接入你的智能体
发布者自己的命令。不是 PlugMCP 端点。
用我们的密钥与日志托管一份副本。
uvx graphiti-local==0.11.0Claude Code 没有针对本地命令的安装链接。复制下方命令并在终端中运行。
claude mcp add io.github.renezander030-graphiti-local -- uvx graphiti-local==0.11.0在终端中运行。
{ "mcpServers": { "io.github.renezander030-graphiti-local": { "command": "uvx", "args": [ "graphiti-local==0.11.0" ] } }}claude_desktop_config.json
远程 URL 还是本机命令？ 两种安装方式的区别
来自发布者
Local memory for your agents. You approve what they learn.
Retrieve project decisions through six read-only MCP tools or the kg CLI.
Proposed updates stay in a separate queue until a human approves and applies them.
Ollama handles local inference; embedded LadybugDB stores the graph. No Docker or
cloud API key is needed for the local setup.
Local-first temporal knowledge graph with six read-only MCP tools and human-reviewed writes.
Captured output excerpts with pauses condensed. Measured results and limitations.
uvx --from git+https://github.com/renezander030/graphiti-local kg-demo
Answers a question against a small synthetic graph shipped with the package. No Ollama, no model downloads, no database setup: retrieval runs on keywords alone, so nothing contacts a model. Ask your own question by passing it as an argument.
This is the read path only. Building a graph from your own text needs extraction, which needs a model, and that is the full setup below.
Install uv and Ollama, then start Ollama. Setup downloads need internet access. Run these commands in bash or zsh:
git clone https://github.com/renezander030/graphiti-local.git
cd graphiti-local
uv sync --frozen
ollama pull qwen2.5:7b
ollama pull nomic-embed-text
export GRAPHITI_LOCAL_CONFIG="$PWD/config/ollama.example.yaml"
export KG_WORKSPACE_DIR="$PWD/workspace/local-demo"
export KG_LADYBUG_PATH="$KG_WORKSPACE_DIR/graph.ladybug"
uv run --frozen kg-ladybug-setup --database "$KG_LADYBUG_PATH" --apply
uv run --frozen kg doctor
uv run --frozen kg-ingest examples/local_memory_demo.jsonl --apply
uv run --frozen kg ask "Which database does Aurora Analytics use?" example
The synthetic example returns DuckDB. Follow the complete walkthrough to propose PostgreSQL, review and apply that update, and retrieve it from an MCP client. Model extraction can be wrong; inspect the returned facts and validity timestamps.
To review extraction before it touches the configured graph, run
kg-ingest INPUT --review-output review.jsonl, inspect the snapshot, and mark every
fact in the generated review.jsonl.review.jsonl file as accept, refuse, or
contested. The printed restore command refuses unresolved decisions and promotes a
checksum-sealed snapshot containing accepted facts only. Searches return current facts
by default; kg ask --history is the explicit historical view.
kg-ingest also accepts a Markdown/text file or a directory tree directly. Large
documents split at stable text boundaries. Transient writes retry with one stable
episode id; ingest-receipts.jsonl and ingest-failures.jsonl in the workspace show
exactly what landed and what still needs attention.
Give each user a group, and each group its own Ladybug file:
graph:
groups: [alice, bob]
database:
provider: ladybug
ladybug:
layout: per-group # default: single, one file for every group
directory: ./workspace/groups
server:
transport: streamable-http
auth:
tokens:
- {name: alice, token: "${ALICE_TOKEN}", groups: [alice]}
- {name: bob, token: "${BOB_TOKEN}", groups: [bob]}
Each file is named by the SHA-256 of its group, so no group id can point outside the
directory. Reads, kg ask and the drain open only the file of the group they address;
a token granted alice never opens Bob's file. A read names one group, or the token's
single group is used. For a tenant boundary inside one database server, use FalkorDB
or Neo4j.
Use it for local agent memory with explicit human review. Skip it if you need agents to write through MCP or want a hosted service without local setup. FalkorDB and Neo4j are also supported.
If this helps your workflow, star the repository and share your setup result.
Maintained by René Zander, who builds context layers for AI agents on temporal knowledge graphs.
Independent community project built on Graphiti, not affiliated with or endorsed by Zep. Apache-2.0.
Local-first temporal knowledge graph with six read-only MCP tools and human-reviewed writes.
{ "mcpServers": { "io.github.renezander030-graphiti-local": { "command": "uvx", "args": [ "graphiti-local==0.11.0" ] } }}工具
暂无工具列表。远程服务器在首次健康检查后会获得工具列表。
常见问题
在本页复制适用于 Cursor、Claude Desktop、Claude Code 或 VS Code 的 stdio 配置。它使用发布者的安装方式，而不是 PlugMCP 代理。
此记录未列出必需的环境变量。
Graphiti Local 通过 stdio 在本地运行。
上次检查时间：2026-11-15。 Local-first temporal knowledge graph with six read-only MCP tools and human-reviewed writes.