the ones that best match your needs
+    based on their capabilities and descriptions.
+
+    Parameters
+    ----------
+    requirements : str
+        Describe what you need the MCP server to do. Be specific about your use case.
+        Examples:
+        - "I need to read and write files"
+        - "I want to search the web for real-time information"
+        - "I need to control a browser and take screenshots"
+        - "I want to execute code in a sandbox"
+
+    max_results : int, optional
+        Maximum number of MCP servers to return (default: 5)
+
+    Returns
+    -------
+    list
+        A TextContent object with detailed information about matching MCP servers,
+        including their names, descriptions, capabilities, and installation instructions.
+
+    Examples
+    --------
+    ```python
+    # Find a server for file operations
+    await find_mcp_server("I need to read and modify files in my project")
+
+    # Find a server for web search
+    await find_mcp_server("I want to search the internet for current information")
+    ```
+    """
+
+    with redirect_stdout(sys.stderr):
+        try:
+            logger.info(f"Searching for MCP servers matching: {requirements}")
+
+            # Search using GRAPH_COMPLETION for intelligent matching
+            search_results = await context.cognee_client.search(
+                query_text=f"Find MCP servers that can: {requirements}. Include their capabilities, installation instructions, and documentation.",
+                query_type="GRAPH_COMPLETION",
+            )
+
+            # Format the results
+            if context.cognee_client.use_api:
+                if isinstance(search_results, str):
+                    result_text = search_results
+                elif isinstance(search_results, list) and len(search_results) > 0:
+                    result_text = str(search_results[0])
+                else:
+                    result_text = json.dumps(search_results, cls=json.JSONEncoder)
+            else:
+                if isinstance(search_results, list) and len(search_results) > 0:
+                    result_text = str(search_results[0])
+                else:
+                    result_text = str(search_results)
+
+            logger.info("MCP server search completed")
+
+            return [
+                types.TextContent(
+                    type="text",
+                    text=f"🔍 MCP Servers matching your requirements:\n\n{result_text}",
+                )
+            ]
+
+        except Exception as e:
+            error_msg = f"❌ Failed to search for MCP servers: {str(e)}"
+            logger.error(error_msg)
+            return [types.TextContent(type="text", text=error_msg)]

cognee-mcp/src/tools/list_mcp_servers.py

@@ -0,0 +1,65 @@
+from contextlib import redirect_stdout
+import json
+import sys
+from cognee.shared.logging_utils import get_logger
+import mcp.types as types
+
+from src.utils import context
+
+logger = get_logger()
+
+
+async def list_mcp_servers() -> list:
+    """
+    List all MCP servers stored in your personal registry.
+
+    Returns detailed information about MCP servers you've previously remembered,
+    including their connection details (URL/command), capabilities, and documentation.
+    Use this information to connect to the servers with your MCP client.
+
+    Returns
+    -------
+    list
+        A list of all MCP servers in the registry with their connection information.
+    """
+
+    with redirect_stdout(sys.stderr):
+        try:
+            logger.info("Listing all MCP servers")
+
+            # Search for all MCP servers with connection details
+            search_results = await context.cognee_client.search(
+                query_text="List all MCP servers with their names, descriptions, capabilities, connection information (URL, command, args), installation instructions, and documentation links",
+                query_type="GRAPH_COMPLETION",
+            )
+
+            # Format the results
+            if context.cognee_client.use_api:
+                if isinstance(search_results, str):
+                    result_text = search_results
+                elif isinstance(search_results, list) and len(search_results) > 0:
+                    result_text = str(search_results[0])
+                else:
+                    result_text = json.dumps(search_results, cls=json.JSONEncoder)
+            else:
+                if isinstance(search_results, list) and len(search_results) > 0:
+                    result_text = str(search_results[0])
+                else:
+                    result_text = str(search_results)
+
+            output_text = f"📋 MCP Servers in Registry:\n\n{result_text}\n\n"
+            output_text += "💡 Use the connection information above (URL or command/args) to configure your MCP client."
+
+            logger.info("MCP server listing completed")
+
+            return [
+                types.TextContent(
+                    type="text",
+                    text=output_text,
+                )
+            ]
+
+        except Exception as e:
+            error_msg = f"❌ Failed to list MCP servers: {str(e)}"
+            logger.error(error_msg)
+            return [types.TextContent(type="text", text=error_msg)]

cognee-mcp/src/tools/remember_mcp_server.py

@@ -0,0 +1,143 @@
+import asyncio
+from contextlib import redirect_stdout
+import sys
+from cognee.shared.logging_utils import get_logger, get_log_file_location
+import mcp.types as types
+
+from src.utils import context
+
+logger = get_logger()
+
+
+async def remember_mcp_server(
+    server_name: str,
+    description: str,
+    capabilities: str,
+    url: str = None,
+    command: str = None,
+    args: str = None,
+    installation: str = None,
+    repository_url: str = None,
+    documentation_url: str = None,
+    tags: str = None,
+) -> list:
+    """
+    Store information about an MCP server for future discovery.
+
+    Use this when you learn about an MCP server and want to remember its details,
+    capabilities, and connection information for later retrieval.
+
+    Parameters
+    ----------
+    server_name : str
+        The name of the MCP server (e.g., "filesystem", "brave-search", "puppeteer")
+
+    description : str
+        A comprehensive description of what the MCP server does, its main features,
+        and what problems it solves. Be detailed to improve search accuracy.
+
+    capabilities : str
+        What the server can do. List specific capabilities, use cases, and features.
+        Examples: "file operations, directory listing, search files"
+        or "web search, real-time information, news retrieval"
+
+    url : str, optional
+        Server URL for HTTP/SSE connections (e.g., "http://localhost:8124/sse")
+
+    command : str, optional
+        Command to run for stdio-based servers (e.g., "python", "npx")
+
+    args : str, optional
+        Command arguments as a comma-separated string (e.g., "src/server.py, --transport, stdio")
+
+    installation : str, optional
+        How to install and configure the server (commands, config examples, etc.)
+
+    repository_url : str, optional
+        GitHub or source code repository URL
+
+    documentation_url : str, optional
+        Link to documentation or README
+
+    tags : str, optional
+        Comma-separated tags for categorization (e.g., "filesystem, tools, dev-tools")
+
+    Returns
+    -------
+    list
+        A TextContent object confirming the server was stored successfully.
+
+    Examples
+    --------
+    ```python
+    await remember_mcp_server(
+        server_name="filesystem",
+        description="Provides comprehensive file system operations including reading, writing, editing files and directories",
+        capabilities="read files, write files, search files, list directories, create directories, move files",
+        installation="npx -y @modelcontextprotocol/server-filesystem /path/to/allowed/directory",
+        repository_url="https://github.com/modelcontextprotocol/servers",
+        tags="filesystem, tools, files"
+    )
+    ```
+    """
+
+    async def store_mcp_server() -> None:
+        with redirect_stdout(sys.stderr):
+            logger.info(f"Storing MCP server: {server_name}")
+
+            # Create structured content about the MCP server
+            server_content = f"""
+# MCP Server: {server_name}
+
+## Description
+{description}
+
+## Capabilities
+{capabilities}
+
+## Connection
+URL: {url or "Not provided"}
+Command: {command or "Not provided"}
+Args: {args or "Not provided"}
+
+## Installation
+{installation or "Not provided"}
+
+## Repository
+{repository_url or "Not provided"}
+
+## Documentation
+{documentation_url or "Not provided"}
+
+## Tags
+{tags or "Not provided"}
+"""
+
+            try:
+                # Add to knowledge graph with special node set
+                await context.cognee_client.add(
+                    server_content, node_set=["mcp_servers", server_name]
+                )
+
+                # Process into knowledge graph
+                await context.cognee_client.cognify()
+
+                logger.info(f"Successfully stored MCP server: {server_name}")
+            except Exception as e:
+                logger.error(f"Failed to store MCP server {server_name}: {str(e)}")
+                raise ValueError(f"Failed to store MCP server: {str(e)}")
+
+    # Run as background task
+    asyncio.create_task(store_mcp_server())
+
+    log_file = get_log_file_location()
+    return [
+        types.TextContent(
+            type="text",
+            text=(
+                f"✅ Started storing MCP server '{server_name}' in background.\n"
+                f"Check logs at {log_file} for completion status.\n"
+                f"Use 'find_mcp_server' to search for it once processing is complete."
+            ),
+        )
+    ]

cognee-mcp/src/utils/__init__.py

@@ -0,0 +1,3 @@
+from .context import cognee_client, set_cognee_client
+
+__all__ = ["cognee_client", "set_cognee_client"]

cognee-mcp/src/utils/context.py

@@ -0,0 +1,12 @@
+from typing import Optional, TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from src.clients.cognee_client import CogneeClient
+
+cognee_client: Optional["CogneeClient"] = None
+
+
+def set_cognee_client(client: "CogneeClient") -> None:
+    """Set the global cognee client instance."""
+    global cognee_client
+    cognee_client = client

cognee-mcp/uv.lock

@@ -1,5 +1,5 @@
 version = 1
-revision = 2
+revision = 3
 requires-python = ">=3.10"
 resolution-markers = [
     "python_full_version >= '3.14' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin'",
@@ -196,15 +196,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/bd/af/ad12d592f623aae2bd1d3463201dc39c201ea362f9ddee0d03efd9e83720/aiohttp-3.13.0-cp314-cp314t-win_amd64.whl", hash = "sha256:1f164699a060c0b3616459d13c1464a981fddf36f892f0a5027cbd45121fb14b", size = 496010, upload-time = "2025-10-06T19:58:05.589Z" },
 ]
 
-[[package]]
-name = "aiolimiter"
-version = "1.2.1"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/f1/23/b52debf471f7a1e42e362d959a3982bdcb4fe13a5d46e63d28868807a79c/aiolimiter-1.2.1.tar.gz", hash = "sha256:e02a37ea1a855d9e832252a105420ad4d15011505512a1a1d814647451b5cca9", size = 7185, upload-time = "2024-12-08T15:31:51.496Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/f3/ba/df6e8e1045aebc4778d19b8a3a9bc1808adb1619ba94ca354d9ba17d86c3/aiolimiter-1.2.1-py3-none-any.whl", hash = "sha256:d3f249e9059a20badcb56b61601a83556133655c11d1eb3dd3e04ff069e5f3c7", size = 6711, upload-time = "2024-12-08T15:31:49.874Z" },
-]
-
 [[package]]
 name = "aiosignal"
 version = "1.4.0"
@@ -679,16 +670,13 @@ wheels = [
 
