M
memgraph/ai-toolkit
Agent memory and GraphRAG on Memgraph
MIT license
View on GitHub ↗Link checked 2026-11-12 · HTTP 200 · opens the vendor's own site
Install & config
Client config (built from the install command this repo gives)
{
"mcpServers": {
"memgraph-ai-toolkit": {
"command": "uvx",
"args": ["ruff@0.16.0"]
}
}
}Parsed from the README we fetched on 2026-11-04; we have not executed it.
command/args come from the install command the repository states. When it is ambiguous, the project README wins.
VS Code (.vscode/mcp.json - the key is servers)
{
"servers": {
"memgraph-ai-toolkit": {
"command": "uvx",
"args": ["ruff@0.16.0"]
}
}
}Same server, but VS Code keeps it under <code>servers</code>.
Claude Code (command line)
claude mcp add --transport stdio memgraph-ai-toolkit -- uvx ruff@0.16.0
Claude Code does not read a config file; add the server with this command.
Key names differ by client: Clients & CLI
- Source
- collected from the abordage/awesome-mcp list · type: MCP server
- Harvest history
- first seen 2026-11-05 · last seen 2026-11-14 · seen 10× · listed by 10 sources
- Score
- score 1,003 · 10 sources · mcp
- Install
uvx ruff@0.16.0
FAQ (answers come from our records)
How do I install it?
The repository provides this install command:
uvx ruff@0.16.0. It comes from the repository itself; we cannot guarantee it runs as-is in your client.Who is it for?
We filed it under these tags: MCP 服务. The tags come from the entry itself, not a taxonomy we imposed.
Who maintains it?
first catalogued 2026-11-05；last seen 2026-11-14。There is no official certification or quality rating here — we record sources and dates only.
Third-party quality signals
# Memgraph AI Toolkit
以下摘录自该仓库的 README（片段） · 在 GitHub 读完整 README →
Python packages for building AI applications with Memgraph: agent memory, knowledge graphs, GraphRAG, and framework integrations.
Use the packages independently, or combine them to bring documents, databases, and agent activity into a graph.
Documentation · Examples · Discord · Issues
## Choose your starting point
| I want to… | Start here |
| Give coding agents memory across sessions and tools | Context Graph |
| Extract entities and relationships from documents | unstructured2graph |
| Query Memgraph in natural language or build a LangChain agent | langchain-memgraph |
| Connect an MCP client to Memgraph | mcp-memgraph |
| Use Memgraph as LightRAG storage | lightrag-memgraph |
| Migrate a MySQL or PostgreSQL database into a graph | sql2graph |
| Work directly with Memgraph from Python | memgraph-toolbox |
| Define and validate a graph's entity and relation types | hygm |
## Agent memory with Context Graph
Keep what your agents learn in a database you control. Context Graph captures sessions, tool calls, and skill usage, stores explicit memory, and retrieves relevant past conversations for the next session.
It supports Claude Code, Codex, Copilot CLI, Cursor, OpenCode, Antigravity CLI, and Grok Build, plus applications built with the Claude Agent SDK or OpenAI Agents SDK.
bash
curl -fsSL https://raw.githubusercontent.com/memgraph/ai-toolkit/main/context-graph/scripts/install.sh | bash
Setup installs the CLI and connectors, starts its own Memgraph container or connects to one you specify, and wires the agents you select. Docker is needed if setup starts Memgraph for you.
Get started with Context Graph · Components · Recall benchmarks
## Knowledge graphs from documents
unstructured2graph parses documents and extracts entities and relationships. Its default backend, GLiNER2, runs locally without an LLM API key; LightRAG is available as an LLM-based alternative.
Start Memgraph:
bash
docker run -p 7687:7687 --name memgraph \
memgraph/memgraph-mage:latest --schema-info-enabled=true
Install the package:
bash
pip install unstructured2graph
Extract a graph from text:
python
import asyncio
from memgraphtoolbox.api.memgraph import Memgraph
from unstructured2graph import fromtexts
from unstructured2graph.gliner2backend import GLiNER2Backend
async def main():
await fromtexts(
texts=["Alice works for Acme in London."],
memgraph=Memgraph(),
extractionbackend=GLiNER2Backend(),
enforceontology=True,
asyncio.run(main())
The extraction model downloads on first use. See unstructured2graph for files, URLs, document-format extras, and custom ontologies.
## Framework integrations
Install only the integration you need:
bash
pip install langchain-memgraph # graph store, QA chain, and agent toolkit
pip install mcp-memgraph # MCP server for database queries and schema tools
pip install lightrag-memgraph # LightRAG storage backends
Use the packages independently, or combine them to bring documents, databases, and agent activity into a graph.
Documentation · Examples · Discord · Issues
## Choose your starting point
| I want to… | Start here |
| Give coding agents memory across sessions and tools | Context Graph |
| Extract entities and relationships from documents | unstructured2graph |
| Query Memgraph in natural language or build a LangChain agent | langchain-memgraph |
| Connect an MCP client to Memgraph | mcp-memgraph |
| Use Memgraph as LightRAG storage | lightrag-memgraph |
| Migrate a MySQL or PostgreSQL database into a graph | sql2graph |
| Work directly with Memgraph from Python | memgraph-toolbox |
| Define and validate a graph's entity and relation types | hygm |
## Agent memory with Context Graph
Keep what your agents learn in a database you control. Context Graph captures sessions, tool calls, and skill usage, stores explicit memory, and retrieves relevant past conversations for the next session.
It supports Claude Code, Codex, Copilot CLI, Cursor, OpenCode, Antigravity CLI, and Grok Build, plus applications built with the Claude Agent SDK or OpenAI Agents SDK.
bash
curl -fsSL https://raw.githubusercontent.com/memgraph/ai-toolkit/main/context-graph/scripts/install.sh | bash
Setup installs the CLI and connectors, starts its own Memgraph container or connects to one you specify, and wires the agents you select. Docker is needed if setup starts Memgraph for you.
Get started with Context Graph · Components · Recall benchmarks
## Knowledge graphs from documents
unstructured2graph parses documents and extracts entities and relationships. Its default backend, GLiNER2, runs locally without an LLM API key; LightRAG is available as an LLM-based alternative.
Start Memgraph:
bash
docker run -p 7687:7687 --name memgraph \
memgraph/memgraph-mage:latest --schema-info-enabled=true
Install the package:
bash
pip install unstructured2graph
Extract a graph from text:
python
import asyncio
from memgraphtoolbox.api.memgraph import Memgraph
from unstructured2graph import fromtexts
from unstructured2graph.gliner2backend import GLiNER2Backend
async def main():
await fromtexts(
texts=["Alice works for Acme in London."],
memgraph=Memgraph(),
extractionbackend=GLiNER2Backend(),
enforceontology=True,
asyncio.run(main())
The extraction model downloads on first use. See unstructured2graph for files, URLs, document-format extras, and custom ontologies.
## Framework integrations
Install only the integration you need:
bash
pip install langchain-memgraph # graph store, QA chain, and agent toolkit
pip install mcp-memgraph # MCP server for database queries and schema tools
pip install lightrag-memgraph # LightRAG storage backends
Listed badge (optional)
Copy this line into the README if you want the listing mark. Optional, and it does not affect being listed.
[](https://anclum.com/en/s/memgraph-ai-toolkit/)
- Source
- awesome · https://github.com/memgraph/ai-toolkit
- Browse by field
- MCP servers
- Listed by
- 10 independent sources
- added
- today
More like this
Filed under
Every tag opens the full list for that filter. Counts are the live catalogue figures.