🗄️ Mnemo — the agent that never forgets

A standalone AI assistant with a persistent, temporal memory brain. Tell it something in one session; ask about it in a completely new session and it recalls it — with who, what, and when — citing its sources.

Mnemo pairs a temporal knowledge graph (Graphiti on FalkorDB) with a Claude-session harness (MCP tools + hooks + skills), so any Claude Code session can remember and recall across time. Its persona is the archivist: precise, unhurried, and it never states a fact without saying where and when it was true.

you (session 1):  "Remember: Marco leads Solaris; it launches Q4 2027; it depends on Helios."
you (session 2):  "Who leads Solaris, when does it launch, what does it depend on?"
Mnemo:            Lead: Marco · Launch: Q4 2027 · Depends on: Helios API   (as of 2026-07-17)

How it works

Mnemo has two layers, exactly like a well-run agent: a git-versioned identity (markdown) and a memory brain (a graph database). A thin connection layer (MCP + hooks) plugs that brain into any Claude session.

flowchart TB
    subgraph SESSION["🧠 Claude session (this repo, any repo, or Claude Desktop)"]
        Q["You ask a question<br/>or state a fact"]
    end

    subgraph CONNECT["🔌 Connection layer"]
        direction LR
        MCPR["recall tool"]
        MCPM["remember tool"]
        SS["SessionStart hook<br/>· ensure DB up<br/>· surface tools"]
        STP["Stop hook<br/>· auto-learn:<br/>summarize + ingest"]
    end

    subgraph BRAIN["💾 Memory brain"]
        direction LR
        GR["Graphiti<br/>extract entities + typed edges<br/>bi-temporal validity"]
        FDB[("FalkorDB<br/>property graph")]
        EMB["fastembed<br/>local embeddings"]
    end

    Q -->|"tell"| MCPM --> GR
    Q -->|"ask"| MCPR --> GR
    GR <--> FDB
    GR <--- EMB
    SS -.injects context.-> SESSION
    STP --> GR

Ingest and recall — remembering turns text into a graph; recalling runs a hybrid search over it and answers in persona:

flowchart LR
    T["raw text<br/>(who / what / when)"] --> RM["remember()"]
    RM --> EX["Graphiti extraction<br/>· entities as typed nodes<br/>· relationships as typed edges<br/>· dates/status as node attributes"]
    EX --> KG[("FalkorDB<br/>temporal property graph")]

    QQ["a question"] --> RC["recall()"]
    KG --> HS["hybrid search_()<br/>vector + BM25 + graph traversal"]
    RC --> HS
    HS --> CTX["facts + entity attributes + source excerpts"]
    CTX --> ANS["Claude answers in the<br/>archivist persona, with citations"]

Why memory survives across sessions — nothing is shared between sessions except the graph on disk:

sequenceDiagram
    participant S1 as Session 1 (Mon)
    participant M as Mnemo
    participant DB as FalkorDB (volume)
    participant S2 as Session 2 (Fri · fresh process)
    S1->>M: remember("Marco leads Solaris, launches Q4 2027")
    M->>DB: extract → nodes + typed edges + valid-time
    Note over S1,DB: session ends → Stop hook summarizes & ingests it too
    S2->>M: recall("who leads Solaris? when?")
    M->>DB: hybrid search (edges + nodes + episodes)
    DB-->>S2: Marco · Q4 2027 (cited, as-of dated)

The pieces

Embeddings run locally (fastembed, 384-dim) so no embeddings key is needed; the LLM (extraction + answers) is Claude, and the client accepts both a standard sk-ant-api03 key and a Claude Code sk-ant-oat OAuth token.

Related MCP server: my-memory-mcp

How Mnemo compares

Most "AI memory" is either a flat vector store (good at similar text, blind to relationships and time) or an opaque per-user blob you can't inspect. Mnemo is a temporal knowledge graph with a Claude-native connection layer.

Where Mnemo sits: it is not a competitor to Graphiti — it is built on Graphiti. Think of it as Graphiti (the brain) + a Claude-session body: the MCP tools, the SessionStart/Stop hooks, the archivist persona, and the operational glue (OAuth-token auth, local embeddings, portable ${CLAUDE_PROJECT_DIR} config, comprehensive search_() recall) that turn a memory library into a memory agent you can talk to.

vs. a Notion/RAG-backed assistant (e.g. an agent whose "memory" is a Notion database): those retrieve documents by similarity. Mnemo retrieves facts and their relationships over time — it can answer "who used to own this, and who owns it now", which flat retrieval cannot.

What Mnemo is not (yet)

Honest scope — it's an experiment, not a product:

• No multi-user / auth on the graph — single local user.

• No managed hosting or horizontal scale (FalkorDB is a local Docker container).

• Extraction is LLM-driven and ~good, not perfect — occasionally a detail is missed (it will say "not found" rather than hallucinate).

• Auto-learned summaries vary in quality; recall-heavy sessions produce thin summaries.

• No daemon; the brain is up while docker compose is running.

Quickstart

git clone <this repo> && cd mnemo-agent
cp .env.example .env          # set ANTHROPIC_API_KEY (sk-ant-api03 key OR sk-ant-oat token)
docker compose up -d          # start FalkorDB (defaults to port 6380)
python -m venv .venv && . .venv/bin/activate && pip install -r requirements.txt
python scripts/seed.py        # load the fictional sample data (Atlas project)
pytest -v                     # prove it: ingest + recall + temporal, all live

Then talk to it from any Claude Code session in this repo — the SessionStart hook wires the recall/remember tools automatically. To use Mnemo from any repo or Claude Desktop, add the .mcp.json server to your global config.

Verified behaviour

Proven live (see the test suite + docs/):

• Cross-session: store in one claude process, recall in a separate one — including dates and dependencies.

• Temporal: after a reassignment, "who currently owns X" returns the new owner, not the stale one (edge invalidation).

• Durable: survives a docker compose restart (data in the FalkorDB volume).

• MCP transport: tools list and call over the real MCP stdio protocol.

• Auto-learn: finished sessions are summarized and ingested by the Stop hook.

Design & internals

• docs/2026-07-16-mnemo-agent-design.md — the design spec.

• docs/agent-memory-plan.md — the graph-first memory model this is built on (why a graph, not a flat table; the 3-tier idea; engine selection).

Tech stack

Python 3.12 · Graphiti graphiti-core · FalkorDB (Docker) · Anthropic Claude (extraction + answers) · fastembed (local embeddings) · MCP (FastMCP) · pytest.