 [[package]]
 name = "cognee"
-version = "0.5.0"
+version = "0.3.7"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiofiles" },
     { name = "aiohttp" },
-    { name = "aiolimiter" },
     { name = "aiosqlite" },
     { name = "alembic" },
-    { name = "diskcache" },
-    { name = "fakeredis", extra = ["lua"] },
     { name = "fastapi" },
     { name = "fastapi-users", extra = ["sqlalchemy"] },
     { name = "fastembed" },
@@ -725,17 +713,25 @@ dependencies = [
     { name = "uvicorn" },
     { name = "websockets" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/6c/eb/09694604a0e64f0d79c1896cb6d806a9445922a0db82df09f2eb20eed94a/cognee-0.5.0.tar.gz", hash = "sha256:a3863b7779f01577ea9b1fb87cf053000dfa57db9b840c069a3d7eed037459d2", size = 14346425, upload-time = "2025-12-15T19:37:36.463Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/9e/b5/4846db96fe1060ae5158b736742988aa2866709d6f763c69a7d398cea7a2/cognee-0.3.7.tar.gz", hash = "sha256:2ce189d7d8aca5c79c5a7efc46b28a15998aa43d5d8755c0b8ad48a6c9392a1e", size = 14187817, upload-time = "2025-10-22T20:29:40.638Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/e7/7a/399f52fa19dcdee7505d8c605cff99a6cdd3fdc8235720c2fecbd8e356b3/cognee-0.5.0-py3-none-any.whl", hash = "sha256:b8293a3b7316ba77fb0738519ee553005f368c83d11fb0202c56a822e06487a9", size = 1357874, upload-time = "2025-12-15T19:37:38.814Z" },
+    { url = "https://files.pythonhosted.org/packages/3d/95/1ed70690b86dfdccc9467d82c6b120e3aefa12bd34af66b6913b105c3cae/cognee-0.3.7-py3-none-any.whl", hash = "sha256:5f895c0826db892ea3cbbe6003fa5ebfd27f4fabe329c9c5196d76758a2a08db", size = 1268254, upload-time = "2025-10-22T20:29:38.175Z" },
 ]
 
 [package.optional-dependencies]
+codegraph = [
+    { name = "fastembed", marker = "python_full_version < '3.13'" },
+    { name = "transformers" },
+    { name = "tree-sitter" },
+    { name = "tree-sitter-python" },
+]
 docs = [
-    { name = "lxml", version = "4.9.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.13'" },
-    { name = "lxml", version = "5.4.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.13'" },
+    { name = "lxml" },
     { name = "unstructured", extra = ["csv", "doc", "docx", "epub", "md", "odt", "org", "pdf", "ppt", "pptx", "rst", "rtf", "tsv", "xlsx"] },
 ]
+huggingface = [
+    { name = "transformers" },
+]
 neo4j = [
     { name = "neo4j" },
 ]
@@ -747,32 +743,40 @@ postgres = [
 
 [[package]]
 name = "cognee-mcp"
-version = "0.5.0"
+version = "0.4.0"
 source = { editable = "." }
 dependencies = [
-    { name = "cognee", extra = ["docs", "neo4j", "postgres"] },
+    { name = "cognee", extra = ["codegraph", "docs", "huggingface", "neo4j", "postgres"] },
     { name = "fastmcp" },
     { name = "httpx" },
     { name = "mcp" },
+    { name = "pydantic" },
     { name = "uv" },
 ]
 
 [package.dev-dependencies]
 dev = [
     { name = "debugpy" },
+    { name = "mypy" },
+    { name = "ruff" },
 ]
 
 [package.metadata]
 requires-dist = [
-    { name = "cognee", extras = ["postgres", "docs", "neo4j"], specifier = "==0.5.0" },
+    { name = "cognee", extras = ["postgres", "codegraph", "huggingface", "docs", "neo4j"], specifier = "==0.3.7" },
     { name = "fastmcp", specifier = ">=2.10.0,<3.0.0" },
     { name = "httpx", specifier = ">=0.27.0,<1.0.0" },
     { name = "mcp", specifier = ">=1.12.0,<2.0.0" },
+    { name = "pydantic", specifier = ">=2.12.1" },
     { name = "uv", specifier = ">=0.6.3,<1.0.0" },
 ]
 
 [package.metadata.requires-dev]
-dev = [{ name = "debugpy", specifier = ">=1.8.12,<2.0.0" }]
+dev = [
+    { name = "debugpy", specifier = ">=1.8.12,<2.0.0" },
+    { name = "mypy", specifier = ">=1.18.2" },
+    { name = "ruff", specifier = ">=0.14.2" },
+]
 
 [[package]]
 name = "colorama"
@@ -1264,25 +1268,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/36/f4/c6e662dade71f56cd2f3735141b265c3c79293c109549c1e6933b0651ffc/exceptiongroup-1.3.0-py3-none-any.whl", hash = "sha256:4d111e6e0c13d0644cad6ddaa7ed0261a0b36971f6d23e7ec9b4b9097da78a10", size = 16674, upload-time = "2025-05-10T17:42:49.33Z" },
 ]
 
-[[package]]
-name = "fakeredis"
-version = "2.32.1"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "redis" },
-    { name = "sortedcontainers" },
-    { name = "typing-extensions", marker = "python_full_version < '3.11'" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/56/14/b47b8471303af7deed7080290c14cff27a831fa47b38f45643e6bf889cee/fakeredis-2.32.1.tar.gz", hash = "sha256:dd8246db159f0b66a1ced7800c9d5ef07769e3d2fde44b389a57f2ce2834e444", size = 171582, upload-time = "2025-11-06T01:40:57.836Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/c2/d2/c28f6909864bfdb7411bb8f39fabedb5a50da1cbd7da5a1a3a46dfea2eab/fakeredis-2.32.1-py3-none-any.whl", hash = "sha256:e80c8886db2e47ba784f7dfe66aad6cd2eab76093c6bfda50041e5bc890d46cf", size = 118964, upload-time = "2025-11-06T01:40:55.885Z" },
-]
-
-[package.optional-dependencies]
-lua = [
-    { name = "lupa" },
-]
-
 [[package]]
 name = "fastapi"
 version = "0.119.0"
@@ -1729,8 +1714,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/7f/91/ae2eb6b7979e2f9b035a9f612cf70f1bf54aad4e1d125129bef1eae96f19/greenlet-3.2.4-cp310-cp310-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c2ca18a03a8cfb5b25bc1cbe20f3d9a4c80d8c3b13ba3df49ac3961af0b1018d", size = 584358, upload-time = "2025-08-07T13:18:23.708Z" },
     { url = "https://files.pythonhosted.org/packages/f7/85/433de0c9c0252b22b16d413c9407e6cb3b41df7389afc366ca204dbc1393/greenlet-3.2.4-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:9fe0a28a7b952a21e2c062cd5756d34354117796c6d9215a87f55e38d15402c5", size = 1113550, upload-time = "2025-08-07T13:42:37.467Z" },
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+
 [[package]]
 name = "triton"
 version = "3.5.0"
@@ -6289,8 +6248,7 @@ dependencies = [
     { name = "filetype" },
     { name = "html5lib" },
     { name = "langdetect" },
-    { name = "lxml", version = "4.9.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.13'" },
-    { name = "lxml", version = "5.4.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.13'" },
+    { name = "lxml" },
     { name = "nltk" },
     { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
     { name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },

cognee/__init__.py

@@ -19,7 +19,6 @@ from .api.v1.add import add
 from .api.v1.delete import delete
 from .api.v1.cognify import cognify
 from .modules.memify import memify
-from .modules.run_custom_pipeline import run_custom_pipeline
 from .api.v1.update import update
 from .api.v1.config.config import config
 from .api.v1.datasets.datasets import datasets

cognee/api/client.py

@@ -21,9 +21,8 @@ from cognee.api.v1.notebooks.routers import get_notebooks_router
 from cognee.api.v1.permissions.routers import get_permissions_router
 from cognee.api.v1.settings.routers import get_settings_router
 from cognee.api.v1.datasets.routers import get_datasets_router
-from cognee.api.v1.cognify.routers import get_cognify_router
+from cognee.api.v1.cognify.routers import get_code_pipeline_router, get_cognify_router
 from cognee.api.v1.search.routers import get_search_router
-from cognee.api.v1.ontologies.routers.get_ontology_router import get_ontology_router
 from cognee.api.v1.memify.routers import get_memify_router
 from cognee.api.v1.add.routers import get_add_router
 from cognee.api.v1.delete.routers import get_delete_router
@@ -40,8 +39,6 @@ from cognee.api.v1.users.routers import (
 )
 from cognee.modules.users.methods.get_authenticated_user import REQUIRE_AUTHENTICATION
 
-# Ensure application logging is configured for container stdout/stderr
-setup_logging()
 logger = get_logger()
 
 if os.getenv("ENV", "prod") == "prod":
@@ -77,9 +74,6 @@ async def lifespan(app: FastAPI):
 
     await get_default_user()
 
-    # Emit a clear startup message for docker logs
-    logger.info("Backend server has started")
-
     yield
 
 
@@ -264,8 +258,6 @@ app.include_router(
 
 app.include_router(get_datasets_router(), prefix="/api/v1/datasets", tags=["datasets"])
 
-app.include_router(get_ontology_router(), prefix="/api/v1/ontologies", tags=["ontologies"])
-
 app.include_router(get_settings_router(), prefix="/api/v1/settings", tags=["settings"])
 
 app.include_router(get_visualize_router(), prefix="/api/v1/visualize", tags=["visualize"])
@@ -278,6 +270,10 @@ app.include_router(get_responses_router(), prefix="/api/v1/responses", tags=["re
 
 app.include_router(get_sync_router(), prefix="/api/v1/sync", tags=["sync"])
 
+codegraph_routes = get_code_pipeline_router()
+if codegraph_routes:
+    app.include_router(codegraph_routes, prefix="/api/v1/code-pipeline", tags=["code-pipeline"])
+
 app.include_router(
     get_users_router(),
     prefix="/api/v1/users",

cognee/api/v1/add/add.py

@@ -155,7 +155,7 @@ async def add(
         - LLM_API_KEY: API key for your LLM provider (OpenAI, Anthropic, etc.)
 
