Midas

Midas

The local memory layer for long-horizon AI agents — remembers across sessions, keeps what's current, and won't act on stale memory. No LLM at ingest · $0 per message · fully local · every recall traces to its source.

Your AI assistant forgets everything between sessions. Midas is the memory that lives next to it, on your machine. Your coding agent remembers the decisions, conventions, and bugs from three sessions ago — without piping every message through an LLM to "extract" facts. It costs nothing per message, nothing leaves your computer, every memory traces back to the exact turn it came from, and it won't let an agent act on memory that's stale or never confirmed.

uv tool install "midas-memory[mcp,local]"   # install

midas init # create the shared memory + wire up your MCP clients

or, no Python: npx -y midas-memory-mcp # TypeScript port

or, as a library: pip install "midas-memory[local]"

uv tool upgrade midas-memory # Update Midas

Install in your agent · See the benchmarks · Complete overview · Roadmap

Why Midas

Most memory tools call an LLM to summarize every session — so you pay in tokens forever, add latency, ship every turn to a provider, and get back *rewritten* facts you can't audit. Midas makes the opposite bet, and that bet is what makes it cheap, private, and trustworthy:

• $0 and private by construction. No LLM at ingest or query → no API spend, nothing leaves your machine, fast local ops (~tens of ms, no per-turn network round-trip).
• You can trust what it recalls. Recall returns the verbatim source turn, not an LLM rewrite — so there's no extraction step that can silently hallucinate a "fact" you never said.
• It stays current on its own. Typed belief revision supersedes the old value instead of piling up duplicates; selective forgetting keeps it bounded — all with no LLM.
• It's safe to build on. A provenance guard lets memory inform planning but blocks memory-justified external or destructive actions unless you explicitly confirmed them — and a *superseded* memory can't authorize an action at all.
• One file, many tools. Point Claude Code, Cursor, and your chat app at one SQLite file and they share one live memory.
• Proven, not asserted. Every claim has a reproducible benchmark — *including the experiments that failed.*

How Midas compares

Every Midas number below is measured and reproducible from this repo; the LLM-at-ingest column reflects the structural properties of that design class (Mem0, Zep, Hindsight) and the figures documented in BENCHMARKS.md.

The last row is deliberate: Midas trades whole-conversation abilities for $0, privacy, and auditability, and publishes the measurements that show exactly where that trade bites.

More than recall: a memory you can govern

Finding a buried fact is table stakes. A long-horizon coding agent needs memory it can act on safely and resume from cleanly — which is where similarity search alone falls short:

These properties are measured, not asserted — the [agent-memory bench suite](docs/agent-memory-benches.md) scores action-safety, decision-adherence, repeated-mistake avoidance, resume fidelity, conflict detection/precision (live contradictions between agents found without over-flagging), and adversarial memory-safety across scripted multi-session projects. The safety eval blocks 10 / 10 adversarial attacks (ASR 0.00) — including a planted confirmation next to a prohibition, a confirmation for a *different* action, a provenance-laundering supersession, and a cross-namespace approval — with no over-blocking (benign-pass 1.00). Deterministic, $0, no LLM. Reproduce every number with one command:

uv run python -m eval.benches      # the whole governance suite — or `midas bench` from a checkout

How it does on the benchmarks

Deterministic, reader-independent retrieval (recall@k — fraction of the gold supporting turns pulled into context) on the full public sets, vs a recency-window baseline:

And the cross-system metric, judged answer-rate (same gpt-4o judge the leaderboards use):

All of it at 0 LLM calls, $0, and 0 data egress at ingest. Full numbers, per-category breakdowns, reproduce commands, and the head-to-head vs Mem0/Zep/Mastra are in [BENCHMARKS.md](BENCHMARKS.md).

Eval-first means we publish the misses too. Hybrid retrieval, reranking, thread-diversification,

dual-granularity indexing, and *naive distillation* were all measured to not help (or to hurt) and

are documented as such. That honesty is the point — see BENCHMARKS.md and

docs/frontier-2026.md.

Connect it to your coding agent

One command wires up everything:

midas init        # creates the shared memory + configures every MCP client it finds

midas status # check what's wired · run midas init --dry-run to preview first

Both take `--json` to emit a machine-readable *client wiring receipt* — which memory each client got wired to, under which scope/policy, and which clients were skipped (config paths only, never memory contents). Paste it into a bug report, or let another agent verify the setup without scraping prose.

midas init creates one shared memory (~/.midas/memory.sqlite3) and points the MCP clients it detects — Claude Code, Codex, Grok Build, Cursor, Claude Desktop, Windsurf, VS Code, Gemini CLI, Cline, Zed — at it. So all your agents read and write the same memory, autonomously, with no per-client paths to keep in sync.

Prefer a single endpoint over per-client launches? Run one server and give your clients an MCP URL:

midas serve --http        # → http://127.0.0.1:7077/mcp   (one server, one memory, every client shares it)

midas serve --http --token # require Authorization: Bearer on every request

Keep Midas current with `midas update`. See your memory anytime with `midas inspect`.

Already carrying agent memory in files? `midas import --from claude-md CLAUDE.md` (or --from cursorrules, --from jsonl, --from mem0, --from zep) turns those rules and exports into first-class, recallable, governable memories — tagged with where they came from, idempotent on re-run.

Want memory even when the agent never calls capture? `midas init --claude-hook` installs a Claude Code SessionEnd hook that offers each session's user turns to memory — Midas's no-LLM policy still decides what is actually kept.

Manual setup — any client, or to customize (click to expand)

Midas is a standard MCP server: point any client at the `midas-mcp` command. It uses the shared store by default — no path needed. The universal block:

{ "mcpServers": { "midas": { "command": "midas-mcp", "env": { "MIDAS_MCP_EMBEDDER": "local" } } } }

Override per client with env: `MIDAS_MCP_DB` (default ~/.midas/memory.sqlite3; :memory: = ephemeral) · MIDAS_MCP_MAX_RECORDS · MIDAS_MCP_MIN_IMPORTANCE · MIDAS_MCP_NAMESPACE.

⚠️ GUI apps don't share your shell `PATH`. If a client says *"command not found"*, use the absolute

path from which midas-mcp. On Windows use forward slashes in JSON paths.

Once connected, Midas injects a short policy into the agent (*recall first, then capture durable facts/decisions/preferences/constraints/corrections*). The agent captures freely; Midas decides what's kept — it scores importance (no LLM), drops trivia, skips duplicates, revises stale beliefs, and forgets the low-value tail to stay bounded. Before any memory-justified external or destructive action, the agent calls `che