Causal memory layer for AI agents — MCP server that records decision→outcome relationships. Survives compaction.
A causal memory layer for AI agents. Records decisions and their outcomes as causal relationships, so agents learn from experience across sessions and survive compaction.
Memory frameworks today (Mem0, Zep, Letta, OpenViking, MemOS) store what happened. causal-memory stores why — the causal link between a decision and its outcome. This is the slice every other memory layer misses.
Every agent has the same problem: after N compactions, it forgets why it made past decisions. It reverts to a state where the same bug gets fixed the same wrong way, the same architecture choice gets re-debated, the same lesson gets relearned.
This happens because causal information is the most fragile type under text compaction. Real-LLM benchmark (using grok-build's production compaction prompt):
Compactions (k) Textual recall Causal-table recall --------- 1 100% 100% 2 85% 100% 3 55% 100% 5 45% 100%
The causal table doesn't decay because it lives outside the agent's context window — compaction cannot touch it. See docs/design.md and the full benchmark writeup.
LoCoMo (1,986 questions, deepseek-chat answerer + judge, frozen protocol): overall 65.0% · cats 1–4 59.4% · evidence hit rate 74.4% (BM25) · adversarial abstention 84.3% — three controlled runs with full methodology and failure analysis in docs/benchmarks/locomo.md. Honest reading: factual chit-chat QA is Mem0/Zep's home turf, not ours — but abstention behavior and per-run engineering deltas are documented openly, and the compaction-survival experiment below is the one this system is designed to win.
From the project README.
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A Datalog engine for LLM agent memory: stratified rules, provenance-tracked facts, incremental derivation, and an MCP server that lets your harness use it as a shared brain.
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