         Optional:
-        - LLM_PROVIDER: "openai" (default), "anthropic", "gemini", "ollama", "mistral", "bedrock"
+        - LLM_PROVIDER: "openai" (default), "anthropic", "gemini", "ollama", "mistral"
         - LLM_MODEL: Model name (default: "gpt-5-mini")
         - DEFAULT_USER_EMAIL: Custom default user email
         - DEFAULT_USER_PASSWORD: Custom default user password
@@ -205,7 +205,6 @@ async def add(
         pipeline_name="add_pipeline",
         vector_db_config=vector_db_config,
         graph_db_config=graph_db_config,
-        use_pipeline_cache=True,
         incremental_loading=incremental_loading,
         data_per_batch=data_per_batch,
     ):

cognee/api/v1/add/routers/get_add_router.py

@@ -82,9 +82,7 @@ def get_add_router() -> APIRouter:
                 datasetName,
                 user=user,
                 dataset_id=datasetId,
-                node_set=node_set
-                if node_set != [""]
-                else None,  # Transform default node_set endpoint value to None
+                node_set=node_set if node_set else None,
             )
 
             if isinstance(add_run, PipelineRunErrored):

cognee/api/v1/cognify/code_graph_pipeline.py

@@ -0,0 +1,119 @@
+import os
+import pathlib
+import asyncio
+from typing import Optional
+from cognee.shared.logging_utils import get_logger, setup_logging
+from cognee.modules.observability.get_observe import get_observe
+
+from cognee.api.v1.search import SearchType, search
+from cognee.api.v1.visualize.visualize import visualize_graph
+from cognee.modules.cognify.config import get_cognify_config
+from cognee.modules.pipelines import run_tasks
+from cognee.modules.pipelines.tasks.task import Task
+from cognee.modules.users.methods import get_default_user
+from cognee.shared.data_models import KnowledgeGraph
+from cognee.modules.data.methods import create_dataset
+from cognee.tasks.documents import classify_documents, extract_chunks_from_documents
+from cognee.tasks.graph import extract_graph_from_data
+from cognee.tasks.ingestion import ingest_data
+from cognee.tasks.repo_processor import get_non_py_files, get_repo_file_dependencies
+
+from cognee.tasks.storage import add_data_points
+from cognee.tasks.summarization import summarize_text
+from cognee.infrastructure.llm import get_max_chunk_tokens
+from cognee.infrastructure.databases.relational import get_relational_engine
+
+observe = get_observe()
+
+logger = get_logger("code_graph_pipeline")
+
+
+@observe
+async def run_code_graph_pipeline(
+    repo_path,
+    include_docs=False,
+    excluded_paths: Optional[list[str]] = None,
+    supported_languages: Optional[list[str]] = None,
+):
+    import cognee
+    from cognee.low_level import setup
+
+    await cognee.prune.prune_data()
+    await cognee.prune.prune_system(metadata=True)
+    await setup()
+
+    cognee_config = get_cognify_config()
+    user = await get_default_user()
+    detailed_extraction = True
+
+    tasks = [
+        Task(
+            get_repo_file_dependencies,
+            detailed_extraction=detailed_extraction,
+            supported_languages=supported_languages,
+            excluded_paths=excluded_paths,
+        ),
+        # Task(summarize_code, task_config={"batch_size": 500}), # This task takes a long time to complete
+        Task(add_data_points, task_config={"batch_size": 30}),
+    ]
+
+    if include_docs:
+        # This tasks take a long time to complete
+        non_code_tasks = [
+            Task(get_non_py_files, task_config={"batch_size": 50}),
+            Task(ingest_data, dataset_name="repo_docs", user=user),
+            Task(classify_documents),
+            Task(extract_chunks_from_documents, max_chunk_size=get_max_chunk_tokens()),
+            Task(
+                extract_graph_from_data,
+                graph_model=KnowledgeGraph,
+                task_config={"batch_size": 50},
+            ),
+            Task(
+                summarize_text,
+                summarization_model=cognee_config.summarization_model,
+                task_config={"batch_size": 50},
+            ),
+        ]
+
+    dataset_name = "codebase"
+
+    # Save dataset to database
+    db_engine = get_relational_engine()
+    async with db_engine.get_async_session() as session:
+        dataset = await create_dataset(dataset_name, user, session)
+
+    if include_docs:
+        non_code_pipeline_run = run_tasks(
+            non_code_tasks, dataset.id, repo_path, user, "cognify_pipeline"
+        )
+        async for run_status in non_code_pipeline_run:
+            yield run_status
+
+    async for run_status in run_tasks(
+        tasks, dataset.id, repo_path, user, "cognify_code_pipeline", incremental_loading=False
+    ):
+        yield run_status
+
+
+if __name__ == "__main__":
+
+    async def main():
+        async for run_status in run_code_graph_pipeline("REPO_PATH"):
+            print(f"{run_status.pipeline_run_id}: {run_status.status}")
+
+        file_path = os.path.join(
+            pathlib.Path(__file__).parent, ".artifacts", "graph_visualization.html"
+        )
+        await visualize_graph(file_path)
+
+        search_results = await search(
+            query_type=SearchType.CODE,
+            query_text="How is Relationship weight calculated?",
+        )
+
+        for file in search_results:
+            print(file["name"])
+
+    logger = setup_logging(name="code_graph_pipeline")
+    asyncio.run(main())

cognee/api/v1/cognify/cognify.py

@@ -3,7 +3,6 @@ from pydantic import BaseModel
 from typing import Union, Optional
 from uuid import UUID
 
-from cognee.modules.cognify.config import get_cognify_config
 from cognee.modules.ontology.ontology_env_config import get_ontology_env_config
 from cognee.shared.logging_utils import get_logger
 from cognee.shared.data_models import KnowledgeGraph
@@ -20,6 +19,7 @@ from cognee.modules.ontology.get_default_ontology_resolver import (
 from cognee.modules.users.models import User
 
 from cognee.tasks.documents import (
+    check_permissions_on_dataset,
     classify_documents,
     extract_chunks_from_documents,
 )
@@ -53,7 +53,6 @@ async def cognify(
     custom_prompt: Optional[str] = None,
     temporal_cognify: bool = False,
     data_per_batch: int = 20,
-    **kwargs,
 ):
     """
     Transform ingested data into a structured knowledge graph.
@@ -79,11 +78,12 @@ async def cognify(
 
     Processing Pipeline:
         1. **Document Classification**: Identifies document types and structures
-        2. **Text Chunking**: Breaks content into semantically meaningful segments
-        3. **Entity Extraction**: Identifies key concepts, people, places, organizations
-        4. **Relationship Detection**: Discovers connections between entities
-        5. **Graph Construction**: Builds semantic knowledge graph with embeddings
-        6. **Content Summarization**: Creates hierarchical summaries for navigation
+        2. **Permission Validation**: Ensures user has processing rights
+        3. **Text Chunking**: Breaks content into semantically meaningful segments
+        4. **Entity Extraction**: Identifies key concepts, people, places, organizations
+        5. **Relationship Detection**: Discovers connections between entities
+        6. **Graph Construction**: Builds semantic knowledge graph with embeddings
+        7. **Content Summarization**: Creates hierarchical summaries for navigation
 
     Graph Model Customization:
         The `graph_model` parameter allows custom knowledge structures:
@@ -224,7 +224,6 @@ async def cognify(
             config=config,
             custom_prompt=custom_prompt,
             chunks_per_batch=chunks_per_batch,
-            **kwargs,
         )
 
     # By calling get pipeline executor we get a function that will have the run_pipeline run in the background or a function that we will need to wait for
@@ -239,7 +238,6 @@ async def cognify(
         vector_db_config=vector_db_config,
         graph_db_config=graph_db_config,
         incremental_loading=incremental_loading,
-        use_pipeline_cache=True,
         pipeline_name="cognify_pipeline",
         data_per_batch=data_per_batch,
     )
@@ -253,7 +251,6 @@ async def get_default_tasks(  # TODO: Find out a better way to do this (Boris's
     config: Config = None,
     custom_prompt: Optional[str] = None,
     chunks_per_batch: int = 100,
-    **kwargs,
 ) -> list[Task]:
     if config is None:
         ontology_config = get_ontology_env_config()
@@ -275,11 +272,9 @@ async def get_default_tasks(  # TODO: Find out a better way to do this (Boris's
     if chunks_per_batch is None:
         chunks_per_batch = 100
 
-    cognify_config = get_cognify_config()
-    embed_triplets = cognify_config.triplet_embedding
-
     default_tasks = [
         Task(classify_documents),
+        Task(check_permissions_on_dataset, user=user, permissions=["write"]),
         Task(
             extract_chunks_from_documents,
             max_chunk_size=chunk_size or get_max_chunk_tokens(),
@@ -291,17 +286,12 @@ async def get_default_tasks(  # TODO: Find out a better way to do this (Boris's
             config=config,
             custom_prompt=custom_prompt,
             task_config={"batch_size": chunks_per_batch},
-            **kwargs,
         ),  # Generate knowledge graphs from the document chunks.
         Task(
             summarize_text,
             task_config={"batch_size": chunks_per_batch},
         ),
-        Task(
-            add_data_points,
-            embed_triplets=embed_triplets,
-            task_config={"batch_size": chunks_per_batch},
-        ),
+        Task(add_data_points, task_config={"batch_size": chunks_per_batch}),
     ]
 
     return default_tasks
@@ -315,13 +305,14 @@ async def get_temporal_tasks(
 
     The pipeline includes:
     1. Document classification.
-    2. Document chunking with a specified or default chunk size.
-    3. Event and timestamp extraction from chunks.
-    4. Knowledge graph extraction from events.
-    5. Batched insertion of data points.
+    2. Dataset permission checks (requires "write" access).
+    3. Document chunking with a specified or default chunk size.
+    4. Event and timestamp extraction from chunks.
+    5. Knowledge graph extraction from events.
+    6. Batched insertion of data points.
 
