On 130 adversarial scenarios designed so the query semantically matches bad advice better than good advice:
→ plain vector search: 0–3% correct
→ Roampal: 100% correct
Efficiency: 63% fewer tokens — retrieves 1 outcome-verified result vs RAG's top-3 semantic matches.
Core mechanism
• AI marks outcome → success +0.2, failure −0.3 (explicit or auto-detected from conversation)
• New memories: 70 % embedding / 30 % outcome score
Proven memories (5+ uses): 40 % embedding / 60 % outcome score
Key difference from Mem0/Zep
They update on relevance/consistency. Roampal updates on real outcomes.
Reproducible results (JSON in repo):
Plain Vector Roampal
Coding (30) 3.3 % 100 % ← p=0.001, Cohen’s d=7.49
Learning curve: 58 % → 93 % accuracy as memories accumulate (p=0.005, d=13.4)
I’m not a programmer — psychology degree, MBA, day job managing $6.5 M contracts. Nine months of nights & weekends with only Cursor, Claude, and copy-paste.
100 % local · runs offline with Ollama, LM Studio, or Claude Desktop · MIT license · no telemetry · no signup
GitHub (full benchmarks + all 130 adversarial scenarios):
https://github.com/roampal-ai/roampal
Website + demo video:
Happy to answer technical questions or take brutal feedback in the comments.