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Memory and knowledge · MCP server

Graphiti Local: MCP server, setup and security

Graphiti Local is an open-source MCP server that lets an AI agent query a local temporal knowledge graph through six readonly retrieval tools. Its repository has 5 stars and its last commit is from October 23, 2026 and it is licensed under Apache-2.0.

Actively maintained

What Graphiti Local does

According to its repository: “Local temporal knowledge graph with six read-only retrieval tools, Ollama inference, embedded LadybugDB, and a separate human-approved CLI write queue.”

An MCP server gives an AI agent extra tools it calls during the conversation: the agent reads their description, decides to use them and gets the result back. Graphiti Local is listed under Memory and knowledge, a category of 250 servers in the directory, where it ranks #236 by stars.

Topics declared by the repository: agent-memory, ai-agents, context-engineering, graphiti, knowledge-graph, local-first.

Installing and connecting Graphiti Local

Distribution modes declared in the official MCP registry:

  • PyPI package (Python, started with uvx or pip)

Like any MCP server, you declare it in your client's configuration (Claude Desktop, Claude Code, Cursor, VS Code, Codex…): a name, the command that starts it or its URL, and any environment variables. The client then starts it and lists its tools. Project language: Python.

Security: what to check

An MCP server runs code with the rights you give it, and the model reads its tool descriptions: a malicious or compromised server can hijack the agent. Read the code, or pick a maintained and widely used server, before connecting it.

None of the risk signals we track (system access, secrets, missing licence, abandonment) applies to Graphiti Local. That does not replace reading the code.

Good practice: grant only the rights needed (least privilege), require human approval for irreversible actions, and run the AI agent security check.

Project activity and maintenance

Created on August 25, 2026, the renezander030/graphiti-local repository has 5 stars and 0 forks, with 0 open issues. It has more stars than 13% of the servers in the directory. It received changes this month. Version published in the registry: 0.10.0 (October 23, 2026).

Stars measure attention, not quality: a recent, heavily promoted project can collect them fast, while a useful but niche server will have few.

Context7Context7 : 63.6K63.6KHindsightHindsight : 53.4K53.4KCogneeCognee : 32.7K32.7KSkill SeekersSkill Seekers : 15.2K15.2KHonchoHoncho : 7.8K7.8KBasic memoryBasic memory : 4.3K4.3KNoteDiscoveryNoteDiscovery : 2.8K2.8KMemantoMemanto : 2.3K2.3KGraphiti LocalGraphiti Local : 55
GitHub stars of the most followed servers in the category (log scale); Graphiti Local in orange. Checked October 25, 2026.

Verdict

Graphiti Local is a reasonable choice; check the points below before connecting it.

Strengths

  • Active project: recent commit
  • Listed in the official MCP registry
  • Permissive licence (Apache-2.0)

Limitations

  • Few visible users

Frequently asked questions

How do I install Graphiti Local?

Graphiti Local is distributed as PyPI package (Python, started with uvx or pip). Add it to your client's MCP configuration (Claude Desktop, Cursor, VS Code…) following the README of renezander030/graphiti-local.

Is Graphiti Local free?

The code is published under the Apache-2.0 licence and can be used free of charge.

Is Graphiti Local safe?

No risk signal found (system access, secrets, licence, abandonment). As with any MCP server, keep rights minimal and read the code before sensitive use.

Is it an official server?

Nothing says so: it is a project published by renezander030 on GitHub. Check who maintains it before giving it access.

What are the alternatives to Graphiti Local?

In the same category: Context7, Basic memory, Hindsight, and Cognee. Context7 and Basic memory score higher on our grid.

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Neutrality. No affiliate links. No vendor pays to be reviewed, rated or ranked. Reviews are directed by François Chollet, neutral and factual, and every data point is dated and sourced. How we score