hippo-memory
Coding agents lose context between sessions, forcing teams to re-explain patterns and domain knowledge repeatedly.
Stop re-teaching your agent. Make your agent's memory work like a brain. Hippo is long-term memory for coding agents. It's a critical layer for your AI harness that connects across your different tools (Cursor, Claude Code, Codex). It keeps your proprietary data completely local, and it actually learns over time.
- Ask Claude to recall previous project patterns and avoid repeating failed approaches across coding sessions.
- Generate documentation automatically by retrieving institutional knowledge from past development decisions and rationales.
- Automate onboarding by having agents access historical context about your codebase architecture and conventions.
Coding agents lose context between sessions, forcing teams to re-explain patterns and domain knowledge repeatedly. Hippo provides persistent, local memory across tools, reducing setup overhead and enabling agents to build on prior work without re-teaching.
Engineering teams using multiple AI coding assistants who need agents to retain project context and proprietary patterns without cloud storage.
https://github.com/kitfunso/hippo-memory
By kitfunso
How to Get It
claude plugins install kitfunso/hippo-memory
Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.
Auto-generated from the tool's public listing — not hands-on verified. Cross-check against the source repo's README before running.
After installing, paste this into Claude:
Recall previous project patterns and avoid repeating failed approaches across coding sessions
Trust Signals Auto-scanned
Community Pulse Growing
Discussed on Hacker News
- Show HN: Hippo, biologically inspired memory for AI agents — Hacker News · 128 pts
1 mentions across 1 sources
Reviewer notes
Auto-scanned review. These are observations, not a security certification.
Scored from trust signals (evidence-eval-v1): 777 GitHub stars; contributors unknown; last commit 0d ago; license MIT.
Things to check
- Scanned, not hands-on tested — this entry was auto-scanned from public metadata (GitHub metrics, license, security flags). No reviewer has run it, and no tool-specific limitations have been documented yet.
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Evaluation
Scored from trust signals (evidence-eval-v1): 777 GitHub stars; contributors unknown; last commit 0d ago; license MIT.