memory3l-mcp

Auditable memory over MCP: an append-only fact ledger plus verifiable invariants, served read-only from SQLite.

The mcp-name: string above and server.json's name must stay identical: the MCP Registry verifies PyPI package ownership by matching them. Changing one without the other fails verification.

What this is for

A memory system claims it never loses a fact. Almost none can prove it, because nothing keeps an independent record to compare against.

memory3l keeps one: an append-only ledger of every (slot, value) ever extracted, written when each summary is created — including the summaries later overridden or merged away, which is exactly where facts disappear. This server exposes the cross-check as tools, so an agent can ask whether a value is still reachable, why a fact left the working set, or whether a deletion actually completed, and get an answer derived from persisted state rather than from the summary's own paraphrase.

The checks are the product. The storage is ordinary SQLite.

Install

uvx memory3l-mcp                  # no install step, runs from PyPI

The audit core (memory3l) is installed as an ordinary dependency. MEMORY3L_ROOT is only needed when developing against a source checkout:

MEMORY3L_ROOT=/path/to/Memory uvx memory3l-mcp

The ledger defaults to ~/.memory3l/memory3l.db and is created on demand, so a fresh install starts and answers episodes: [] instead of failing. Point it somewhere else with MEMORY3L_DB, or --db PATH.

Try it without your own pipeline

python3 scripts/seed_demo.py --db /tmp/demo.db   # generates a real ledger offline
MEMORY3L_DB=/tmp/demo.db uvx memory3l-mcp

No network, no API key, no Redis.

Tools

Read-only by default. Without --allow-write (or MEMORY3L_ALLOW_WRITE=1) the server is an audit surface: it can inspect memory but not create it.

Tool	Answers
memory_context	the memory block to put in your prompt — call before answering
remember	record a completed turn — call after answering
store_info	which database is served, whether it is readable, and whether memory is on
list_episodes	which episode ids exist — call this first
audit	the invariant report for one episode
audit_summary	the same, aggregated over every episode
current	the newest value of every slot, as the model should be told it
history	every value a slot ever held, with supersession
fact	why one fact left the working set (live / archived / missing)
evidence	the original dialogue a fact was extracted from
tombstones	what was erased, what is provably gone, what prose remains
temporal	the versioned projection, its anomalies, and its DDL
append_facts	intake for your own extractor; it cannot make a fact visible

Giving an agent memory

The memory half is two calls per turn, and the order matters:

memory_context(episode_id)                       # before you answer
remember(episode_id, user_message, agent_message) # after you answer

memory_context returns memory in a fixed order (recent dialogue, current values, index titles, summary chain) with no persona attached, so it drops into any prompt. An empty block is the correct answer for a new conversation.

MCP has no hook, so nothing is automatic: if you skip memory_context the agent answers without memory even though remember recorded the turns. The memory3l skill is what teaches an agent to make both calls.

Two operational notes:

• remember is where the model call goes. It summarises, so each turn costs a summariser call. Point --summarizer at a small model.
• The conversation survives a restart. One episode_id is one conversation; the chain and the turn counter are read back from SQLite, so a restarted server continues rather than starting over.

Without a model (--summarizer none) memory still works — turns are stored verbatim — but nothing is extracted, so the current-value registry stays empty and the audit tools have nothing to check. store_info reports which mode you are in rather than letting the difference be discovered later.

The invariants

• I1 conservation — a ledger fact resolves to no summary, active or archived: it was extracted and now nothing can reach it. Real silent loss.
• I2 top-level reachability — a slot's newest value lives only inside an index member, so the model is never told it: stored, and practically invisible.
• I3 provenance — a fact's evidence no longer resolves to stored dialogue.
• I4 extraction completeness — the extractor itself missed facts present in the raw turns. The blind spot the other invariants cannot see: they all pass when nothing was extracted at all.
• I5 deletion — an erasure is verified rather than assumed: the value is gone and its evidence destroyed, with residual prose mentions counted.

An episode with no facts satisfies every invariant trivially, so an unknown episode is an explicit error rather than an empty pass. A tool that answers "no violations" about an episode that does not exist is worse than one that fails.

Configure your client

Any MCP client that can launch a stdio server. For DSH, add one row to $DSH_HOME/profiles/<name>/cordis.patch.yml:

- insert:
    - id: mcp-memory
      name: '@deepseek-ai/dsh-mcp-client'
      config:
        serverName: memory
        transport: stdio
        command: uvx
        args: ['memory3l-mcp']
        env:
          MEMORY3L_DB: /path/to/your/memory3l.db

Tools then appear as mcp__memory__audit, mcp__memory__current, and so on — the same mcp__<server>__<tool> shape Claude Code and Codex use. DSH passes the env entries through to the server (credential-shaped variables are scrubbed from the ambient environment, which is why the ledger path is set explicitly here).

The skill

skills/memory3l/SKILL.md is an Agent Skills bundle that teaches an agent when to reach for these tools and how to report the answer without overstating it — chiefly that "no violations" is not a guarantee that nothing was lost.

Install it into the cross-client directory so every compliant agent sees it:

memory3l-mcp-install-skill           # → ~/.agents/skills/memory3l

Skills installed there are visible to any client that scans .agents/skills/, and vice versa. No per-client adaptation. The command refuses to overwrite an existing skill whose content differs — pass --force if you mean it, or --target DIR to install somewhere else.

Development

MEMORY3L_ROOT=$PWD PYTHONPATH=$PWD/src python -m unittest discover -s tests
python scripts/seed_demo.py --db /tmp/demo.db

Requires mcp>=2. Note that mcp 2.0 renamed FastMCP to MCPServer and made mcp.server.fastmcp raise on import; this package targets 2.x.

License

MIT