M
memgraph/ai-toolkit
Agent memory and GraphRAG on Memgraph
MIT 许可证
在 GitHub 查看 ↗链接已于 2026-11-12 核查 · HTTP 200 · 打开的是厂商自己的官网
安装与配置
客户端配置（取自该仓库的安装命令）
{
"mcpServers": {
"memgraph-ai-toolkit": {
"command": "uvx",
"args": ["ruff@0.16.0"]
}
}
}解析自我们于 2026-11-04 抓取的该仓库 README；我们未执行过。
command/args 来自仓库写明的安装命令；不确定时请以该项目 README 为准。
VS Code（.vscode/mcp.json —— 键名是 servers）
{
"servers": {
"memgraph-ai-toolkit": {
"command": "uvx",
"args": ["ruff@0.16.0"]
}
}
}VS Code 顶层键是 <code>servers</code>，其余与上面那份相同。
Claude Code（命令行）
claude mcp add --transport stdio memgraph-ai-toolkit -- uvx ruff@0.16.0
Claude Code 不收 JSON 文件，用这条命令直接加。
配置项的键名以你的客户端为准：客户端与 CLI
- 来源明细
- 收录自 abordage/awesome-mcp 榜单（我们从中收集的条目之一） · 条目类型：MCP 服务
- 收录与活跃
- 收录于 2026-11-05 · 最近见到 2026-11-14 · 共见到 10 次 · 被 10 个来源列出
- 评分构成
- 综合分 1,003 · 10 个来源 · mcp
- 安装
uvx ruff@0.16.0
常见问题（答案都来自我们的记录）
怎么安装？
该仓库给出的安装命令是
uvx ruff@0.16.0。我们的记录里它是仓库自己提供的；不保证在你的客户端上直接可用。它适合谁 / 用来做什么？
我们把它归在这些标签下: MCP 服务。标签来自条目自身，不是我们强加的分类。
谁维护它、还在更新吗？
我们收录于 2026-11-05；最近见到 2026-11-14。这里没有"官方认证"或"质量评级"——我们只记录来源与时间。
第三方质量标记
# 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
收录徽章（可选）
想把收录标记放进 README 就复制这行，可选，不影响收录。
[](https://anclum.com/zh/s/memgraph-ai-toolkit/)
- 来源
- awesome · https://github.com/memgraph/ai-toolkit
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