     Args:
-        user (User, optional): The user requesting task execution.
+        user (User, optional): The user requesting task execution, used for permission checks.
         chunker (Callable, optional): A text chunking function/class to split documents. Defaults to TextChunker.
         chunk_size (int, optional): Maximum token size per chunk. If not provided, uses system default.
         chunks_per_batch (int, optional): Number of chunks to process in a single batch in Cognify
@@ -334,6 +325,7 @@ async def get_temporal_tasks(
 
     temporal_tasks = [
         Task(classify_documents),
+        Task(check_permissions_on_dataset, user=user, permissions=["write"]),
         Task(
             extract_chunks_from_documents,
             max_chunk_size=chunk_size or get_max_chunk_tokens(),

cognee/api/v1/cognify/routers/__init__.py

@@ -1 +1,2 @@
 from .get_cognify_router import get_cognify_router
+from .get_code_pipeline_router import get_code_pipeline_router

cognee/api/v1/cognify/routers/get_code_pipeline_router.py

@@ -0,0 +1,90 @@
+import json
+from cognee.shared.logging_utils import get_logger
+from fastapi import APIRouter
+from fastapi.responses import JSONResponse
+from cognee.api.DTO import InDTO
+from cognee.modules.retrieval.code_retriever import CodeRetriever
+from cognee.modules.storage.utils import JSONEncoder
+
+
+logger = get_logger()
+
+
+class CodePipelineIndexPayloadDTO(InDTO):
+    repo_path: str
+    include_docs: bool = False
+
+
+class CodePipelineRetrievePayloadDTO(InDTO):
+    query: str
+    full_input: str
+
+
+def get_code_pipeline_router() -> APIRouter:
+    try:
+        import cognee.api.v1.cognify.code_graph_pipeline
+    except ModuleNotFoundError:
+        logger.error("codegraph dependencies not found. Skipping codegraph API routes.")
+        return None
+
+    router = APIRouter()
+
+    @router.post("/index", response_model=None)
+    async def code_pipeline_index(payload: CodePipelineIndexPayloadDTO):
+        """
+        Run indexation on a code repository.
+
+        This endpoint processes a code repository to create a knowledge graph
+        of the codebase structure, dependencies, and relationships.
+
+        ## Request Parameters
+        - **repo_path** (str): Path to the code repository
+        - **include_docs** (bool): Whether to include documentation files (default: false)
+
+        ## Response
+        No content returned. Processing results are logged.
+
+        ## Error Codes
+        - **409 Conflict**: Error during indexation process
+        """
+        from cognee.api.v1.cognify.code_graph_pipeline import run_code_graph_pipeline
+
+        try:
+            async for result in run_code_graph_pipeline(payload.repo_path, payload.include_docs):
+                logger.info(result)
+        except Exception as error:
+            return JSONResponse(status_code=409, content={"error": str(error)})
+
+    @router.post("/retrieve", response_model=list[dict])
+    async def code_pipeline_retrieve(payload: CodePipelineRetrievePayloadDTO):
+        """
+        Retrieve context from the code knowledge graph.
+
+        This endpoint searches the indexed code repository to find relevant
+        context based on the provided query.
+
+        ## Request Parameters
+        - **query** (str): Search query for code context
+        - **full_input** (str): Full input text for processing
+
+        ## Response
+        Returns a list of relevant code files and context as JSON.
+
+        ## Error Codes
+        - **409 Conflict**: Error during retrieval process
+        """
+        try:
+            query = (
+                payload.full_input.replace("cognee ", "")
+                if payload.full_input.startswith("cognee ")
+                else payload.full_input
+            )
+
+            retriever = CodeRetriever()
+            retrieved_files = await retriever.get_context(query)
+
+            return json.dumps(retrieved_files, cls=JSONEncoder)
+        except Exception as error:
+            return JSONResponse(status_code=409, content={"error": str(error)})
+
+    return router

cognee/api/v1/cognify/routers/get_cognify_router.py

@@ -41,11 +41,6 @@ class CognifyPayloadDTO(InDTO):
     custom_prompt: Optional[str] = Field(
         default="", description="Custom prompt for entity extraction and graph generation"
     )
-    ontology_key: Optional[List[str]] = Field(
-        default=None,
-        examples=[[]],
-        description="Reference to one or more previously uploaded ontologies",
-    )
 
 
 def get_cognify_router() -> APIRouter:
@@ -73,7 +68,6 @@ def get_cognify_router() -> APIRouter:
         - **dataset_ids** (Optional[List[UUID]]): List of existing dataset UUIDs to process. UUIDs allow processing of datasets not owned by the user (if permitted).
         - **run_in_background** (Optional[bool]): Whether to execute processing asynchronously. Defaults to False (blocking).
         - **custom_prompt** (Optional[str]): Custom prompt for entity extraction and graph generation. If provided, this prompt will be used instead of the default prompts for knowledge graph extraction.
-        - **ontology_key** (Optional[List[str]]): Reference to one or more previously uploaded ontology files to use for knowledge graph construction.
 
         ## Response
         - **Blocking execution**: Complete pipeline run information with entity counts, processing duration, and success/failure status
@@ -88,8 +82,7 @@ def get_cognify_router() -> APIRouter:
         {
             "datasets": ["research_papers", "documentation"],
             "run_in_background": false,
-            "custom_prompt": "Extract entities focusing on technical concepts and their relationships. Identify key technologies, methodologies, and their interconnections.",
-            "ontology_key": ["medical_ontology_v1"]
+            "custom_prompt": "Extract entities focusing on technical concepts and their relationships. Identify key technologies, methodologies, and their interconnections."
         }
         ```
 
@@ -115,35 +108,13 @@ def get_cognify_router() -> APIRouter:
             )
 
         from cognee.api.v1.cognify import cognify as cognee_cognify
-        from cognee.api.v1.ontologies.ontologies import OntologyService
 
         try:
             datasets = payload.dataset_ids if payload.dataset_ids else payload.datasets
-            config_to_use = None
-
-            if payload.ontology_key:
-                ontology_service = OntologyService()
-                ontology_contents = ontology_service.get_ontology_contents(
-                    payload.ontology_key, user
-                )
-
-                from cognee.modules.ontology.ontology_config import Config
-                from cognee.modules.ontology.rdf_xml.RDFLibOntologyResolver import (
-                    RDFLibOntologyResolver,
-                )
-                from io import StringIO
-
-                ontology_streams = [StringIO(content) for content in ontology_contents]
-                config_to_use: Config = {
-                    "ontology_config": {
-                        "ontology_resolver": RDFLibOntologyResolver(ontology_file=ontology_streams)
-                    }
-                }
 
             cognify_run = await cognee_cognify(
                 datasets,
                 user,
-                config=config_to_use,
                 run_in_background=payload.run_in_background,
                 custom_prompt=payload.custom_prompt,
             )

cognee/api/v1/datasets/routers/get_datasets_router.py

@@ -208,14 +208,14 @@ def get_datasets_router() -> APIRouter:
             },
         )
 
-        from cognee.modules.data.methods import delete_dataset
+        from cognee.modules.data.methods import get_dataset, delete_dataset
 
-        dataset = await get_authorized_existing_datasets([dataset_id], "delete", user)
+        dataset = await get_dataset(user.id, dataset_id)
 
         if dataset is None:
             raise DatasetNotFoundError(message=f"Dataset ({str(dataset_id)}) not found.")
 
-        await delete_dataset(dataset[0])
+        await delete_dataset(dataset)
 
     @router.delete(
         "/{dataset_id}/data/{data_id}",

cognee/api/v1/ontologies/__init__.py

@@ -1,4 +0,0 @@
-from .ontologies import OntologyService
-from .routers.get_ontology_router import get_ontology_router
-
-__all__ = ["OntologyService", "get_ontology_router"]

cognee/api/v1/ontologies/ontologies.py

@@ -1,158 +0,0 @@
-import os
-import json
-import tempfile
-from pathlib import Path
-from datetime import datetime, timezone
-from typing import Optional, List
-from dataclasses import dataclass
-from fastapi import UploadFile
-
-
-@dataclass
-class OntologyMetadata:
-    ontology_key: str
-    filename: str
-    size_bytes: int
-    uploaded_at: str
-    description: Optional[str] = None
-
-
-class OntologyService:
-    def __init__(self):
-        pass
-
-    @property
-    def base_dir(self) -> Path:
-        return Path(tempfile.gettempdir()) / "ontologies"
-
-    def _get_user_dir(self, user_id: str) -> Path:
-        user_dir = self.base_dir / str(user_id)
-        user_dir.mkdir(parents=True, exist_ok=True)
-        return user_dir
-
-    def _get_metadata_path(self, user_dir: Path) -> Path:
-        return user_dir / "metadata.json"
-
-    def _load_metadata(self, user_dir: Path) -> dict:
-        metadata_path = self._get_metadata_path(user_dir)
-        if metadata_path.exists():
-            with open(metadata_path, "r") as f:
-                return json.load(f)
-        return {}
-
-    def _save_metadata(self, user_dir: Path, metadata: dict):
-        metadata_path = self._get_metadata_path(user_dir)
-        with open(metadata_path, "w") as f:
-            json.dump(metadata, f, indent=2)
-
-    async def upload_ontology(
-        self, ontology_key: str, file: UploadFile, user, description: Optional[str] = None
-    ) -> OntologyMetadata:
-        if not file.filename:
-            raise ValueError("File must have a filename")
-        if not file.filename.lower().endswith(".owl"):
-            raise ValueError("File must be in .owl format")
-
-        user_dir = self._get_user_dir(str(user.id))
-        metadata = self._load_metadata(user_dir)
-
-        if ontology_key in metadata:
-            raise ValueError(f"Ontology key '{ontology_key}' already exists")
-
-        content = await file.read()
-
-        file_path = user_dir / f"{ontology_key}.owl"
-        with open(file_path, "wb") as f:
-            f.write(content)
-
-        ontology_metadata = {
-            "filename": file.filename,
-            "size_bytes": len(content),
-            "uploaded_at": datetime.now(timezone.utc).isoformat(),
-            "description": description,
-        }
-        metadata[ontology_key] = ontology_metadata
-        self._save_metadata(user_dir, metadata)
-
-        return OntologyMetadata(
-            ontology_key=ontology_key,
-            filename=file.filename,
-            size_bytes=len(content),
-            uploaded_at=ontology_metadata["uploaded_at"],
-            description=description,
-        )
-
-    async def upload_ontologies(
-        self,
-        ontology_key: List[str],
-        files: List[UploadFile],
-        user,
-        descriptions: Optional[List[str]] = None,
-    ) -> List[OntologyMetadata]:
-        """
-        Upload ontology files with their respective keys.
-
-        Args:
-            ontology_key: List of unique keys for each ontology
-            files: List of UploadFile objects (same length as keys)
-            user: Authenticated user
-            descriptions: Optional list of descriptions for each file
-
-        Returns:
-            List of OntologyMetadata objects for uploaded files
-
-        Raises:
-            ValueError: If keys duplicate, file format invalid, or array lengths don't match
-        """
-        if len(ontology_key) != len(files):
-            raise ValueError("Number of keys must match number of files")
-
-        if len(set(ontology_key)) != len(ontology_key):
-            raise ValueError("Duplicate ontology keys not allowed")
-
-        results = []
-
-        for i, (key, file) in enumerate(zip(ontology_key, files)):
-            results.append(
-                await self.upload_ontology(
-                    ontology_key=key,
-                    file=file,
-                    user=user,
-                    description=descriptions[i] if descriptions else None,
-                )
-            )
-        return results
-
-    def get_ontology_contents(self, ontology_key: List[str], user) -> List[str]:
-        """
-        Retrieve ontology content for one or more keys.
-
-        Args:
-            ontology_key: List of ontology keys to retrieve (can contain single item)
-            user: Authenticated user
-
-        Returns:
-            List of ontology content strings
-
-        Raises:
-            ValueError: If any ontology key not found
-        """
-        user_dir = self._get_user_dir(str(user.id))
-        metadata = self._load_metadata(user_dir)
-
-        contents = []
-        for key in ontology_key:
-            if key not in metadata:
-                raise ValueError(f"Ontology key '{key}' not found")
-
-            file_path = user_dir / f"{key}.owl"
-            if not file_path.exists():
-                raise ValueError(f"Ontology file for key '{key}' not found")
-
-            with open(file_path, "r", encoding="utf-8") as f:
-                contents.append(f.read())
-        return contents
-
-    def list_ontologies(self, user) -> dict:
-        user_dir = self._get_user_dir(str(user.id))
-        return self._load_metadata(user_dir)

