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+# Project Guide for AI Agents
+
+This Agments.md file provides operational guidance for AI assistants collaborating on the LightRAG codebase. Use it to understand the repository layout, preferred tooling, and expectations for adding or modifying functionality.
+
+## Core Purpose
+
+LightRAG is an advanced Retrieval-Augmented Generation (RAG) framework designed to enhance information retrieval and generation through graph-based knowledge representation. The project aims to provide a more intelligent and efficient way to process and retrieve information from documents by leveraging both graph structures and vector embeddings.
+
+## Project Structure for Navigation
+
+- `/lightrag`: Core Python package (ingestion, querying, storage abstractions, utilities). Key modules include `lightrag/lightrag.py` orchestration, `operate.py` pipeline helpers, `kg/` storage backends, `llm/` bindings, and `utils*.py`.
+- `/lightrag/api`: FastAPI with Gunicorn for production. FastAPI service for LightRAG , auth, WebUI assets live in  `lightrag_server.py`. Routers live in `routers/`, shared helpers in `utils_api.py`. Gunicorn startup logic lives in `run_with_gunicorn.py`.
+- `/lightrag_webui`: React 19 + TypeScript + Tailwind front-end built with Vite/Bun. Uses component folders under `src/` and configuration via `env.*.sample`.
+- `/inputs`, `/rag_storage`, `/dickens`, `/temp`: data directories. Treat contents as mutable working data; avoid committing generated artefacts.
+- `/tests` and root-level `test_*.py`: Integration and smoke-test scripts (graph storage, API endpoints, behaviour regressions). Many expect specific environment variables or services.
+- `/docs`, `/k8s-deploy`, `docker-compose.yml`: Deployment notes, Kubernetes manifests, and container orchestration helpers.
+- Configuration templates: `.env.example`, `config.ini.example`, `lightrag.service.example`. Copy and adapt for local runs without committing secrets.
+
+## Environment Setup and Tooling
+
+- Python 3.10 is required. Recommended bootstrap:
+
+  ```bash
+  # Development installation
+  python -m venv .venv
+  source .venv/bin/activate
+  pip install -e .
+  pip install -e .[api]
+
+  # Start API server
+  lightrag-server
+
+  # Production deployment
+  lightrag-gunicorn --workers 3
+  ```
+
+- Duplicate `.env.example` to `.env` and adjust storage, LLM, and reranker bindings. Mirror `config.ini.example` when customising pipeline defaults.
+- Storage backends (PostgreSQL, Redis, Neo4j, Milvus, etc.) are selected via `LIGHTRAG_*` environment variables. Ensure connection URLs and credentials are in place before running ingestion or tests.
+- CLI entry points: `python -m lightrag` for package usage, `lightrag-server` (or `uvicorn lightrag.api.lightrag_server:app --reload`) for the API, `lightrag-gunicorn` for production gunicorn runs.
+- Front-end work: install dependencies with `bun install` (preferred) or `npm install`, then use `bunx --bun vite` commands defined in `package.json`.
+
+## Frontend Development
+
+* **Package Manager**: **ALWAYS USE BUN** - Never use npm or yarn unless Bun is unavailable
+  **Commands**:
+
+  - `bun install` - Install dependencies
+
+  - `bun run dev` - Start development server
+
+  - `bun run build` - Build for production
+
+  - `bun run lint` - Run linting
+
+  - `bun test` - Run tests
+
+  - `bun run preview` - Preview production build
+
+
+* **Pattern**: All frontend operations must use Bun commands
+* **Testing**: Use `bun test` for all frontend testing
+
+## Coding Conventions
+
+- Embrace type hints, dataclasses, and asynchronous patterns already present in `lightrag/lightrag.py` and storage implementations. Keep long-running jobs within `asyncio` flows and reuse helpers from `lightrag.operate`.
+- Honour abstraction boundaries: new storage providers should inherit from the relevant base classes in `lightrag.base`; reusable logic belongs in `utils.py`/`utils_graph.py`.
+- Use `lightrag.utils.logger` (not bare `print`) and let environment toggles (`VERBOSE`, `LOG_LEVEL`) control verbosity.
+- Respect configuration defaults in `lightrag/constants.py`, extending with care and synchronising related documentation when behaviour changes.
+- API additions should live under `lightrag/api/routers`, leverage dependency injections from `utils_api.py`, and return structured responses consistent with existing handlers.
+- Front-end code should remain in TypeScript, rely on functional React components with hooks, and follow Tailwind utility style. Co-locate component-specific styles; reserve custom CSS for cases Tailwind cannot cover.
+- Storage Backends
+  - **Default**: In-memory with file persistence
+  - **Production Options**: PostgreSQL, MongoDB, Redis, Neo4j
+  - **Pattern**: Abstract storage interface with multiple implementations
+
+* Lock Key Generation Consistency
+  - **Critical Pattern**: Always sort parameters for lock key generation to prevent deadlocks
+  - **Example**: `sorted_key_parts = sorted([src, tgt])` before creating lock key
+  - **Why**: Prevents different lock keys for same relationship pair processed in different orders
+  - **Apply to**: Any function that uses locks with multiple parameters
+* Priority Queue Implementation
+  * **Pattern**: Use priority-based task queuing for LLM requests
+  * **Benefits**: Critical operations get higher priority
+  * **Implementation**: Lower priority values = higher priority
+
+## Testing and Quality Gates
+
+- Run Python tests with `python -m pytest tests` for the FastAPI suite, and execute targeted scripts (for example `python tests/test_graph_storage.py`, `python test_lock_fix.py`) when touching related functionality. Many scripts require running backing services; check `.env` for prerequisites.
+- Perform linting via `ruff check .` (configured in `pyproject.toml`) and address warnings. For formatting, match the existing style rather than introducing new tools.
+- Front-end validation: `bun test`, `bunx --bun vite build`, and `bunx --bun vite lint`. The `*-no-bun` scripts exist if Bun is unavailable.
+- When touching deployment assets, ensure `docker-compose config` or relevant `kubectl` dry-runs succeed before submitting changes.
+
+## Runtime and Operational Notes
+
+- Knowledge ingestion expects documents inside `inputs/` and writes intermediate state to `rag_storage/`. Keep these directories gitignored; never check in private data or large artefacts.
+- Use `operate.py` helpers (e.g., `chunking_by_token_size`, `extract_entities`) to keep ingestion behaviour consistent. If extending the pipeline, document new steps in `docs/` and update any affected CLI usage.
+- The API and core package rely on `.env`/`config.ini` being co-located with the current working directory. Scripts such as `tests/test_graph_storage.py` dynamically read these files; ensure they are in sync.
+
+## Contribution Checklist
+
+1. Run `pre-commit run --all-files` before sumitting PR.
+2. Describe the change, affected modules, and operational impact in your PR. Mention any new environment knobs or storage dependencies.
+3. Link related issues or discussions when available.
+4. Confirm all applicable checks pass (`ruff`, pytest suite, targeted integration scripts, front-end build/tests when touched).
+5. Capture screenshots or GIFs for front-end or API changes that affect user-visible behaviour.
+6. Keep each PR focused on a single concern and update documentation (`README.md`, `docs/`, `.env.example`) when behaviour or configuration changes.
+
+Follow this playbook to keep LightRAG contributions predictable, testable, and production-ready.