Factum MCP server
io.github.factum-project/factum·v0.1.3·3 starsAuditable memory for AI agents: provenance, cascade retraction, conflict refusal
3 stars
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Factum tools
No tool declarations could be read from the package source. They show once the server is installed.
Public scan report
scanner v0.1.10 · 2026-10-03 · same rubric, same numbers if you re-run it
- –Code scanpackage could not be scannedn/a
- –Live reliabilityno gateway calls yet and no remote to proben/a
- –Tool poisoningtools not inspected (local package is not executed); not countedn/a
- Auth qualitylocal package, no credentials required12/15
- Maintenancelast push 12 days ago15/15
- Maintainer identityregistry namespace matches repository owner7/10
How Factum compares
Against every graded MCP server, from our own scans and uptime checks. Updated after each scan run.
- Safety score
- 85/100
- higher than 70% of 48,255 servers
- GitHub stars
- 3
- more than 80% of 28,688 servers
Install Factum in Claude Code, Cursor or VS Code
The registry entry has no remote endpoint or installable package.
What the publisher says
From the Factum repository's README, as published. We do not edit it. Read it on GitHub
Factum — Auditable Memory for AI Agents
Status: v0.1.3 — Early stage, seeking early collaborators.
Core write/query/retract pipeline works. Not production-ready. Architectural decisions are still open to change.
Interactive docs — animated syntax parsing, 7-tuple explorer, query pipeline, token efficiency chart, and MCP architecture diagram.
Every fact an agent writes carries mandatory provenance. When a source is retracted, everything derived from it is invalidated automatically — cascade retraction. When facts conflict, Factum returns Ambiguous instead of guessing.
A structured knowledge language (S-expression based), Rust implementation, stdio MCP server — works with Claude Code, Cursor, and any MCP client.
Status: v0.1.3, early stage. Core write/query/retract pipeline works; no semantic search or memory consolidation yet — Factum handles verified structured facts, not conversation context. Best suited for compliance-sensitive agents, multi-agent shared knowledge bases, and anywhere "why did the agent believe X" needs an answer. See the multi-agent usage guide for shared knowledge base patterns.
How is this different from Mem0 / Zep / Letta?
Unique to Factum: grammar-enforced provenance · cascade retraction · conflict refusal.
Not (yet) in Factum: embedding-based semantic retrieval · memory consolidation · HTTP transport.
Complementary: Mem0/Letta store and retrieve context; Factum stores auditable structured facts. They can run side by side via MCP.
What this means in practice
Academic context: The STALE benchmark (2025) shows that even the best LLM agents achieve only 55.2% accuracy at detecting when their own memories are outdated — confirming that memory staleness is an unsolved problem in agent systems.
Key Features (Implemented)
Shortened. The full README is on GitHub.
Nothing above is checked by us. What we check is on the safety report.
Factum: common questions
- Is Factum MCP server safe?
- Yes, by our scan: it is graded A (85/100). Read the Factum safety report
- Does Factum need an API key?
- Not as far as the registry entry and our scan can tell: no credentials are declared or required.
- Is Factum maintained?
- The last commit was 44 days ago (2026-09-21). The latest release is v0.1.3.
- How does Factum compare with other MCP servers?
- safety score 85/100 (higher than 70% of 48,255 servers); github stars 3 (more than 80% of 28,688 servers). Full comparison.