cognee/api/v1/ontologies/routers/get_ontology_router.py

@@ -1,109 +0,0 @@
-from fastapi import APIRouter, File, Form, UploadFile, Depends, Request
-from fastapi.responses import JSONResponse
-from typing import Optional, List
-
-from cognee.modules.users.models import User
-from cognee.modules.users.methods import get_authenticated_user
-from cognee.shared.utils import send_telemetry
-from cognee import __version__ as cognee_version
-from ..ontologies import OntologyService
-
-
-def get_ontology_router() -> APIRouter:
-    router = APIRouter()
-    ontology_service = OntologyService()
-
-    @router.post("", response_model=dict)
-    async def upload_ontology(
-        request: Request,
-        ontology_key: str = Form(...),
-        ontology_file: UploadFile = File(...),
-        description: Optional[str] = Form(None),
-        user: User = Depends(get_authenticated_user),
-    ):
-        """
-        Upload a single ontology file for later use in cognify operations.
-
-        ## Request Parameters
-        - **ontology_key** (str): User-defined identifier for the ontology.
-        - **ontology_file** (UploadFile): Single OWL format ontology file
-        - **description** (Optional[str]): Optional description for the ontology.
-
-        ## Response
-        Returns metadata about the uploaded ontology including key, filename, size, and upload timestamp.
-
-        ## Error Codes
-        - **400 Bad Request**: Invalid file format, duplicate key, multiple files uploaded
-        - **500 Internal Server Error**: File system or processing errors
-        """
-        send_telemetry(
-            "Ontology Upload API Endpoint Invoked",
-            user.id,
-            additional_properties={
-                "endpoint": "POST /api/v1/ontologies",
-                "cognee_version": cognee_version,
-            },
-        )
-
-        try:
-            # Enforce: exactly one uploaded file for "ontology_file"
-            form = await request.form()
-            uploaded_files = form.getlist("ontology_file")
-            if len(uploaded_files) != 1:
-                raise ValueError("Only one ontology_file is allowed")
-
-            if ontology_key.strip().startswith(("[", "{")):
-                raise ValueError("ontology_key must be a string")
-            if description is not None and description.strip().startswith(("[", "{")):
-                raise ValueError("description must be a string")
-
-            result = await ontology_service.upload_ontology(
-                ontology_key=ontology_key,
-                file=ontology_file,
-                user=user,
-                description=description,
-            )
-
-            return {
-                "uploaded_ontologies": [
-                    {
-                        "ontology_key": result.ontology_key,
-                        "filename": result.filename,
-                        "size_bytes": result.size_bytes,
-                        "uploaded_at": result.uploaded_at,
-                        "description": result.description,
-                    }
-                ]
-            }
-        except ValueError as e:
-            return JSONResponse(status_code=400, content={"error": str(e)})
-        except Exception as e:
-            return JSONResponse(status_code=500, content={"error": str(e)})
-
-    @router.get("", response_model=dict)
-    async def list_ontologies(user: User = Depends(get_authenticated_user)):
-        """
-        List all uploaded ontologies for the authenticated user.
-
-        ## Response
-        Returns a dictionary mapping ontology keys to their metadata including filename, size, and upload timestamp.
-
-        ## Error Codes
-        - **500 Internal Server Error**: File system or processing errors
-        """
-        send_telemetry(
-            "Ontology List API Endpoint Invoked",
-            user.id,
-            additional_properties={
-                "endpoint": "GET /api/v1/ontologies",
-                "cognee_version": cognee_version,
-            },
-        )
-
-        try:
-            metadata = ontology_service.list_ontologies(user)
-            return metadata
-        except Exception as e:
-            return JSONResponse(status_code=500, content={"error": str(e)})
-
-    return router

cognee/api/v1/permissions/routers/get_permissions_router.py

@@ -1,20 +1,15 @@
 from uuid import UUID
-from typing import List, Union
+from typing import List
 
 from fastapi import APIRouter, Depends
 from fastapi.responses import JSONResponse
 
 from cognee.modules.users.models import User
-from cognee.api.DTO import InDTO
 from cognee.modules.users.methods import get_authenticated_user
 from cognee.shared.utils import send_telemetry
 from cognee import __version__ as cognee_version
 
 
-class SelectTenantDTO(InDTO):
-    tenant_id: UUID | None = None
-
-
 def get_permissions_router() -> APIRouter:
     permissions_router = APIRouter()
 
@@ -231,39 +226,4 @@ def get_permissions_router() -> APIRouter:
             status_code=200, content={"message": "Tenant created.", "tenant_id": str(tenant_id)}
         )
 
-    @permissions_router.post("/tenants/select")
-    async def select_tenant(payload: SelectTenantDTO, user: User = Depends(get_authenticated_user)):
-        """
-        Select current tenant.
-
-        This endpoint selects a tenant with the specified UUID. Tenants are used
-        to organize users and resources in multi-tenant environments, providing
-        isolation and access control between different groups or organizations.
-
-        Sending a null/None value as tenant_id selects his default single user tenant
-
-        ## Request Parameters
-        - **tenant_id** (Union[UUID, None]): UUID of the tenant to select, If null/None is provided use the default single user tenant
-
-        ## Response
-        Returns a success message along with selected tenant id.
-        """
-        send_telemetry(
-            "Permissions API Endpoint Invoked",
-            user.id,
-            additional_properties={
-                "endpoint": f"POST /v1/permissions/tenants/{str(payload.tenant_id)}",
-                "tenant_id": str(payload.tenant_id),
-            },
-        )
-
-        from cognee.modules.users.tenants.methods import select_tenant as select_tenant_method
-
-        await select_tenant_method(user_id=user.id, tenant_id=payload.tenant_id)
-
-        return JSONResponse(
-            status_code=200,
-            content={"message": "Tenant selected.", "tenant_id": str(payload.tenant_id)},
-        )
-
     return permissions_router

cognee/api/v1/search/search.py

@@ -31,9 +31,6 @@ async def search(
     only_context: bool = False,
     use_combined_context: bool = False,
     session_id: Optional[str] = None,
-    wide_search_top_k: Optional[int] = 100,
-    triplet_distance_penalty: Optional[float] = 3.5,
-    verbose: bool = False,
 ) -> Union[List[SearchResult], CombinedSearchResult]:
     """
     Search and query the knowledge graph for insights, information, and connections.
@@ -124,8 +121,6 @@ async def search(
 
         session_id: Optional session identifier for caching Q&A interactions. Defaults to 'default_session' if None.
 
-        verbose: If True, returns detailed result information including graph representation (when possible).
-
     Returns:
         list: Search results in format determined by query_type:
 
@@ -205,9 +200,6 @@ async def search(
         only_context=only_context,
         use_combined_context=use_combined_context,
         session_id=session_id,
-        wide_search_top_k=wide_search_top_k,
-        triplet_distance_penalty=triplet_distance_penalty,
-        verbose=verbose,
     )
 
