Plug-in failure memory layer for coding agents that surfaces past mistake traces before each new attempt

Customer: Solo dev or indie hacker building a LeetCode-style coding agent (Python, LangGraph/asyncio) who’s demoing it to employers or selling it as a study tool — tired of watching their agent repeat the same off-by-one errors across problems

Problem: Coding agents have no cross-problem memory: fail on sliding window problem #1, fail identically on #47. Each session starts amnesia. Chroma is already in the stack but nobody wires it to failure traces — just embeddings of problem statements

Pricing: one-time — $800 in first 60 days (40 sales at $19)

Why now

Papers on reasoning trace compression + failure context preservation just dropped — early movers can ship a concrete implementation before the blog-post crowd catches up; LangGraph adoption spiked in 2025 making the integration obvious

Go-to-market

1. Ship open-source Python package (pip install agent-failure-memory) with MIT license — targets devs already using Chroma/sentence-transformers, zero friction to try
2. Post teardown on r/MachineLearning and r/LocalLLaMA: ‘I wired failure traces into a LangGraph coding agent — here’s what it learned across 50 LeetCode problems’ with before/after pass-rate numbers
3. Sell $19 ‘pro bundle’ on Gumroad: extended example notebooks, pre-tuned retrieval configs for Claude vs GPT-4o, Discord access — announce in same Reddit thread
4. DM 20 devs who publicly posted LangGraph coding-agent repos on GitHub in last 6 months, offer free pro bundle for honest feedback / tweet

Moat (or lack thereof)

No moat. Any dev can clone and extend in a weekend. Edge is shipping first + owning the SEO/community anchor post. If it works, OpenAI or Anthropic bakes this into their agent SDKs within 12 months — exit before then or pivot to vertical (e.g., failure memory for SQL agents specifically)