feat: add bedrock as supported llm provider (#1830)

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## Description
<!--
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.
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Added support for AWS Bedrock, and the models that are available there.
This was a contributor PR that was never finished, so now I polished it
up and made it work.

## Type of Change
<!-- Please check the relevant option -->
- [ ] Bug fix (non-breaking change that fixes an issue)
- [x] New feature (non-breaking change that adds functionality)
- [ ] Breaking change (fix or feature that would cause existing
functionality to change)
- [ ] Documentation update
- [ ] Code refactoring
- [ ] Performance improvement
- [ ] Other (please specify):

## Screenshots/Videos (if applicable)
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## Pre-submission Checklist
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- [x] **I have tested my changes thoroughly before submitting this PR**
- [x] **This PR contains minimal changes necessary to address the
issue/feature**
- [x] My code follows the project's coding standards and style
guidelines
- [x] I have added tests that prove my fix is effective or that my
feature works
- [x] I have added necessary documentation (if applicable)
- [ ] All new and existing tests pass
- [ ] I have searched existing PRs to ensure this change hasn't been
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- [ ] I have linked any relevant issues in the description
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## DCO Affirmation
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<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->

## Summary by CodeRabbit

* **New Features**
* Added AWS Bedrock as a new LLM provider with support for multiple
authentication methods.
* Integrated three new AI models: Claude 4.5 Sonnet, Claude 4.5 Haiku,
and Amazon Nova Lite.

<sub>✏️ Tip: You can customize this high-level summary in your review
settings.</sub>

<!-- end of auto-generated comment: release notes by coderabbit.ai -->

.github/workflows/test_llms.yml

@@ -84,3 +84,93 @@ 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

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"
+        - LLM_PROVIDER: "openai" (default), "anthropic", "gemini", "ollama", "mistral", "bedrock"
         - LLM_MODEL: Model name (default: "gpt-5-mini")
         - DEFAULT_USER_EMAIL: Custom default user email
         - DEFAULT_USER_PASSWORD: Custom default user password

cognee/infrastructure/files/storage/s3_config.py

@@ -9,6 +9,8 @@ class S3Config(BaseSettings):
     aws_access_key_id: Optional[str] = None
     aws_secret_access_key: Optional[str] = None
     aws_session_token: Optional[str] = None
+    aws_profile_name: Optional[str] = None
+    aws_bedrock_runtime_endpoint: Optional[str] = None
     model_config = SettingsConfigDict(env_file=".env", extra="allow")
 
 

cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/bedrock/__init__.py

@@ -0,0 +1,5 @@
+"""Bedrock LLM adapter module."""
+
+from .adapter import BedrockAdapter
+
+__all__ = ["BedrockAdapter"]

cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/bedrock/adapter.py

@@ -0,0 +1,153 @@
+import litellm
+import instructor
+from typing import Type
+from pydantic import BaseModel
+from litellm.exceptions import ContentPolicyViolationError
+from instructor.exceptions import InstructorRetryException
+
+from cognee.infrastructure.llm.LLMGateway import LLMGateway
+from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.llm_interface import (
+    LLMInterface,
+)
+from cognee.infrastructure.llm.exceptions import (
+    ContentPolicyFilterError,
+    MissingSystemPromptPathError,
+)
+from cognee.infrastructure.files.storage.s3_config import get_s3_config
+from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.rate_limiter import (
+    rate_limit_async,
+    rate_limit_sync,
+    sleep_and_retry_async,
+    sleep_and_retry_sync,
+)
+from cognee.modules.observability.get_observe import get_observe
+
+observe = get_observe()
+
+
+class BedrockAdapter(LLMInterface):
+    """
+    Adapter for AWS Bedrock API with support for three authentication methods:
+    1. API Key (Bearer Token)
+    2. AWS Credentials (access key + secret key)
+    3. AWS Profile (boto3 credential chain)
+    """
+
+    name = "Bedrock"
+    model: str
+    api_key: str
+    default_instructor_mode = "json_schema_mode"
+
+    MAX_RETRIES = 5
+
+    def __init__(
+        self,
+        model: str,
+        api_key: str = None,
+        max_completion_tokens: int = 16384,
+        streaming: bool = False,
+        instructor_mode: str = None,
+    ):
+        self.instructor_mode = instructor_mode if instructor_mode else self.default_instructor_mode
+
+        self.aclient = instructor.from_litellm(
+            litellm.acompletion, mode=instructor.Mode(self.instructor_mode)
+        )
+        self.client = instructor.from_litellm(litellm.completion)
+        self.model = model
+        self.api_key = api_key
+        self.max_completion_tokens = max_completion_tokens
+        self.streaming = streaming
+
+    def _create_bedrock_request(
+        self, text_input: str, system_prompt: str, response_model: Type[BaseModel]
+    ) -> dict:
+        """Create Bedrock request with authentication."""
+
+        request_params = {
+            "model": self.model,
+            "custom_llm_provider": "bedrock",
+            "drop_params": True,
+            "messages": [
+                {"role": "user", "content": text_input},
+                {"role": "system", "content": system_prompt},
+            ],
+            "response_model": response_model,
+            "max_retries": self.MAX_RETRIES,
+            "max_completion_tokens": self.max_completion_tokens,
+            "stream": self.streaming,
+        }
+
+        s3_config = get_s3_config()
+
+        # Add authentication parameters
+        if self.api_key:
+            request_params["api_key"] = self.api_key
+        elif s3_config.aws_access_key_id and s3_config.aws_secret_access_key:
+            request_params["aws_access_key_id"] = s3_config.aws_access_key_id
+            request_params["aws_secret_access_key"] = s3_config.aws_secret_access_key
+            if s3_config.aws_session_token:
+                request_params["aws_session_token"] = s3_config.aws_session_token
+        elif s3_config.aws_profile_name:
+            request_params["aws_profile_name"] = s3_config.aws_profile_name
+
+        if s3_config.aws_region:
+            request_params["aws_region_name"] = s3_config.aws_region
+
+        # Add optional parameters
+        if s3_config.aws_bedrock_runtime_endpoint:
+            request_params["aws_bedrock_runtime_endpoint"] = s3_config.aws_bedrock_runtime_endpoint
+
+        return request_params
+
+    @observe(as_type="generation")
+    @sleep_and_retry_async()
+    @rate_limit_async
+    async def acreate_structured_output(
+        self, text_input: str, system_prompt: str, response_model: Type[BaseModel]
+    ) -> BaseModel:
+        """Generate structured output from AWS Bedrock API."""
+
+        try:
+            request_params = self._create_bedrock_request(text_input, system_prompt, response_model)
+            return await self.aclient.chat.completions.create(**request_params)
+
+        except (
+            ContentPolicyViolationError,
+            InstructorRetryException,
+        ) as error:
+            if (
+                isinstance(error, InstructorRetryException)
+                and "content management policy" not in str(error).lower()
+            ):
+                raise error
+
+            raise ContentPolicyFilterError(
+                f"The provided input contains content that is not aligned with our content policy: {text_input}"
+            )
+
+    @observe
+    @sleep_and_retry_sync()
+    @rate_limit_sync
+    def create_structured_output(
+        self, text_input: str, system_prompt: str, response_model: Type[BaseModel]
+    ) -> BaseModel:
+        """Generate structured output from AWS Bedrock API (synchronous)."""
+
+        request_params = self._create_bedrock_request(text_input, system_prompt, response_model)
+        return self.client.chat.completions.create(**request_params)
+
+    def show_prompt(self, text_input: str, system_prompt: str) -> str:
+        """Format and display the prompt for a user query."""
+        if not text_input:
+            text_input = "No user input provided."
+        if not system_prompt:
+            raise MissingSystemPromptPathError()
+        system_prompt = LLMGateway.read_query_prompt(system_prompt)
+
+        formatted_prompt = (
+            f"""System Prompt:\n{system_prompt}\n\nUser Input:\n{text_input}\n"""
+            if system_prompt
+            else None
+        )
+        return formatted_prompt

cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/get_llm_client.py

@@ -24,6 +24,7 @@ class LLMProvider(Enum):
     - CUSTOM: Represents a custom provider option.
     - GEMINI: Represents the Gemini provider.
     - MISTRAL: Represents the Mistral AI provider.
+    - BEDROCK: Represents the AWS Bedrock provider.
     """
 
     OPENAI = "openai"
@@ -32,6 +33,7 @@ class LLMProvider(Enum):
     CUSTOM = "custom"
     GEMINI = "gemini"
     MISTRAL = "mistral"
+    BEDROCK = "bedrock"
 
 
 def get_llm_client(raise_api_key_error: bool = True):
@@ -154,7 +156,7 @@ def get_llm_client(raise_api_key_error: bool = True):
         )
 
     elif provider == LLMProvider.MISTRAL:
-        if llm_config.llm_api_key is None:
+        if llm_config.llm_api_key is None and raise_api_key_error:
             raise LLMAPIKeyNotSetError()
 
         from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.mistral.adapter import (
@@ -169,5 +171,21 @@ def get_llm_client(raise_api_key_error: bool = True):
             instructor_mode=llm_config.llm_instructor_mode.lower(),
         )
 
+    elif provider == LLMProvider.BEDROCK:
+        # if llm_config.llm_api_key is None and raise_api_key_error:
+        #     raise LLMAPIKeyNotSetError()
+
+        from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.bedrock.adapter import (
+            BedrockAdapter,
+        )
+
+        return BedrockAdapter(
+            model=llm_config.llm_model,
+            api_key=llm_config.llm_api_key,
+            max_completion_tokens=max_completion_tokens,
+            streaming=llm_config.llm_streaming,
+            instructor_mode=llm_config.llm_instructor_mode.lower(),
+        )
+
     else:
         raise UnsupportedLLMProviderError(provider)

cognee/modules/settings/get_settings.py

@@ -16,6 +16,7 @@ class ModelName(Enum):
     anthropic = "anthropic"
     gemini = "gemini"
     mistral = "mistral"
+    bedrock = "bedrock"
 
 
 class LLMConfig(BaseModel):
@@ -77,6 +78,10 @@ def get_settings() -> SettingsDict:
             "value": "mistral",
             "label": "Mistral",
         },
+        {
+            "value": "bedrock",
+            "label": "Bedrock",
+        },
     ]
 
     return SettingsDict.model_validate(
@@ -157,6 +162,20 @@ def get_settings() -> SettingsDict:
                             "label": "Mistral Large 2.1",
                         },
                     ],
+                    "bedrock": [
+                        {
+                            "value": "eu.anthropic.claude-sonnet-4-5-20250929-v1:0",
+                            "label": "Claude 4.5 Sonnet",
+                        },
+                        {
+                            "value": "eu.anthropic.claude-haiku-4-5-20251001-v1:0",
+                            "label": "Claude 4.5 Haiku",
+                        },
+                        {
+                            "value": "eu.amazon.nova-lite-v1:0",
+                            "label": "Amazon Nova Lite",
+                        },
+                    ],
                 },
             },
             vector_db={