     return filtered_search_results

cognee/api/v1/ui/node_setup.py

@@ -1,360 +0,0 @@
-import os
-import platform
-import subprocess
-import tempfile
-from pathlib import Path
-
-import requests
-
-from cognee.shared.logging_utils import get_logger
-
-logger = get_logger()
-
-
-def get_nvm_dir() -> Path:
-    """
-    Get the nvm directory path following standard nvm installation logic.
-    Uses XDG_CONFIG_HOME if set, otherwise falls back to ~/.nvm.
-    """
-    xdg_config_home = os.environ.get("XDG_CONFIG_HOME")
-    if xdg_config_home:
-        return Path(xdg_config_home) / "nvm"
-    return Path.home() / ".nvm"
-
-
-def get_nvm_sh_path() -> Path:
-    """
-    Get the path to nvm.sh following standard nvm installation logic.
-    """
-    return get_nvm_dir() / "nvm.sh"
-
-
-def check_nvm_installed() -> bool:
-    """
-    Check if nvm (Node Version Manager) is installed.
-    """
-    try:
-        # Check if nvm is available in the shell
-        # nvm is typically sourced in shell config files, so we need to check via shell
-        if platform.system() == "Windows":
-            # On Windows, nvm-windows uses a different approach
-            result = subprocess.run(
-                ["nvm", "version"],
-                capture_output=True,
-                text=True,
-                timeout=10,
-                shell=True,
-            )
-        else:
-            # On Unix-like systems, nvm is a shell function, so we need to source it
-            # First check if nvm.sh exists
-            nvm_path = get_nvm_sh_path()
-            if not nvm_path.exists():
-                logger.debug(f"nvm.sh not found at {nvm_path}")
-                return False
-
-            # Try to source nvm and check version, capturing errors
-            result = subprocess.run(
-                ["bash", "-c", f"source {nvm_path} && nvm --version"],
-                capture_output=True,
-                text=True,
-                timeout=10,
-            )
-
-            if result.returncode != 0:
-                # Log the error to help diagnose configuration issues
-                if result.stderr:
-                    logger.debug(f"nvm check failed: {result.stderr.strip()}")
-                return False
-
-        return result.returncode == 0
-    except Exception as e:
-        logger.debug(f"Exception checking nvm: {str(e)}")
-        return False
-
-
-def install_nvm() -> bool:
-    """
-    Install nvm (Node Version Manager) on Unix-like systems.
-    """
-    if platform.system() == "Windows":
-        logger.error("nvm installation on Windows requires nvm-windows.")
-        logger.error(
-            "Please install nvm-windows manually from: https://github.com/coreybutler/nvm-windows"
-        )
-        return False
-
-    logger.info("Installing nvm (Node Version Manager)...")
-
-    try:
-        # Download and install nvm
-        nvm_install_script = "https://raw.githubusercontent.com/nvm-sh/nvm/v0.40.3/install.sh"
-        logger.info(f"Downloading nvm installer from {nvm_install_script}...")
-
-        response = requests.get(nvm_install_script, timeout=60)
-        response.raise_for_status()
-
-        # Create a temporary script file
-        with tempfile.NamedTemporaryFile(mode="w", suffix=".sh", delete=False) as f:
-            f.write(response.text)
-            install_script_path = f.name
-
-        try:
-            # Make the script executable and run it
-            os.chmod(install_script_path, 0o755)
-            result = subprocess.run(
-                ["bash", install_script_path],
-                capture_output=True,
-                text=True,
-                timeout=120,
-            )
-
-            if result.returncode == 0:
-                logger.info("✓ nvm installed successfully")
-                # Source nvm in current shell session
-                nvm_dir = get_nvm_dir()
-                if nvm_dir.exists():
-                    return True
-                else:
-                    logger.warning(
-                        f"nvm installation completed but nvm directory not found at {nvm_dir}"
-                    )
-                    return False
-            else:
-                logger.error(f"nvm installation failed: {result.stderr}")
-                return False
-        finally:
-            # Clean up temporary script
-            try:
-                os.unlink(install_script_path)
-            except Exception:
-                pass
-
-    except requests.exceptions.RequestException as e:
-        logger.error(f"Failed to download nvm installer: {str(e)}")
-        return False
-    except Exception as e:
-        logger.error(f"Failed to install nvm: {str(e)}")
-        return False
-
-
-def install_node_with_nvm() -> bool:
-    """
-    Install the latest Node.js version using nvm.
-    Returns True if installation succeeds, False otherwise.
-    """
-    if platform.system() == "Windows":
-        logger.error("Node.js installation via nvm on Windows requires nvm-windows.")
-        logger.error("Please install Node.js manually from: https://nodejs.org/")
-        return False
-
-    logger.info("Installing latest Node.js version using nvm...")
-
-    try:
-        # Source nvm and install latest Node.js
-        nvm_path = get_nvm_sh_path()
-        if not nvm_path.exists():
-            logger.error(f"nvm.sh not found at {nvm_path}. nvm may not be properly installed.")
-            return False
-
-        nvm_source_cmd = f"source {nvm_path}"
-        install_cmd = f"{nvm_source_cmd} && nvm install node"
-
-        result = subprocess.run(
-            ["bash", "-c", install_cmd],
-            capture_output=True,
-            text=True,
-            timeout=300,  # 5 minutes timeout for Node.js installation
-        )
-
-        if result.returncode == 0:
-            logger.info("✓ Node.js installed successfully via nvm")
-
-            # Set as default version
-            use_cmd = f"{nvm_source_cmd} && nvm alias default node"
-            subprocess.run(
-                ["bash", "-c", use_cmd],
-                capture_output=True,
-                text=True,
-                timeout=30,
-            )
-
-            # Add nvm to PATH for current session
-            # This ensures node/npm are available in subsequent commands
-            nvm_dir = get_nvm_dir()
-            if nvm_dir.exists():
-                # Update PATH for current process
-                nvm_bin = nvm_dir / "versions" / "node"
-                # Find the latest installed version
-                if nvm_bin.exists():
-                    versions = sorted(nvm_bin.iterdir(), reverse=True)
-                    if versions:
-                        latest_node_bin = versions[0] / "bin"
-                        if latest_node_bin.exists():
-                            current_path = os.environ.get("PATH", "")
-                            os.environ["PATH"] = f"{latest_node_bin}:{current_path}"
-
-            return True
-        else:
-            logger.error(f"Failed to install Node.js: {result.stderr}")
-            return False
-
-    except subprocess.TimeoutExpired:
-        logger.error("Timeout installing Node.js (this can take several minutes)")
-        return False
-    except Exception as e:
-        logger.error(f"Error installing Node.js: {str(e)}")
-        return False
-
-
-def check_node_npm() -> tuple[bool, str]:  # (is_available, error_message)
-    """
-    Check if Node.js and npm are available.
-    If not available, attempts to install nvm and Node.js automatically.
-    """
-
-    try:
-        # Check Node.js - try direct command first, then with nvm if needed
-        result = subprocess.run(["node", "--version"], capture_output=True, text=True, timeout=10)
-        if result.returncode != 0:
-            # If direct command fails, try with nvm sourced (in case nvm is installed but not in PATH)
-            nvm_path = get_nvm_sh_path()
-            if nvm_path.exists():
-                result = subprocess.run(
-                    ["bash", "-c", f"source {nvm_path} && node --version"],
-                    capture_output=True,
-                    text=True,
-                    timeout=10,
-                )
-                if result.returncode != 0 and result.stderr:
-                    logger.debug(f"Failed to source nvm or run node: {result.stderr.strip()}")
-        if result.returncode != 0:
-            # Node.js is not installed, try to install it
-            logger.info("Node.js is not installed. Attempting to install automatically...")
-
-            # Check if nvm is installed
-            if not check_nvm_installed():
-                logger.info("nvm is not installed. Installing nvm first...")
-                if not install_nvm():
-                    return (
-                        False,
-                        "Failed to install nvm. Please install Node.js manually from https://nodejs.org/",
-                    )
-
-            # Install Node.js using nvm
-            if not install_node_with_nvm():
-                return (
-                    False,
-                    "Failed to install Node.js. Please install Node.js manually from https://nodejs.org/",
-                )
-
-            # Verify installation after automatic setup
-            # Try with nvm sourced first
-            nvm_path = get_nvm_sh_path()
-            if nvm_path.exists():
-                result = subprocess.run(
-                    ["bash", "-c", f"source {nvm_path} && node --version"],
-                    capture_output=True,
-                    text=True,
-                    timeout=10,
-                )
-                if result.returncode != 0 and result.stderr:
-                    logger.debug(
-                        f"Failed to verify node after installation: {result.stderr.strip()}"
-                    )
-            else:
-                result = subprocess.run(
-                    ["node", "--version"], capture_output=True, text=True, timeout=10
-                )
-            if result.returncode != 0:
-                nvm_path = get_nvm_sh_path()
-                return (
-                    False,
-                    f"Node.js installation completed but node command is not available. Please restart your terminal or source {nvm_path}",
-                )
-
-        node_version = result.stdout.strip()
-        logger.debug(f"Found Node.js version: {node_version}")
-
-        # Check npm - handle Windows PowerShell scripts
-        if platform.system() == "Windows":
-            # On Windows, npm might be a PowerShell script, so we need to use shell=True
-            result = subprocess.run(
-                ["npm", "--version"], capture_output=True, text=True, timeout=10, shell=True
-            )
-        else:
-            # On Unix-like systems, if we just installed via nvm, we may need to source nvm
-            # Try direct command first
-            result = subprocess.run(
-                ["npm", "--version"], capture_output=True, text=True, timeout=10
-            )
-            if result.returncode != 0:
-                # Try with nvm sourced
-                nvm_path = get_nvm_sh_path()
-                if nvm_path.exists():
-                    result = subprocess.run(
-                        ["bash", "-c", f"source {nvm_path} && npm --version"],
-                        capture_output=True,
-                        text=True,
-                        timeout=10,
-                    )
-                    if result.returncode != 0 and result.stderr:
-                        logger.debug(f"Failed to source nvm or run npm: {result.stderr.strip()}")
-
-        if result.returncode != 0:
-            return False, "npm is not installed or not in PATH"
-
-        npm_version = result.stdout.strip()
-        logger.debug(f"Found npm version: {npm_version}")
-
-        return True, f"Node.js {node_version}, npm {npm_version}"
-
-    except subprocess.TimeoutExpired:
-        return False, "Timeout checking Node.js/npm installation"
-    except FileNotFoundError:
-        # Node.js is not installed, try to install it
-        logger.info("Node.js is not found. Attempting to install automatically...")
-
-        # Check if nvm is installed
-        if not check_nvm_installed():
-            logger.info("nvm is not installed. Installing nvm first...")
-            if not install_nvm():
-                return (
-                    False,
-                    "Failed to install nvm. Please install Node.js manually from https://nodejs.org/",
-                )
-
-        # Install Node.js using nvm
-        if not install_node_with_nvm():
-            return (
-                False,
-                "Failed to install Node.js. Please install Node.js manually from https://nodejs.org/",
-            )
-
-        # Retry checking Node.js after installation
-        try:
-            result = subprocess.run(
-                ["node", "--version"], capture_output=True, text=True, timeout=10
-            )
-            if result.returncode == 0:
-                node_version = result.stdout.strip()
-                # Check npm
-                nvm_path = get_nvm_sh_path()
-                if nvm_path.exists():
-                    result = subprocess.run(
-                        ["bash", "-c", f"source {nvm_path} && npm --version"],
-                        capture_output=True,
-                        text=True,
-                        timeout=10,
-                    )
-                    if result.returncode == 0:
-                        npm_version = result.stdout.strip()
-                        return True, f"Node.js {node_version}, npm {npm_version}"
-                    elif result.stderr:
-                        logger.debug(f"Failed to source nvm or run npm: {result.stderr.strip()}")
-        except Exception as e:
-            logger.debug(f"Exception retrying node/npm check: {str(e)}")
-
-        return False, "Node.js/npm not found. Please install Node.js from https://nodejs.org/"
-    except Exception as e:
-        return False, f"Error checking Node.js/npm: {str(e)}"

