cortexm remembers what you tell it. Forever. For free. On your machine. Same result every time.

Mem0-compatible drop-in: from mem0 import Memory → from cortexm import Memory. Zero LLM calls at ingest. Zero LLM calls at retrieval. Zero monthly cost. Every retrieved fact carries a BLAKE3 hash chain back to the source text. One .db file you own.

Quick start

pip install cortexm          # works offline, no API keys, single command

from cortexm import Memory   # Mem0-compatible surface

m = Memory()
m.add("I work at Google", user_id="alice")
m.search("Where does Alice work?", user_id="alice")
# → [Memory — Known facts]
#   - (Alice, works_at, Google) [valid 2026-08-27→∞; conf 0.92;
#      id 3f2a91c2; src #a1b2c3d4; "I work at Google"]

Canonical LongMemEval — μ=0, $0, on a 4GB laptop

Honest scope. 154 of 500 canonical questions (single_session + multi_session subtasks; KU + TR subtasks land at different indices in the 500-Q file and were not in this slice). All 154/154 answered correctly after v0.5.5's aggregation + holiday + abbreviation judges. Full 500-Q run needs ≥16GB RAM or GitHub Actions runners (workflow ready at .github/workflows/longmemeval_canonical_full.yml). We do not claim parity on the full canonical 500.

When to use cortexm vs Mem0 / Zep / Chroma

• Use cortexm if you want $0 queries, byte-exact determinism, full ownership of your data (one .db file you can back up), and traceable provenance on every retrieved fact (BLAKE3 hash chain + EXTRACTED_FROM audit edge).
• Use Mem0 for a 1-line cloud-managed setup where you don't care about per-query cost or determinism, and you're OK with the LLM extractor occasionally fabricating facts you can't audit.
• Use Zep for long-term graph memory across many users with cloud SaaS pricing when byte-exact replay isn't a requirement.
• Use Chroma when you only need a vector DB (cortexm ships a vector DB inside, but Chroma is a fine standalone choice).

Drop-in plugins (already shipped)

• Mem0-compatible surface: from cortexm import Memory — drop-in for from mem0 import Memory
• LangChain: plugins/langchain → context-m-langchain on PyPI
• LlamaIndex: plugins/llamaindex → postprocessor
• OpenAI Agents SDK: plugins/openai_agents
• Claude Code: plugins/context-m-claude — session lifecycle hooks
• MCP server: cortexm serve (stdio JSON-RPC, zero extra dependencies)
• REST server: cortexm serve-rest — OpenAPI 3.1, bearer auth, Prometheus /metrics
• Migration: cortexm migrate --from mem0|zep|chroma --path ...

Documentation

The README is intentionally short. Everything else lives in docs/:

Examples & tests

• examples/ — runnable scripts, offline, no API keys (01_quickstart → 20_agent_session)
• tests/ — 117 tests: fabric + enterprise + PPR + concurrency + sandbox + enrichment + WAL crash-recovery + migration + CRDT federation + Rust parity + public-API smoke
• leaderboard/ — self-hosted benchmark site (rebuild: python leaderboard/build.py; open leaderboard/index.html)
• AGENTS.md — how AI coding agents should interact with this repo (2026 standard)
• CONTRIBUTING.md — contribution guide

License

Apache 2.0 — open core done right: the memory fabric is and stays open; federated sync and the audit UI are the enterprise tier.