Mimir

Your AI agent's memory is a folder of markdown files. Open it in Obsidian. Edit a fact by hand. The agent sees your edit on its next thought — no sync step, because there's nothing to sync.

Every agent framework has the same problem: conversations start from zero. Full-context-stuffing burns your token budget, naive summarization throws facts away forever, and flat vector RAG can't tell a memory from yesterday apart from one from six months ago.

Mimir is a memory engine that fixes this without asking you to run anything. No cloud account, no Docker, no API key. Point it at nothing and it works — a local file becomes the transcript, another becomes the searchable facts, and a folder of markdown becomes the part you can actually read, understand, and correct. Add a local model (or a cloud one) later and every layer gets sharper automatically. Nothing you built against ever has to change.

Why a vault, not a database

Every other memory system stores your agent's knowledge as opaque rows you'd need a script to inspect. Mimir writes it as OKF (Open Knowledge Format) — YAML frontmatter, markdown prose, [[wikilinks]] between facts and the entities they mention. That means:

• You can open it. ~/.mimir/vault is a real Obsidian vault. Graph view, backlinks, search — all free, all native.
• You can fix it. The agent got something wrong? Edit the note. The fix takes effect on the next read. No re-ingestion, no cache invalidation dance.
• It's yours. git init your vault if you want history. Copy the folder to a new machine and your agent remembers everything, everywhere.
• The databases are just indexes. DuckDB and Qdrant exist to make search fast — the vault is the source of truth, always.

What's actually happening under the hood

Four stores, each one optional except the first, each degrading independently if it's missing:

A conversation flows through: capture (raw turns land in DuckDB, best-effort push to Redis) → flush at session end (an LLM — or a deterministic offline digest — turns the session into an Obsidian scene note, extracts atomic facts, dedupes against what's already known, flags contradictions instead of silently picking a winner, refreshes a running persona doc every N facts). Recall runs the other way: semantic cache check → BM25 + vector hybrid search → reciprocal rank fusion → a four-signal score (semantic relevance, recency decay, access frequency, graph proximity through your vault's own wikilinks) → a context string ready to inject into your agent's prompt.

Full diagrams and a module-by-module walkthrough: docs/ARCHITECTURE.md.

Quickstart

git clone https://github.com/hasil7677/mimir.git
cd mimir
pip install -e ".[dev]"
uvicorn app.main:app --port 8080

That's it — GET /health works with zero config. No mimir.yaml, no Redis, no API key required. Copy mimir.yaml.example to mimir.yaml when you want to point at a local Ollama model, a cloud LLM, or a Redis instance; every setting has a sane default until then.

Use it from Claude Code (or any MCP client) right now

claude mcp add mimir --scope user -e MIMIR_USER_ID=you -- python /path/to/mimir/engine/adapters/mcp_embedded.py

No gateway to run — the embedded adapter imports the engine directly, so a process only exists while your agent session is open. Three tools show up: mimir_recall, mimir_remember, mimir_flush. Point your CLAUDE.md at them and your agent starts building a memory of you, one conversation at a time.

Also documented: OpenCode (native MCP), Pi (via the community pi-mcp-adapter), and a plain HTTP contract for anything else — see docs/CLIENTS.md.

Status

This is early — built fast, tested hard, not yet battle-tested by anyone but me. Here's the honest split:

Solid and tested (83 tests — 80 pass with zero services running, 3 need a live Redis — real HTTP layer, real filesystem, real dedup logic): hybrid recall with a 4-signal scoring formula · semantic caching with measured cache hits · L1.5 fact consolidation (exact-dup detection needs zero LLM calls; an LLM present gets you store/skip/supersede/contradiction-flag decisions, with hallucinated target IDs rejected) · GDPR-style erasure and export across every store · a self-healing recovery path for orphaned sessions (found live, fixed same day — see the commit log if you want to watch that happen) · MCP support verified end-to-end inside real Claude Code sessions.

Known gaps, not hidden:

• No real graph database yet — KuZu has no Python 3.11+ wheels as of this writing, so entity relationships live as vault [[wikilinks]] with hop-distance scoring instead of a Cypher-traversable graph. The scoring interface is already hop-based, so KuZu slots in without a rewrite once it's installable.
• Entity extraction is a regex heuristic (capitalized-run detection), not a real NER model. It works, and it also occasionally wikilinks a stray proper noun it shouldn't. spaCy is the planned fix.
• No benchmark numbers yet — no PersonaMem run, no measured recall accuracy. Every design claim here is architectural reasoning, not a published score.
• LangChain / OpenAI Agents adapters aren't built. The HTTP contract they'd need already exists.

If you're looking for something production-hardened with a support contract, this isn't it yet. If you want to see what a memory system looks like when the databases are treated as caches and the filesystem is treated as the truth, open the vault.

Development

pip install -e ".[dev]"
pytest              # 83 tests; Redis-backed ones auto-skip without a server

CI runs the suite on Python 3.11–3.13, on both Ubuntu and Windows — the Windows leg is not decorative, it's what caught a real timezone bug during development.

License

MIT — see LICENSE. Built on the idea that the core memory engine should always be free and open; anything resembling a hosted/managed offering is a separate conversation for another day.