cognee/api/v1/ui/npm_utils.py

@@ -1,50 +0,0 @@
-import platform
-import subprocess
-from pathlib import Path
-from typing import List
-
-from cognee.shared.logging_utils import get_logger
-from .node_setup import get_nvm_sh_path
-
-logger = get_logger()
-
-
-def run_npm_command(cmd: List[str], cwd: Path, timeout: int = 300) -> subprocess.CompletedProcess:
-    """
-    Run an npm command, ensuring nvm is sourced if needed (Unix-like systems only).
-    Returns the CompletedProcess result.
-    """
-    if platform.system() == "Windows":
-        # On Windows, use shell=True for npm commands
-        return subprocess.run(
-            cmd,
-            cwd=cwd,
-            capture_output=True,
-            text=True,
-            timeout=timeout,
-            shell=True,
-        )
-    else:
-        # On Unix-like systems, try direct command first
-        result = subprocess.run(
-            cmd,
-            cwd=cwd,
-            capture_output=True,
-            text=True,
-            timeout=timeout,
-        )
-        # If it fails and nvm might be installed, try with nvm sourced
-        if result.returncode != 0:
-            nvm_path = get_nvm_sh_path()
-            if nvm_path.exists():
-                nvm_cmd = f"source {nvm_path} && {' '.join(cmd)}"
-                result = subprocess.run(
-                    ["bash", "-c", nvm_cmd],
-                    cwd=cwd,
-                    capture_output=True,
-                    text=True,
-                    timeout=timeout,
-                )
-                if result.returncode != 0 and result.stderr:
-                    logger.debug(f"npm command failed with nvm: {result.stderr.strip()}")
-        return result

cognee/api/v1/ui/ui.py

@@ -15,8 +15,6 @@ import shutil
 
 from cognee.shared.logging_utils import get_logger
 from cognee.version import get_cognee_version
-from .node_setup import check_node_npm, get_nvm_dir, get_nvm_sh_path
-from .npm_utils import run_npm_command
 
 logger = get_logger()
 
@@ -287,6 +285,48 @@ def find_frontend_path() -> Optional[Path]:
     return None
 
 
+def check_node_npm() -> tuple[bool, str]:
+    """
+    Check if Node.js and npm are available.
+    Returns (is_available, error_message)
+    """
+
+    try:
+        # Check Node.js
+        result = subprocess.run(["node", "--version"], capture_output=True, text=True, timeout=10)
+        if result.returncode != 0:
+            return False, "Node.js is not installed or not in PATH"
+
+        node_version = result.stdout.strip()
+        logger.debug(f"Found Node.js version: {node_version}")
+
+        # Check npm - handle Windows PowerShell scripts
+        if platform.system() == "Windows":
+            # On Windows, npm might be a PowerShell script, so we need to use shell=True
+            result = subprocess.run(
+                ["npm", "--version"], capture_output=True, text=True, timeout=10, shell=True
+            )
+        else:
+            result = subprocess.run(
+                ["npm", "--version"], capture_output=True, text=True, timeout=10
+            )
+
+        if result.returncode != 0:
+            return False, "npm is not installed or not in PATH"
+
+        npm_version = result.stdout.strip()
+        logger.debug(f"Found npm version: {npm_version}")
+
+        return True, f"Node.js {node_version}, npm {npm_version}"
+
+    except subprocess.TimeoutExpired:
+        return False, "Timeout checking Node.js/npm installation"
+    except FileNotFoundError:
+        return False, "Node.js/npm not found. Please install Node.js from https://nodejs.org/"
+    except Exception as e:
+        return False, f"Error checking Node.js/npm: {str(e)}"
+
+
 def install_frontend_dependencies(frontend_path: Path) -> bool:
     """
     Install frontend dependencies if node_modules doesn't exist.
@@ -301,7 +341,24 @@ def install_frontend_dependencies(frontend_path: Path) -> bool:
     logger.info("Installing frontend dependencies (this may take a few minutes)...")
 
     try:
-        result = run_npm_command(["npm", "install"], frontend_path, timeout=300)
+        # Use shell=True on Windows for npm commands
+        if platform.system() == "Windows":
+            result = subprocess.run(
+                ["npm", "install"],
+                cwd=frontend_path,
+                capture_output=True,
+                text=True,
+                timeout=300,  # 5 minutes timeout
+                shell=True,
+            )
+        else:
+            result = subprocess.run(
+                ["npm", "install"],
+                cwd=frontend_path,
+                capture_output=True,
+                text=True,
+                timeout=300,  # 5 minutes timeout
+            )
 
         if result.returncode == 0:
             logger.info("Frontend dependencies installed successfully")
@@ -585,21 +642,6 @@ def start_ui(
     env["HOST"] = "localhost"
     env["PORT"] = str(port)
 
-    # If nvm is installed, ensure it's available in the environment
-    nvm_path = get_nvm_sh_path()
-    if platform.system() != "Windows" and nvm_path.exists():
-        # Add nvm to PATH for the subprocess
-        nvm_dir = get_nvm_dir()
-        # Find the latest Node.js version installed via nvm
-        nvm_versions = nvm_dir / "versions" / "node"
-        if nvm_versions.exists():
-            versions = sorted(nvm_versions.iterdir(), reverse=True)
-            if versions:
-                latest_node_bin = versions[0] / "bin"
-                if latest_node_bin.exists():
-                    current_path = env.get("PATH", "")
-                    env["PATH"] = f"{latest_node_bin}:{current_path}"
-
     # Start the development server
     logger.info(f"Starting frontend server at http://localhost:{port}")
     logger.info("This may take a moment to compile and start...")
@@ -617,26 +659,14 @@ def start_ui(
                 shell=True,
             )
         else:
-            # On Unix-like systems, use bash with nvm sourced if available
-            if nvm_path.exists():
-                # Use bash to source nvm and run npm
-                process = subprocess.Popen(
-                    ["bash", "-c", f"source {nvm_path} && npm run dev"],
-                    cwd=frontend_path,
-                    env=env,
-                    stdout=subprocess.PIPE,
-                    stderr=subprocess.PIPE,
-                    preexec_fn=os.setsid if hasattr(os, "setsid") else None,
-                )
-            else:
-                process = subprocess.Popen(
-                    ["npm", "run", "dev"],
-                    cwd=frontend_path,
-                    env=env,
-                    stdout=subprocess.PIPE,
-                    stderr=subprocess.PIPE,
-                    preexec_fn=os.setsid if hasattr(os, "setsid") else None,
-                )
+            process = subprocess.Popen(
+                ["npm", "run", "dev"],
+                cwd=frontend_path,
+                env=env,
+                stdout=subprocess.PIPE,
+                stderr=subprocess.PIPE,
+                preexec_fn=os.setsid if hasattr(os, "setsid") else None,
+            )
 
         # Start threads to stream frontend output with prefix
         _stream_process_output(process, "stdout", "[FRONTEND]", "\033[33m")  # Yellow

cognee/base_config.py

@@ -1,5 +1,4 @@
 import os
-from pathlib import Path
 from typing import Optional
 from functools import lru_cache
 from cognee.root_dir import get_absolute_path, ensure_absolute_path
@@ -12,9 +11,6 @@ class BaseConfig(BaseSettings):
     data_root_directory: str = get_absolute_path(".data_storage")
     system_root_directory: str = get_absolute_path(".cognee_system")
     cache_root_directory: str = get_absolute_path(".cognee_cache")
-    logs_root_directory: str = os.getenv(
-        "COGNEE_LOGS_DIR", str(os.path.join(os.path.dirname(os.path.dirname(__file__)), "logs"))
-    )
     monitoring_tool: object = Observer.NONE
 
     @pydantic.model_validator(mode="after")
@@ -34,8 +30,6 @@ class BaseConfig(BaseSettings):
         # Require absolute paths for root directories
         self.data_root_directory = ensure_absolute_path(self.data_root_directory)
         self.system_root_directory = ensure_absolute_path(self.system_root_directory)
-        self.logs_root_directory = ensure_absolute_path(self.logs_root_directory)
-
         # Set monitoring tool based on available keys
         if self.langfuse_public_key and self.langfuse_secret_key:
             self.monitoring_tool = Observer.LANGFUSE
@@ -55,7 +49,6 @@ class BaseConfig(BaseSettings):
             "system_root_directory": self.system_root_directory,
             "monitoring_tool": self.monitoring_tool,
             "cache_root_directory": self.cache_root_directory,
-            "logs_root_directory": self.logs_root_directory,
         }
 
 

cognee/cli/commands/cognify_command.py

@@ -22,7 +22,7 @@ relationships, and creates semantic connections for enhanced search and reasonin
 
 Processing Pipeline:
 1. **Document Classification**: Identifies document types and structures
-2. **Permission Validation**: Ensures user has processing rights
+2. **Permission Validation**: Ensures user has processing rights  
 3. **Text Chunking**: Breaks content into semantically meaningful segments
 4. **Entity Extraction**: Identifies key concepts, people, places, organizations
 5. **Relationship Detection**: Discovers connections between entities
@@ -97,13 +97,6 @@ After successful cognify processing, use `cognee search` to query the knowledge
                             chunker_class = LangchainChunker
                         except ImportError:
                             fmt.warning("LangchainChunker not available, using TextChunker")
-                    elif args.chunker == "CsvChunker":
-                        try:
-                            from cognee.modules.chunking.CsvChunker import CsvChunker
-
-                            chunker_class = CsvChunker
-                        except ImportError:
-                            fmt.warning("CsvChunker not available, using TextChunker")
 
                     result = await cognee.cognify(
                         datasets=datasets,

cognee/cli/config.py

@@ -26,7 +26,7 @@ SEARCH_TYPE_CHOICES = [
 ]
 
 # Chunker choices
-CHUNKER_CHOICES = ["TextChunker", "LangchainChunker", "CsvChunker"]
+CHUNKER_CHOICES = ["TextChunker", "LangchainChunker"]
 
 # Output format choices
 OUTPUT_FORMAT_CHOICES = ["json", "pretty", "simple"]

cognee/context_global_variables.py

@@ -4,10 +4,7 @@ from typing import Union
 from uuid import UUID
 
 from cognee.base_config import get_base_config
-from cognee.infrastructure.databases.vector.config import get_vectordb_config
-from cognee.infrastructure.databases.graph.config import get_graph_config
 from cognee.infrastructure.databases.utils import get_or_create_dataset_database
-from cognee.infrastructure.databases.utils import resolve_dataset_database_connection_info
 from cognee.infrastructure.files.storage.config import file_storage_config
 from cognee.modules.users.methods import get_user
 
