Show HN: Komi-learn – continuous memory and self-improvement for coding agents

Komi-learn, a new tool for coding agents, automatically learns a user's coding style, stack, and fixes from sessions and recalls them in future sessions without commands. The tool works with Claude Code and Codex, distilling durable lessons in the background and loading relevant ones at the start of each session. It includes an optional community pool for sharing general lessons, with contributions scrubbed of identifying information and requiring user approval before sharing.

Continuous memory and self-improvement for coding agents. It learns how you work and recalls it automatically, with no commands. Works with Claude Code and Codex. It watches a session, distills durable lessons in the background your style, your stack, fixes that worked , and loads the relevant ones at the start of the next session. No slash commands, nothing to save by hand. The idea is from Hermes Agent https://github.com/nousresearch/hermes-agent ; this is my own take, generalized across hosts with an optional shared layer the community pool, below . It's early. The core loop is built and CI-tested, but it hasn't been battle-tested across a lot of real sessions yet. Feedback and bug reports are welcome. pip install komi-learn komi-learn install or: komi-learn install --host codex install runs a short interactive setup, then recall and background learning start in your next session. If you already use Claude Code you're already logged in. For scripts, komi-learn install --yes takes the defaults. From source: git clone https://github.com/kurikomi-labs/komi-learn cd komi-learn pip install -e . komi-learn doctor check the install and what to fix komi-learn status config + how much it has learned komi-learn config change any setting menu, or config set <key <val komi-learn sync pull the latest community learnings komi-learn queue review/approve/reject what you'd contribute to the pool komi-learn forget <x erase learnings matching <x archive, or --hard to delete komi-learn uninstall remove the hooks keeps your data; --purge to wipe You can change anything after install, e.g. komi-learn config set recall.semantic false or leave the pool with komi-learn config set pool.repo url "" . - Recall: at session start, learnings relevant to the current context are loaded. - Distill: after the session, a background pass reads the transcript and extracts durable lessons corrections, techniques, fixes . - Curate: over time it merges overlapping lessons and archives stale ones. - Share optional : general lessons can be contributed to the community pool, but only ones you approve. It tries not to learn the wrong things. Secrets, machine-specific paths, one-off failures, and "tool X is broken" complaints are filtered out by a deterministic check before the LLM ever sees them. A public pool of general agent lessons, stored as a GitHub repo of signed Markdown files no server . If you opt in, you get lessons other people's agents figured out, and you can contribute your own. Contributions are scrubbed of anything identifying and never leave your machine without your approval each one opens a PR you reviewed . Learnings are content-addressed BLAKE3 and signed Ed25519 ; one signed by more distinct GitHub accounts ranks higher when pulled. That account count is Sybil-resistant but not Sybil-proof, so it's an advisory signal, not a hard trust gate. Recalled community items are labelled and treated as untrusted input. Details: pool-repo-template/CONTRIBUTING.md /kurikomi-labs/komi-learn/blob/main/pool-repo-template/CONTRIBUTING.md . No setup or API key needed: python examples/demo loop.py It runs two sessions: you correct the agent in the first, and the second shows it recalling that with nothing typed. - Python 3.10+ - Claude Code or Codex the agent it plugs into - A working model for the distill step: your existing Claude Code login, or komi-learn login , or an API key via --api-key . komi-learn install verifies these with a real model call and stops with fix steps if something's missing. At runtime, if a hook can't reach the model it skips that learning pass rather than interrupting your session. The engine has no required dependencies. Optional extras add real signing pip install komi-learn crypto and local semantic recall smart ; without them it falls back to a stdlib hash and keyword search. To run your own pool, see pool-repo-template/ /kurikomi-labs/komi-learn/blob/main/pool-repo-template . MIT. Issues and PRs welcome.