memee 1.1.0

Institutional memory for AI agent teams: multi-model, cross-project, token-budgeted.

Memee

One lesson. Every agent. Every team. Every model.

Your agents forget. Every session, every project, every vendor swap: gone. Memee writes it down once. Claude proves it. GPT confirms it. Tomorrow Gemini opens a different repo on a different team and starts already knowing.

pipx install memee         # recommended for a CLI tool
# already installed? pipx upgrade memee

For teams and companies. This OSS release is single-user and self-hosted. If you want the same memory shared across a whole team — with cross-developer, cross-agent, cross-project, cross-model canon building into company-wide institutional knowledge — there's a paid Team edition at memee.eu. Same engine, plus SSO, audit log, and shared scope. Flat $49 / month for up to fifteen seats, or $12k / year Enterprise with SOC 2 and air-gap.

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What Memee actually does

Three jobs. Executed relentlessly.

Records.

Every pattern, every decision, every near-miss. One turn at a time, across every agent on every project.

7-task A/B: time −71 %, iterations −65 %, mistakes 0.

Routes.

Not a dump. A briefing. At task start, the router picks the 5–7 memories the agent actually needs — inside a hard 500-token budget. Your CLAUDE.md grows forever. Memee doesn't.

Measured ~40 tokens per task against a ~2,160-token median baseline.

Scores.

A lesson earns trust by surviving. A second model family agrees: confidence ×1.3. A second project re-uses it: ×1.5. Earn both and it climbs the ladder — hypothesis, tested, validated, canon.

One canon. Four model families. Seventeen engines.

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Install and first use — sixty seconds

pipx install memee
memee setup

# Record something you just learned.
memee record pattern "retry with jitter" \
  --tags reliability,http \
  -c "Exponential backoff, capped at 30s, idempotent verbs only."

# Find it back.
memee search "retry"

# Wire Claude Code / MCP, run a health check.
memee doctor

That's it. Memory lives in ~/.memee/memee.db. No account. Core read/write is fully local. Vector embeddings are optional — on by default via sentence-transformers, which fetches a ~80 MB model on first use. Set TRANSFORMERS_OFFLINE=1 to skip.

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The architecture, on one page

Small engines on SQLite + FTS5 + a 384-dim embedding space.

Layer	Job
Router	Task-aware briefing. Budget-capped.
Quality gate	Validates, deduplicates, rates every incoming memory before it earns a row.
Confidence scoring	Adaptive. Cross-project ×1.5. Cross-model ×1.3. Both stacked ×1.95.
Lifecycle	hypothesis → tested → validated → canon → deprecated. Old advice ages out. Good advice gets promoted.
Dream mode	Nightly. Connects related memories, surfaces contradictions, elevates canon.
Propagation	A validated pattern auto-pushes to projects with matching stack or tags. Fix once. Benefit everywhere.
Review	git diff | memee review - scans a changeset against known anti-patterns. Institutional memory enters code review.
CMAM bridge	Push canon to Anthropic's Managed Agents Memory at /mnt/memory/. Claude sees canon on turn one — no MCP round-trip.

Deeper notes: CLAUDE.md. CMAM spec: docs/cmam.md. Review engine: docs/review-fixes.md.

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The token math

Numbers below are internal simulations and measured benchmarks, not independent third-party evaluations. Treat them as suggestive, not conclusive.

The thing Memee saves isn't the first page. It's the slope.

• Without Memee, median: ~2,160 tokens per turn. That's a CLAUDE.md / AGENTS.md across 27 popular OSS repos (langchain, vercel/ai, prisma, zed, openai/codex, and others), sampled via gh api. Claude Code and Cursor load it in full on every session.
• Without Memee, grown teams: 6k–15k. p95 of the sample hits 9,600. One published outlier reached 42,000.
• With Memee: 500-token cap, measured average ~40 tokens per briefing (min 18, max 67 across 10 task queries on a 500-pattern corpus).
• So the saving, honestly: ≥77 % at median. ≥95 % at 10k-grown teams. ≥99 % at the 42k outlier. And unlike CLAUDE.md, it's bounded. Your library grows. Per-turn context doesn't.

Reproduce locally:

memee benchmark          # OrgMemEval v1.0
pytest tests/ -v         # full suite

Full methodology + per-repo file sizes: docs/benchmarks.md.

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Benchmarks

• OrgMemEval v1.0: 92.4 / 100 across propagation, avoidance, maturity, onboarding, recovery, calibration, synthesis, research. Competitors on the same scenarios: MemPalace 0.9, Letta 1.3, Zep 2.3, Mem0 3.5 (the closest).
• 7-task A/B (with / without Memee): time −71 %, iterations −65 %, quality 56 % → 93 %, ROI ≈ 10.7× at the $49 / month Team tier.
• GigaCorp simulation, 100 projects, 100 agents, 18 months: incidents 12/mo → 3/mo, annual ROI ≈ 3× at the same flat Team tier.
• Retrieval: hit@1 = 100 % on a 12-memory routing benchmark after the recent ranking fix.

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Using it with Claude, GPT, Gemini

An MCP server with 24 tools ships with the install. Drop this into ~/.claude/settings.json — or the Cursor / Continue / any MCP-capable client equivalent:

{
  "mcpServers": {
    "memee": { "command": "memee", "args": ["serve"] }
  }
}

Memee auto-detects the caller's model family from MEMEE_MODEL, ANTHROPIC_MODEL, or OPENAI_MODEL and tags every write with source_model. That's how confidence scoring knows when Claude and Gemini agree — and when they don't.

Quick CLI tour:

memee brief --task "write unit tests"   # PUSH: routed briefing
memee check "about to add eval() here"  # PULL: anti-pattern check
memee propagate                         # cross-project diffusion
memee dream                             # nightly: connect, contradict, promote
memee cmam sync                         # push canon to /mnt/memory/ for Claude

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Pricing

Flat per team. Same engine in every tier.

	Free	Team	Enterprise
	$0 forever · MIT	$49 / month flat — up to 15 seats, annual	from $12k / year — unlimited seats
For	Solo developers. Self-hosted. Full engine, local scope.	Teams that want shared memory, SSO, and an audit trail.	Regulated industries, air-gap, SOC 2.
Stack	Router, quality gate, dream mode, CMAM sync, all 4 model families	Everything in Free + team/org scope with promotion workflows, SSO (SAML / OIDC), RBAC, audit log export, Postgres / Turso backend, multi-agent dashboard, 24h SLA	Everything in Team + SOC 2 Type II, DPA, SCIM, on-prem license key, dedicated CSM, 4h SLA, custom MCP integrations

Between fifteen and a hundred seats, and no SOC 2 needed? Email [e-mail] for a custom Growth plan.

Memee is memory, not model. Value scales sublinearly with headcount — one canon serves the whole team — so pricing is flat, not per-seat.

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Contributing

PRs welcome. Before opening a large one, a short issue describing the direction saves everyone a round-trip.

pip install -e ".[dev]"
pytest tests/ -v

Style: type hints, English docstrings, 100-char lines, ruff clean. New engines live in src/memee/. Every new behaviour wants a test in tests/.

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License

Memee core is MIT. The optional memee-team package is proprietary, distributed under a separate commercial EULA. See memee.eu for the terms.

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Built by people who stopped teaching the same lesson to every new agent.