verifiable-memory

Memory for AI agents that cannot hallucinate. It answers only from stored facts — with the source cited — or it honestly says "I don't know." Every guarantee below is cryptographic or true by construction, not a prompt trick.

An MCP server + Python SDK. Plug it into any agent (Claude Desktop/Code, LangChain, custom). The LLM phrases; this layer guarantees the facts.

The problem

LLMs store knowledge in weights. So they hallucinate, can't cite, can't be edited, can't forget, can't be audited. That blocks agents from any high-stakes use — legal, finance, healthcare, compliance, autonomous workflows.

What you get (an LLM cannot do these from its weights)

• 0% hallucination — exact match only; unknown → honest abstention.
• Citations — every answer carries its source.
• Provable forgetting (GDPR / right-to-be-forgotten) — the fact is really gone; signed proof; Merkle root reverts.
• Valid-time — version a fact; ask "as of date T"; full history.
• Merkle proofs — commit all knowledge to one hash; prove a fact's inclusion without revealing the rest.
• Contradiction detection — surfaces conflicting values with both sources instead of silently picking one.
• Signed receipts + determinism — tamper-evident, same query → same answer.

Benchmark (reproducible — python3 benchmark.py)

Stress-tested to 1,000,000 facts on a 7 GB CPU box, no GPU:

vs a naive "always answer" baseline: 0% vs 100% fabrication on the same traps.

Install

pip install verifiable-memory-mcp
verifiable-memory                     # MCP server over stdio
# from source:
git clone https://github.com/Mars-proj/verifiable-memory && cd verifiable-memory
python3 -m vmem.server

Use from Claude Desktop / Code

{
  "mcpServers": {
    "verifiable-memory": {
      "command": "verifiable-memory",
      "args": [],
      "env": { "VMEM_STATE": "~/.verifiable_memory" }
    }
  }
}

Then your agent can learn_fact, recall (cited or abstains), forget (provably), prove_fact, contradictions, multihop, and more — 13 tools.

How it works (1 line)

Facts are stored as data (subject, relation, object + source), indexed for O(1) exact recall; answers are exact-match-or-abstain; the knowledge state commits to a Merkle root. No vectors needed for the verifiable path → 0 fabrication by construction.

Honest scope

This is a memory / trust layer, not a reasoning engine and not a better chatbot. It wins on verifiability (cite-or-abstain, forget, determinism, audit), not on open-ended fluency. Pair it with your LLM: LLM = language, this = ground truth.

🤝 Using this in production?

Need a hosted API, on-prem deployment, or help integrating verifiable memory into your agent (legal / fintech / healthcare / agent platforms)? → Pilot & enterprise: Sergey · svobodg@gmail.com

MIT licensed. PRs welcome.