fixcache 0.4.4

The procedural memory layer for AI coding agents. Fingerprints stderr, matches to learned fix recipes, tracks efficacy. Zero dependencies, local-first, MCP-native.

fixcache

The procedural memory layer for AI coding agents.

Your agents keep hitting the same errors. fixcache fingerprints every failure, remembers the fix, and surfaces it the next time any agent on any repo hits the same wall.

The third leg of agent memory

Memory type	What it stores	Example tool
Semantic	facts, preferences, knowledge	mem0, cognee
Episodic	conversation history, session traces	Letta, claude-mem
Procedural	reusable fixes for errors that broke you before	fixcache

mem0 and Letta are passive filing cabinets — retrieve on cosine similarity. fixcache is an active immune system — it recognizes the exact shape of failure and intercepts it with a proven recipe.

The 30-second demo

# 1. Teach it a fix
$ fixcache fix "ModuleNotFoundError: No module named 'scikit-learn'" \
    --steps "pip install scikit-learn" "restart kernel"
Stored fix: recipe_id=...
  fingerprint : 714b7ee1df9f3bec (ModuleNotFoundError/python)

# 2. Run any command — fixcache watches stderr
$ fixcache watch --cmd "pytest tests/"
Traceback (most recent call last):
ModuleNotFoundError: No module named 'scikit-learn'

💡 fixcache: matched fingerprint 714b7ee1df9f3bec
   seen 2x — unrated so far

  [1] Fix for: ModuleNotFoundError: No module named 'scikit-learn'  (conf=0.5, freq=1)
      → pip install scikit-learn
      → restart kernel

That's the product. One fingerprint per class of failure. One fix per fingerprint. Hit rate compounds across every repo and every agent that uses it.

What fixcache is

• Procedural memory, not semantic — retrieves by exact pattern match on normalized error signatures, not by cosine similarity on embeddings
• MCP-first, framework-agnostic — works with Claude Code, Cursor, Windsurf, Codex, or any MCP-compatible tool. No runtime lock-in.
• Zero cloud, zero deps — single SQLite file, WAL mode, FTS5 BM25 search. stdlib only. No API keys. No telemetry leaving your machine.
• Privacy-preserving by construction — absolute paths, hex IDs, line numbers, quoted literals all redacted before hashing. Safe to share the sanitized corpus.
• Local-first — your failure history is yours.

What fixcache is NOT

• Not a general-purpose memory layer (that's mem0)
• Not an agent runtime (that's Letta)
• Not an error tracker for humans (that's Sentry)
• Not a Claude-only plugin (that's claude-mem)

Install

pip install fixcache

5 CLI verbs worth learning

fixcache watch <cmd>               # run a command, catch stderr, surface fixes
fixcache fix <signature> --steps   # teach a recipe for an error class
fixcache darwin classify           # stateless classification: stderr → fingerprint + recipe
fixcache darwin report <id>        # record outcome — closes the Bayesian feedback loop
fixcache stats                     # hit rate + efficacy dashboard

5 MCP tools worth connecting

{
  "mcpServers": {
    "fixcache": {
      "command": "fixcache-mcp"
    }
  }
}

Tool	What it does
fix	Teach a recipe for a specific error class
darwin_classify	Classify stderr → matched fingerprint + ranked recipes
match_procedure	Alternative match entry point
report_outcome	Record fix success/failure — closes the Bayesian loop
darwin_stats	Efficacy dashboard + hit rate per fingerprint

The honest benchmark

On a held-out corpus of 123 real GitHub errors scraped from public issues — the author never saw these samples before running fingerprint.py on them:

Metric	Value	What it means
Samples	123	real errors from real repos
Unique fingerprints	69	distinct hashes
Collapse ratio	1.78x	on real data (was 4.09x on hand-tuned samples)
Within-class precision	48.6%	average class agreement
Across-class purity	100%	no false matches between classes — fixes never mis-fire

fixcache collapses the common two-thirds of Python errors well. It fails open on Rust compiler warnings, CUDA-OOM variants, and Node Cannot read properties — all documented, all fixable with targeted redactor additions, none require structural redesign.

The metric that matters for safety: across-class purity is 100%. A fix learned for one error class never mis-fires on another. The dataset stays safe even where it fails to compound.

Reproduce it yourself:

python bench/run_collapse.py --corpus bench/corpus_v2.jsonl

See bench/corpus_v2_notes.md for the source URLs, methodology, and documented weak points.

Why this is defensible

The fingerprinter is ~250 lines of regex. Anyone can clone that. The moat is the corpus: fingerprints + measured outcomes. Every time a developer uses fixcache and rates a fix, that data point makes the next match better for everyone. No funded competitor can replicate a corpus of real user-rated agent failures without building the tool first.

That's the flywheel. Ship it, use it, rate it, share it.

See also

• fixcache corpus export — 123 real GitHub errors with equivalence class labels
• fixcache corpus notes — methodology, source URLs, documented weak points
• CUT_STATUS.md — what was cut from the original lore-memory kitchen sink and why
• Miles0sage/lore-memory-full (private) — archive of the pre-cut version if you need any of the cut features

License

MIT — see LICENSE