FRENES

Memory and knowledge · MCP server

Piia Engram: MCP server, setup and security

Piia Engram is an open-source MCP server that lets an AI agent retain and synchronize user preferences, lessons and decisions across AI tools. Its repository has 163 stars and its last commit is from October 23, 2026 and it is licensed under AGPL-3.0.

Score
72/100
Adoption (stars) 22/40
Maintenance 30/30
Framework (licence, registry, official) 20/30
Actively maintained

What Piia Engram does

According to its repository: “Persistent user identity across AI tools: preferences, lessons and decisions in local JSON with staged verification, AES-256-GCM encryption and cross-tool sync.”

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. Piia Engram is listed under Memory and knowledge, a category of 250 servers in the directory, where it ranks #58 by stars.

Topics declared by the repository: ai-agent, claude-code, codex, context-management, cursor, local-first.

Installing and connecting Piia Engram

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. Listed systems: macOS, Windows, Linux.

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 Piia Engram. 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 May 18, 2026, the Patdolitse/piia-engram repository has 163 stars and 13 forks, with 1 open issues. It has more stars than 74% of the servers in the directory. It received changes this month. Version published in the registry: 4.26.0 (October 21, 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 : 2.3K2.3KPiia EngramPiia Engram : 163163
GitHub stars of the most followed servers in the category (log scale); Piia Engram in orange. Checked October 25, 2026.

Verdict

Piia Engram is a reasonable choice; check the points below before connecting it.

Strengths

  • Active project: recent commit
  • Listed in the official MCP registry

Frequently asked questions

How do I install Piia Engram?

Piia Engram 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 Patdolitse/piia-engram.

Is Piia Engram free?

The code is published under the AGPL-3.0 licence and can be used free of charge.

Is Piia Engram 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 Patdolitse on GitHub. Check who maintains it before giving it access.

What are the alternatives to Piia Engram?

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