AgensGraph AI Tools and Libraries
====================================

Welcome to `AgensGraph AI <https://github.com/skaiworldwide-oss/agensgraph-ai>`_ — a
collection of integrations that connect popular AI and LLM frameworks to
`AgensGraph <https://github.com/skaiworldwide-oss/agensgraph>`_, Skai's PostgreSQL-based
multi-model graph database. Each integration lets you keep your knowledge graphs **and**
your vector embeddings in a single AgensGraph instance, queried with Cypher over the
standard PostgreSQL wire protocol.

Libraries at a glance
---------------------

.. list-table::
   :header-rows: 1
   :widths: 22 78

   * - Library
     - What it gives you
   * - :doc:`LangChain <langchain/index>`
     - Graph store, vector store, chat-message history, a LangGraph checkpointer, and an
       LLM graph transformer for LangChain 1.x.
   * - :doc:`LlamaIndex <llamaindex/index>`
     - ``PropertyGraphIndex`` and ``VectorStoreIndex`` stores, with hybrid search, metadata
       filtering, and AgensGraph-tuned Text2Cypher.
   * - :doc:`LightRAG <lightrag/index>`
     - All four LightRAG storage roles on one database, with workspace multi-tenancy.
   * - :doc:`cognee <cognee/index>`
     - Graph and vector adapters for the cognee AI-memory framework.
   * - :doc:`MCP servers <mcp/index>`
     - Three MCP servers — Cypher, memory, and data modeling — that expose AgensGraph to
       clients like Claude Desktop.

Installing
----------

Each integration is released on PyPI on its own, and the ``agensgraph-ai`` package installs
any combination of them under one name. Name the ones you need as extras:

.. code-block:: bash

   pip install "agensgraph-ai[langchain]"
   pip install "agensgraph-ai[langchain,lightrag]"
   pip install "agensgraph-ai[all]"

.. list-table::
   :header-rows: 1
   :widths: 18 42 40

   * - Extra
     - Installs
     - Then import
   * - ``langchain``
     - ``langchain-agensgraph``
     - ``langchain_agensgraph``
   * - ``llama-index``
     - ``llama-index-agensgraph``
     - ``llama_index_agensgraph``
   * - ``lightrag``
     - ``lightrag-agensgraph``
     - ``lightrag_agensgraph``
   * - ``cognee``
     - ``cognee-agensgraph``
     - ``cognee_agensgraph``
   * - ``mcp``
     - ``mcp-agensgraph-cypher``, ``mcp-agensgraph-memory``, ``mcp-agensgraph-data-modeling``
     - run as commands
   * - ``all``
     - all of the above
     -

* **Name at least one extra.** ``pip install agensgraph-ai`` on its own installs nothing, and a
  misspelled extra installs nothing either: ``pip`` and ``uv`` only print a warning and still
  succeed.
* There is no ``agensgraph_ai`` module. Each extra is a shortcut to the real package, which you
  import under its own name, and ``pip install langchain-agensgraph`` produces the same
  environment as ``pip install "agensgraph-ai[langchain]"``.
* The ``mcp`` extra is for hosting a server yourself over HTTP or SSE. An MCP client normally
  launches the servers with ``uvx``, which needs no install (see :doc:`mcp/installation`).
* Python 3.11 or later; the LightRAG and cognee integrations (and so ``[all]``) need 3.11
  to 3.13.

A common foundation
-------------------

All of these integrations share the same underlying model:

* **One database for graph and vectors.** They talk to AgensGraph through the
  `agensgraph-python <https://pypi.org/project/agensgraph-python/>`_ 2.0 driver, which runs on
  `psycopg 3 <https://www.psycopg.org/psycopg3/>`_, and run Cypher inside it, so a single
  instance can hold both your property graph and your embeddings.
* **pgvector for embeddings.** Vector search uses the `pgvector
  <https://github.com/pgvector/pgvector>`_ extension. AgensGraph does not bundle it, so you
  install it once into your database (see each library's *Installation* page).
* **AgensGraph 2.17 or later is required.** The driver refuses an older server at connect.
  ``SHOW agversion`` tells you which you have; from 2.18 a release reports four numbers, such
  as ``2.18.6.0``, and the first two are the line.

Choosing an integration
------------------------

* Already building in **LangChain** or **LlamaIndex**? Use the matching integration — it
  plugs into that framework's standard graph-store / vector-store interfaces.
* Want a **batteries-included graph-RAG or memory pipeline**? **LightRAG** and **cognee**
  handle ingestion, extraction, and retrieval for you.
* Want an **LLM agent (e.g. Claude Desktop) to use AgensGraph directly** through tools,
  with no Python glue? Use the **MCP servers**.

.. note::

   The source for every integration lives in the
   `agensgraph-ai repository <https://github.com/skaiworldwide-oss/agensgraph-ai>`_, and
   each library ships a runnable demo suite with pre-executed notebooks. Links to those
   demos appear on each library's *Usage* page.

.. toctree::
   :maxdepth: 3
   :hidden:

   langchain/index
   llamaindex/index
   lightrag/index
   cognee/index
   mcp/index