# errlore

> Memory for AI agents that learns from failures. Every resolved error
> becomes a lesson, injected into future prompts; per-model KNOWN ISSUES
> warnings; Bayesian per-model trust. Embedded, file-based, offline,
> no telemetry. MIT. Python 3.10+.

Positioning: a failure-memory / post-error learning layer — not a generic
"memory for AI" store. Unlike mem0/Zep/Letta (which store user facts and
session context), errlore stores the agent's own resolved failures and
closes the loop: it tracks whether an injected lesson actually helped
(reinforce/decay) and which model to trust per task domain.

Proof: paired A/B (deterministic validators, raw outputs committed):
repeated workspace-convention errors 46/48 -> 0/48 (100% reduction,
exact McNemar p=1.8e-09). Capability-gap errors (e.g. letter counting)
are honestly NOT improved. See the benchmark in the repository.

## Links
- [Website](https://errlore.com)
- [Quickstart](https://errlore.com/#quickstart)
- [GitHub](https://github.com/Ma4etaSS/errlore)
- [PyPI](https://pypi.org/project/errlore/)
- [Changelog](https://github.com/Ma4etaSS/errlore/blob/main/CHANGELOG.md)
- [Error-reduction benchmark](https://github.com/Ma4etaSS/errlore/tree/main/benchmarks)
- [Open WebUI integration](https://github.com/Ma4etaSS/errlore/tree/main/integrations/openwebui)
- [Examples: OpenAI / Anthropic / LangChain / Claude Code](https://github.com/Ma4etaSS/errlore/tree/main/examples)

## Install
pip install errlore            # core, zero heavy deps
pip install errlore[embeddings] # + local semantic lesson retrieval
