You shouldn't have to teach it twice.
Every chat is a new intern. You teach them Monday. By Friday they've quit. Memee writes it on the wall. The next intern reads it. So does your teammate's. So does the next model.
Memee shares experience across projects, across agents, across models, and across the people on your team.
pipx install memee # recommended for a CLI tool
# already installed? pipx upgrade memeeFor 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.
Three jobs. Executed relentlessly.
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.
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.
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.
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 doctorThat'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.
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.
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.mdacross 27 popular OSS repos (langchain, vercel/ai, prisma, zed, openai/codex, and others), sampled viagh 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 suiteFull methodology + per-repo file sizes: docs/benchmarks.md.
- OrgMemEval v1.0: 92.2 / 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: 207-query × 255-memory eval harness with 7 difficulty
clusters. BM25-only baseline
nDCG@10 = 0.7273. With the optional cross-encoder rerank (MEMEE_RERANK_MODEL=cross-encoder/ms-marco- MiniLM-L-6-v2,pip install memee[rerank]):nDCG@10 = 0.7628(+0.0355, p=0.0002). Runpython -m tests.retrieval_evalto reproduce.
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 ClaudeFlat 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 info@memee.eu 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.
PRs welcome. Before opening a large one, a short issue describing the direction saves everyone a round-trip.
pip install -e ".[dev]"
pytest tests/ -vStyle: type hints, English docstrings, 100-char lines, ruff clean. New engines live in src/memee/. Every new behaviour wants a test in tests/.
Memee core is MIT. The optional memee-team package is proprietary, distributed under a separate commercial EULA. See memee.eu for the terms.
Built by people who stopped teaching the same lesson to every new agent.