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akitaonrails/ai-memory

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

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Summary Information

Updated 1 hour ago
Type:Library / SDKCategory(s):AI Coding AgentsRAG, Knowledge & MemoryAI Agents & LLM Apps
Added to GitGenius on August 17th, 2026
Created on May 21st, 2026
Open Issues & Pull Requests: 15 (+3)
GitHub issues: Enabled
Number of forks: 643
Total Stargazers: 9,142 (+0)
Total Subscribers: 50 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 7.0 hours
Mean response time: 12.2 hours
90th percentile: 25.7 hours
Tracked items: 403

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Maintainer activity

1 person did triage or write work on this repository in the last 12 months.

Counts unlabeled, assigned, unassigned, milestoned, demilestoned, locked, unlocked over the last 12 months. These are issue and pull request events that require triage or write permission. Commits and code review are not counted. labeled and renamed are excluded because GitHub issue forms record the issue author as the actor. Figures from October 11, 2026. This count is not comparable across projects: each project's automation decides which of these events a person emits.

How this project is maintained

Practically every issue opened in the past year has drawn a reply. 99% of issues opened in the past year have since been closed. Three people close 99% of everything that gets resolved.

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Issue Activity (beta)

Open issues: 6
New in 7 days: 39
Closed in 7 days: 38
Avg open age: 0 days
Stale 30+ days: 0
Stale 90+ days: 0

Recent activity

Opened in 7 days: 38
Closed in 7 days: 36
Comments in 7 days: 23
Events in 7 days: 103

Top labels

  • enhancement (62)
  • bug (42)
  • help wanted (5)
  • rfc (5)
  • documentation (1)
  • windows (1)

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Detailed Description

ai-memory is a persistent memory system for AI coding agents that enables continuity across different agent vendors and CLI sessions.

The tool solves the problem of context loss when switching between different AI coding agents or interrupting and resuming work. Rather than re-explaining project architecture, failed approaches, and open questions each time, ai-memory stores agent state and reasoning in a structured format that persists across sessions. This allows developers to pause work with one agent vendor and seamlessly continue with another, or resume the same agent later without losing accumulated context.

Developers should adopt this tool if they work with multiple AI coding agents or frequently interrupt and resume coding tasks. It suits projects where maintaining continuity across agent handoffs is valuable, particularly in exploratory development or when comparing different agent capabilities on the same codebase. The README positions it specifically for scenarios like quitting Claude Code mid-task and continuing with OpenAI Codex in the same directory without repeating explanations.

The project responds to issues and pull requests within a day. Development activity centers on enhancements and bug fixes. The project is growing steadily.