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    akitaonrails

    akitaonrails/ai-memory

    #11 this week

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

    backend
    Rust
    MIT
    12.3K stars
    873 forks
    12.3K GitHub watchers
    Updated Oct 22, 2026
    View on GitHub

    Build with Backblaze B2

    SDKs, agent skills, IDE extensions, and reference pipelines from Backblaze Labs. All open source.

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    Where it stands

    Rank, growth pace and release cadence relative to everything GStars tracks.

    Ranks #31 among Backend repositories tracked this week, and #2 over the last 30 days; #11 of 2,531 repositories GStars tracks this week (top 1%).

    5 releases in the last 90 days, the latest 1 day ago, with 160 contributors on GitHub.

    The biggest single-day gain in the last year, +855 stars on Oct 20, coincided with release v3.1.2.

    Use Cases & Benefits

    • Provides long-term memory management for AI coding agents enabling seamless session handoffs and persistent context across different agent CLIs.
    • Eliminates context loss between agent sessions by maintaining a shared, git-backed markdown wiki with sanitized lifecycle observations and cross-agent handoffs.
    • Use for continuing interrupted coding sessions across different AI agents without re-explaining project context or previous work.
    • Use for maintaining a durable, searchable knowledge base of coding decisions, conventions, and project rules consolidated from AI agent interactions.
    • Use for running a centralized ai-memory server on a homelab or LAN to share persistent AI coding context across multiple machines and users.

    Should you depend on it?

    License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.

    License

    MIT

    Permissive: commercial use, modification and redistribution are allowed with attribution.

    Community files

    • README: present
    • Contributing guide: present

    Watch-outs

    No watch-outs from GStars' checks.

    General guidance from repository metadata, not legal advice. How we score

    From the README

    Long-term memory for AI coding agents. Quit Claude Code mid-task, start OpenAI Codex in the same directory, continue without re-explaining the architecture, the failed approaches, or the open questions.

    Excerpt from the akitaonrails/ai-memory README on GitHub (MIT license). © its authors.Read the full README on GitHub

    Daily snapshots

    Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots

    Jul 25: +15Aug 17: +369Aug 18: +704Aug 19: +563Aug 20: +316Aug 27: +155Aug 28: +108Aug 29: +37Aug 30: +92Aug 31: +126Sep 1: +85Sep 2: +10Sep 3: +186Sep 4: +65Sep 5: +93Sep 6: +68Sep 7: +138Sep 8: +92Sep 9: +110Sep 10: +211Sep 11: +140Sep 12: +97Sep 15: +113Sep 16: +96Sep 17: +56Sep 18: +57Sep 19: +46Sep 20: +129Sep 21: +283Sep 22: +480Sep 23: +142Sep 24: +119Sep 25: +97Sep 26: +44Sep 27: +35Sep 28: +55Oct 2: +56Oct 3: +44Oct 4: +15Oct 5: +24Oct 6: +38Oct 7: +81Oct 8: +64Oct 9: +61Oct 10: +18Oct 11: +42Oct 12: +7Oct 13: +17Oct 18: +317Oct 19: +570Oct 20: +855Oct 21: +572Oct 22: +411

    +11,118 stars over 90 days across 63 snapshot days; best day +855 on Oct 20.

    Forks

    873+744

    Open issues

    46+41

    Compared with peers in Backend

    Fastest-growing repositories GStars tracks in this cohort for the week of Oct 18, by 7-day star growth

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