akitaonrails

akitaonrails / ai-memory

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Solution for long term memory for agent coding CLIs and to facilitate hand off between different agent vendors

15
0
100% credibility
Found May 22, 2026 at 15 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Rust
AI Summary

ai-memory is a long-term memory system for AI coding assistants. It runs as a background service that automatically captures everything that happens during coding sessions - the prompts you give, the tools your AI uses, the decisions made, and the files touched. When a session ends, it creates a summary. When a new session starts (even with a different AI tool), it automatically hands off the context so you never have to re-explain your project. Knowledge is stored as simple text files you can open in any text editor or markdown viewer, and old or unused memories naturally fade away over time while important knowledge compounds.

How It Works

1
💭 The Frustration of Starting Over

You ask your AI coding assistant to work on a project, spend an hour explaining the architecture, then realize you have to explain everything again next time.

2
🔌 One Command to Remember Everything

You start a special memory service with a single command. It runs quietly in the background, ready to capture everything your AI assistant does.

3
🔗 Your AI Assistant Gets Connected

You connect your favorite AI coding tool to the memory service. From now on, every session, every decision, every file touched gets saved automatically.

4
✨ Work Happens, Memory Takes Care of Itself

You work normally. Your AI assistant writes code, runs commands, makes decisions. Behind the scenes, your memory service quietly records everything without interrupting your flow.

5
Time to Switch Tools?
🤖
Same Assistant, New Session

Your AI assistant starts up and automatically sees everything from your last session - what you were working on, what failed, what's still open.

🔄
Different Assistant

You switch to a completely different AI coding tool. It connects to your memory and immediately knows the context - no re-explaining needed.

6
🔍 Your Knowledge is Searchable

You can ask your memory service questions like 'what did we decide about authentication?' and get back relevant notes from past sessions.

🎉 Continuity at Last

Your AI coding assistant remembers your project across sessions, across tools, across days. You never have to re-explain the same context twice.

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AI-Generated Review

What is ai-memory?

ai-memory is a long-term memory system for AI coding agents like Claude Code and OpenAI Codex, written in Rust. It solves the fundamental problem where AI agents lose all context when a session ends—your next session starts from scratch. The system captures everything automatically through lifecycle hooks, then compiles it into a structured wiki on disk (plain markdown in a git repo) rather than relying on raw log retrieval. When you switch between agents or pick up work days later, the system hands off context automatically without you typing anything special.

Why is it gaining traction?

The killer feature is automatic cross-agent handoff: quit Claude Code mid-task, start Codex in the same directory, and it surfaces a summary of where you left off without any manual intervention. Unlike other memory tools that require you to manually invoke write commands or wrap a vector database in a chat shim, ai-memory captures everything passively through the agent's own lifecycle events. The wiki lives as plain markdown files you can grep, open in Obsidian, or diff like any code. It runs in three tiers: completely free with no API keys (FTS5 search + rule-based summaries), or with optional LLM consolidation and hybrid vector search for better recall.

Who should use this?

Developers who work across multiple AI coding agents or frequently switch between projects will get the most value. If you've ever had to re-explain your codebase architecture to an agent after context was lost, this eliminates that friction. Teams using Claude Code or Codex in long-running sessions where context window compaction happens will appreciate the automatic PreCompact checkpointing. The zero-LLM tier makes it accessible for experimentation without any API costs, while the full LLM-driven consolidation path suits developers who want higher-quality session summaries.

Verdict

This is a genuinely novel approach to agent memory that deserves attention, but the 1.0% credibility score and 15 stars reflect a very early-stage project—v0.2 with limited real-world battle-testing. The documentation is thorough and the architecture is well-reasoned, but adoption requires installing lifecycle hooks and running a server, which adds friction compared to simpler alternatives. Worth watching closely and trying in low-stakes projects, but not ready for mission-critical workflows until the community validates it at scale.

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