@@ -22,67 +19,6 @@ async def set_session_user_context_variable(user):
     session_user.set(user)
 
 
-def multi_user_support_possible():
-    graph_db_config = get_graph_config()
-    vector_db_config = get_vectordb_config()
-
-    graph_handler = graph_db_config.graph_dataset_database_handler
-    vector_handler = vector_db_config.vector_dataset_database_handler
-    from cognee.infrastructure.databases.dataset_database_handler import (
-        supported_dataset_database_handlers,
-    )
-
-    if graph_handler not in supported_dataset_database_handlers:
-        raise EnvironmentError(
-            "Unsupported graph dataset to database handler configured. Cannot add support for multi-user access control mode. Please use a supported graph dataset to database handler or set the environment variables ENABLE_BACKEND_ACCESS_CONTROL to false to switch off multi-user access control mode.\n"
-            f"Selected graph dataset to database handler: {graph_handler}\n"
-            f"Supported dataset to database handlers: {list(supported_dataset_database_handlers.keys())}\n"
-        )
-
-    if vector_handler not in supported_dataset_database_handlers:
-        raise EnvironmentError(
-            "Unsupported vector dataset to database handler configured. Cannot add support for multi-user access control mode. Please use a supported vector dataset to database handler or set the environment variables ENABLE_BACKEND_ACCESS_CONTROL to false to switch off multi-user access control mode.\n"
-            f"Selected vector dataset to database handler: {vector_handler}\n"
-            f"Supported dataset to database handlers: {list(supported_dataset_database_handlers.keys())}\n"
-        )
-
-    if (
-        supported_dataset_database_handlers[graph_handler]["handler_provider"]
-        != graph_db_config.graph_database_provider
-    ):
-        raise EnvironmentError(
-            "The selected graph dataset to database handler does not work with the configured graph database provider. Cannot add support for multi-user access control mode. Please use a supported graph dataset to database handler or set the environment variables ENABLE_BACKEND_ACCESS_CONTROL to false to switch off multi-user access control mode.\n"
-            f"Selected graph database provider: {graph_db_config.graph_database_provider}\n"
-            f"Selected graph dataset to database handler: {graph_handler}\n"
-            f"Supported dataset to database handlers: {list(supported_dataset_database_handlers.keys())}\n"
-        )
-
-    if (
-        supported_dataset_database_handlers[vector_handler]["handler_provider"]
-        != vector_db_config.vector_db_provider
-    ):
-        raise EnvironmentError(
-            "The selected vector dataset to database handler does not work with the configured vector database provider. Cannot add support for multi-user access control mode. Please use a supported vector dataset to database handler or set the environment variables ENABLE_BACKEND_ACCESS_CONTROL to false to switch off multi-user access control mode.\n"
-            f"Selected vector database provider: {vector_db_config.vector_db_provider}\n"
-            f"Selected vector dataset to database handler: {vector_handler}\n"
-            f"Supported dataset to database handlers: {list(supported_dataset_database_handlers.keys())}\n"
-        )
-
-    return True
-
-
-def backend_access_control_enabled():
-    backend_access_control = os.environ.get("ENABLE_BACKEND_ACCESS_CONTROL", None)
-    if backend_access_control is None:
-        # If backend access control is not defined in environment variables,
-        # enable it by default if graph and vector DBs can support it, otherwise disable it
-        return multi_user_support_possible()
-    elif backend_access_control.lower() == "true":
-        # If enabled, ensure that the current graph and vector DBs can support it
-        return multi_user_support_possible()
-    return False
-
-
 async def set_database_global_context_variables(dataset: Union[str, UUID], user_id: UUID):
     """
     If backend access control is enabled this function will ensure all datasets have their own databases,
@@ -102,17 +38,16 @@ async def set_database_global_context_variables(dataset: Union[str, UUID], user_
 
     """
 
-    if not backend_access_control_enabled():
+    base_config = get_base_config()
+
+    if not os.getenv("ENABLE_BACKEND_ACCESS_CONTROL", "false").lower() == "true":
         return
 
     user = await get_user(user_id)
 
     # To ensure permissions are enforced properly all datasets will have their own databases
     dataset_database = await get_or_create_dataset_database(dataset, user)
-    # Ensure that all connection info is resolved properly
-    dataset_database = await resolve_dataset_database_connection_info(dataset_database)
 
-    base_config = get_base_config()
     data_root_directory = os.path.join(
         base_config.data_root_directory, str(user.tenant_id or user.id)
     )
@@ -121,31 +56,19 @@ async def set_database_global_context_variables(dataset: Union[str, UUID], user_
     )
 
     # Set vector and graph database configuration based on dataset database information
-    # TODO: Add better handling of vector and graph config accross Cognee.
-    #  LRU_CACHE takes into account order of inputs, if order of inputs is changed it will be registered as a new DB adapter
     vector_config = {
-        "vector_db_provider": dataset_database.vector_database_provider,
-        "vector_db_url": dataset_database.vector_database_url,
-        "vector_db_key": dataset_database.vector_database_key,
-        "vector_db_name": dataset_database.vector_database_name,
+        "vector_db_url": os.path.join(
+            databases_directory_path, dataset_database.vector_database_name
+        ),
+        "vector_db_key": "",
+        "vector_db_provider": "lancedb",
     }
 
     graph_config = {
-        "graph_database_provider": dataset_database.graph_database_provider,
-        "graph_database_url": dataset_database.graph_database_url,
-        "graph_database_name": dataset_database.graph_database_name,
-        "graph_database_key": dataset_database.graph_database_key,
+        "graph_database_provider": "kuzu",
         "graph_file_path": os.path.join(
             databases_directory_path, dataset_database.graph_database_name
         ),
-        "graph_database_username": dataset_database.graph_database_connection_info.get(
-            "graph_database_username", ""
-        ),
-        "graph_database_password": dataset_database.graph_database_connection_info.get(
-            "graph_database_password", ""
-        ),
-        "graph_dataset_database_handler": "",
-        "graph_database_port": "",
     }
 
     storage_config = {

cognee/eval_framework/Dockerfile

@@ -1,29 +0,0 @@
-FROM python:3.11-slim
-
-# Set environment variables
-ENV PIP_NO_CACHE_DIR=true
-ENV PATH="${PATH}:/root/.poetry/bin"
-ENV PYTHONPATH=/app
-ENV SKIP_MIGRATIONS=true
-
-# System dependencies
-RUN apt-get update && apt-get install -y \
-    gcc \
-    libpq-dev \
-    git \
-    curl \
-    build-essential \
-    && rm -rf /var/lib/apt/lists/*
-
-WORKDIR /app
-
-COPY pyproject.toml poetry.lock README.md /app/
-
-RUN pip install poetry
-
-RUN poetry config virtualenvs.create false
-
-RUN poetry install --extras distributed --extras evals --extras deepeval --no-root
-
-COPY cognee/ /app/cognee
-COPY distributed/ /app/distributed

cognee/eval_framework/answer_generation/answer_generation_executor.py

@@ -35,16 +35,6 @@ class AnswerGeneratorExecutor:
             retrieval_context = await retriever.get_context(query_text)
             search_results = await retriever.get_completion(query_text, retrieval_context)
 
-            ############
-            #:TODO This is a quick fix until we don't structure retriever results properly but lets not leave it like this...this is needed now due to the changed combined retriever structure..
-            if isinstance(retrieval_context, list):
-                retrieval_context = await retriever.convert_retrieved_objects_to_context(
-                    triplets=retrieval_context
-                )
-
-            if isinstance(search_results, str):
-                search_results = [search_results]
-            #############
             answer = {
                 "question": query_text,
                 "answer": search_results[0],

cognee/eval_framework/answer_generation/run_question_answering_module.py

@@ -35,7 +35,7 @@ async def create_and_insert_answers_table(questions_payload):
 
 
 async def run_question_answering(
-    params: dict, system_prompt="answer_simple_question_benchmark.txt", top_k: Optional[int] = None
+    params: dict, system_prompt="answer_simple_question.txt", top_k: Optional[int] = None
 ) -> List[dict]:
     if params.get("answering_questions"):
         logger.info("Question answering started...")

cognee/eval_framework/corpus_builder/task_getters/get_cascade_graph_tasks.py

@@ -8,6 +8,7 @@ from cognee.modules.users.models import User
 from cognee.shared.data_models import KnowledgeGraph
 from cognee.shared.utils import send_telemetry
 from cognee.tasks.documents import (
+    check_permissions_on_dataset,
     classify_documents,
     extract_chunks_from_documents,
 )
@@ -30,6 +31,7 @@ async def get_cascade_graph_tasks(
         cognee_config = get_cognify_config()
         default_tasks = [
             Task(classify_documents),
+            Task(check_permissions_on_dataset, user=user, permissions=["write"]),
             Task(
                 extract_chunks_from_documents, max_chunk_tokens=get_max_chunk_tokens()
             ),  # Extract text chunks based on the document type.

cognee/eval_framework/corpus_builder/task_getters/get_default_tasks_by_indices.py

@@ -30,8 +30,8 @@ async def get_no_summary_tasks(
     ontology_file_path=None,
 ) -> List[Task]:
     """Returns default tasks without summarization tasks."""
-    # Get base tasks (0=classify, 1=extract_chunks)
-    base_tasks = await get_default_tasks_by_indices([0, 1], chunk_size, chunker)
+    # Get base tasks (0=classify, 1=check_permissions, 2=extract_chunks)
+    base_tasks = await get_default_tasks_by_indices([0, 1, 2], chunk_size, chunker)
 
     ontology_adapter = RDFLibOntologyResolver(ontology_file=ontology_file_path)
 
@@ -51,8 +51,8 @@ async def get_just_chunks_tasks(
     chunk_size: int = None, chunker=TextChunker, user=None
 ) -> List[Task]:
     """Returns default tasks with only chunk extraction and data points addition."""
-    # Get base tasks (0=classify, 1=extract_chunks)
-    base_tasks = await get_default_tasks_by_indices([0, 1], chunk_size, chunker)
+    # Get base tasks (0=classify, 1=check_permissions, 2=extract_chunks)
+    base_tasks = await get_default_tasks_by_indices([0, 1, 2], chunk_size, chunker)
 
     add_data_points_task = Task(add_data_points, task_config={"batch_size": 10})