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Memory and knowledge · MCP server

Empathy framework: MCP server, setup and security

Empathy framework is an open-source MCP server published on GitHub that lets an AI agent collaborate across five levels using persistent local memory and anticipatory capabilities. Its repository has 13 stars and its last commit is from April 13, 2026 and it is licensed under Apache-2.0.

Last commit 6 months ago

What Empathy framework does

According to its repository: “Five-level AI collaboration system with persistent local-first memory via MemDocs and anticipatory capabilities.”

The Model Context Protocol (MCP) is a standard plug between an AI agent and an outside service: once the server is connected, its tools show up in the agent, which decides when to call them. Empathy framework is listed under Memory and knowledge, a category of 250 servers in the directory, where it ranks #180 by stars.

Topics declared by the repository: ai-collaboration, anthropic, artificial-intelligence, claude-code, code-analysis, debugging.

Installing and connecting Empathy framework

No package or remote server is declared in the official MCP registry: you install it from source, following the repository README.

It works with any MCP-compatible client (Claude Desktop, Claude Code, Cursor, VS Code, Codex and most recent agents). You add one entry to the client's MCP configuration: server name, start command or URL, environment variables. Project language: Python.

Security: what to check

Connecting an MCP server means installing software that talks straight to your agent: it runs with your rights and the model trusts its tool descriptions. The risk therefore depends on what it can touch and how far its code can be trusted.

None of the risk signals we track (system access, secrets, missing licence, abandonment) applies to Empathy framework. 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 November 8, 2025, the Smart-AI-Memory/empathy-framework repository has 13 stars and 6 forks, with 6 open issues. It has more stars than 31% of the servers in the directory. Last change 6 months ago.

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 : 2.3K2.3KEmpathy frameworkEmpathy framework : 1313
GitHub stars of the most followed servers in the category (log scale); Empathy framework in orange. Checked October 25, 2026.

Verdict

Empathy framework is still a modest project: read its code and compare it with the alternatives before using it.

Strengths

  • Permissive licence (Apache-2.0)

Limitations

  • No commit for 6 months
  • Install from source

Frequently asked questions

How do I install Empathy framework?

No package is declared in the MCP registry: clone the Smart-AI-Memory/empathy-framework repository, follow its README, then declare the start command in your client's MCP configuration.

Is Empathy framework free?

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

Is Empathy framework 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 Smart-AI-Memory on GitHub. Check who maintains it before giving it access.

What are the alternatives to Empathy framework?

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