🧠 AgensGraph AI Tools and Libraries
Welcome to AgensGraph AI — a curated collection of tools, integrations, and starter templates for building AI-powered applications that work with AgensGraph, a PostgreSQL-based multi-model graph database.
This repository currently includes:
- LangChain — LLM agents, tools, and chains
- LlamaIndex — custom data indexing and retrieval
- LightRag — graph-aware RAG for accurate, context-rich retrieval
- cognee — dynamic memory for Agents
- mcp — Model Context Protocol server for AgensGraph enabling database access and graph exploration.
✅ Each library has its own subfolder with a dedicated README to guide you through setup and usage.
📦 Installation
The agensgraph-ai package installs any combination of the integrations under one name.
Pick the ones you need:
pip install "agensgraph-ai[langchain]"
pip install "agensgraph-ai[langchain,lightrag]"
pip install "agensgraph-ai[all]"
| Extra | Installs | 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 |
the three mcp-agensgraph-* servers |
run as commands |
all |
all of the above |
Name at least one extra. pip install agensgraph-ai on its own installs no integrations,
and an extra that is misspelled installs none either — pip warns about that, uv does
not.
Each integration is also released on its own, so it can be installed by name instead. The
two forms produce the same environment — agensgraph-ai ships no code, and is a
convenience rather than a layer:
pip install langchain-agensgraph
pip install llama-index-agensgraph
pip install lightrag-agensgraph
pip install cognee-agensgraph
The MCP servers are commands rather than libraries, and an MCP client normally launches
them itself with uvx, which needs no install at all:
"mcpServers": {
"agensgraph-cypher": {
"command": "uvx",
"args": ["mcp-agensgraph-cypher@0.3.0", "--transport", "stdio"]
}
}
The mcp extra is for the other case: hosting a server yourself over HTTP or SSE.
Database requirements
The Python install is only half of the setup. These integrations talk to a running
AgensGraph, 2.17 or newer, and the vector-backed features need the pgvector
and meta extensions, which AgensGraph does not bundle — see
langchain/README.md for how to build and
enable them. SHOW agversion tells you which you have. From 2.18 a release reports four
numbers, such as 2.18.4.0, with -rc1 on a release candidate; the first two are the line,
so 2.18.6.0-rc1 is a 2.18 server.
🎯 Purpose
This repository is designed to help developers:
- Integrate AgensGraph with modern LLM frameworks
- Leverage graph data in conversational and intelligent apps
- Explore Retrieval-Augmented Generation (RAG), agents, and graph reasoning
Everything is open-source and modular — feel free to use, fork, or contribute.
📄 License
This repository is licensed under the Apache License 2.0.
📬 Contact
For questions, feature requests, or collaboration:
- Open an Issue or Pull Request
Release files for agensgraph-ai 0.3.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| agensgraph_ai-0.3.0.tar.gz | 7.1 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| agensgraph_ai-0.3.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 14.2 kB
Release files / agensgraph_ai-0.3.0.tar.gz
| Download URL | agensgraph_ai-0.3.0.tar.gz |
|---|---|
| Size | 7.1 kB |
| Tags | Source |
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Release files / agensgraph_ai-0.3.0-py3-none-any.whl
| Download URL | agensgraph_ai-0.3.0-py3-none-any.whl |
|---|---|
| Size | 7.1 kB |
| Tags | Python 3 |
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| Uploaded via |
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