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Komi-learn

continuous memory and self-improvement for coding agents

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HN · launched May 31, 2026 · 27 upvotes (top 21%) · 0 views

Niche: ai agent memory infrastructure · 350 launches, 11 in the last 7 days ▼ -58% · Explore this niche →

Category and tags

  • Horizontal
  • Dev tools
  • Developer
  • AI-native
  • Commercial product

Visit website ↗ Source listing ↗

Could you build this?

Partial Creating a basic prompt-chaining agent that logs errors to a vector store is easy, but implementing a robust continuous memory and self-improving reasoning architecture for coding agents requires novel evaluation harnesses and memory compaction mechanisms.

What it would actually take: A functional system needs a local or hosted harness that intercepts agent tool calls and test outputs, stores episodic traces in a graph or vector database, and applies automated reflection/distillation algorithms to rewrite system guidelines or skill libraries. Key challenges include avoiding context pollution, preventing catastrophic forgetting in agent prompts, and building benchmark-driven verification loops. This requires expertise in agentic memory architectures and reinforcement learning from execution feedback.

Discussion

3 comments analyzed.

Competitors mentioned: Structured markdown files, LoCoMo (benchmarking tool)

Concerns raised: No evidence it actually works better than markdown files, Lacks benchmarks for long context sessions, Agents often ignore or don't read markdown files in full

Feature requests: Automatic injection of relevant information into context

Competitors

Other products that read as similar to this one : 3905 launches clear the similarity bar, closest 8 shown.

Attention rank: #793 of 3906 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).

Launched 214 days after the earliest competitor.

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