Memee

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 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.

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.

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.

The architecture, on one page

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

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

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.

Benchmarks

• 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). Run python -m tests.retrieval_eval to
  reproduce.

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

Pricing

Flat per team. Same engine in every tier.

Between fifteen and a hundred seats, and no SOC 2 needed? Email [email protected] 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.

Memory, compared

Memory products cluster around two shapes: per-user context (ChatGPT, Hermes) or per-agent state (Letta, Mem0, Zep). Memee is the only one built per-organisation, with confidence scoring, cross-project propagation, and a flat-fee team tier.

Sources, verified April 2026: anthropic.com/news/skills, mem0.ai/pricing, letta.com/pricing, getzep.com, openai.com/memory, hermes-agent.nousresearch.com.

Three fair questions

From the launch thread. Answered without flinching.

Doesn't an MCP server just add to my context window?

It would, if it dumped. Memee doesn't. The router has a hard 500-token cap and lands at ~40 tokens on average per briefing. Measured, not aspirational.

Compare that to the file you already ship: median CLAUDE.md / AGENTS.md across 27 popular OSS repos is ~2,160 tokens, loaded in full on every session and ridden along on every turn. That file only grows. Memee is the cure for context bloat, not a new source of it.

Don't Claude Skills already do this?

Skills are good. They're also Claude-native, hand-authored, static, and per-folder. Memee is none of those things.

It writes from the conversation, not from your hand. It scores what it captures (cross-model ×1.3, cross-project ×1.5). It ages out what stops earning its keep on the lifecycle ladder, hypothesis → tested → validated → canon → deprecated. The same canon answers GPT in Cursor, Gemini in Continue, and Llama wherever you run it. Skills are a folder. Memee is a memory.

How is this different from Mem0, Letta, Zep, ChatGPT memory?

Most memory projects remember conversations: you talked about X last Tuesday, here it is again. That's chat history with a vector index.

Memee earns canon. A claim arrives at 0.5 confidence and goes nowhere until a second model family agrees and a second project re-uses it. On OrgMemEval v1.0 that capability gap shows up as 92.2 / 100 against a competitor baseline of 2.3. Not because the others are bad. Because they aren't built for the job.

Conversation memory remembers what was said. Institutional memory remembers what was learned.

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/.

License

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.