ReMe: Memory Management Kit for Agents - Remember Me, Refine Me.
C-Rank Grade: S (Elite) - 72/100
External PR Merge Rate: 82%
Response Time: -
First Timer Success: 33%
agentscope-ai/ReMe has a recorded first-timer success rate of 33.3%. Repositories ranked S typically provide actionable feedback during code reviews and actively nurture new community contributors.
Maintainers in agentscope-ai/ReMe respond to incoming external pull requests in approximately 0.0 hours on average. Keeping PRs focused on single tasks and ensuring tests pass helps maintainers review faster.
The C-Rank™ system evaluates GitHub projects on a 0–100 scale using real data: PR merge rates, review turnaround time, active maintainer presence, and first-time contributor success. A score of 71.6 places agentscope-ai/ReMe in the S tier.
The external contributor pull request merge rate for agentscope-ai/ReMe is 81.9%, based on public PR activity from non-core contributors.
agentscope-ai/ReMe currently has 2 active issue(s) tagged with beginner-friendly labels like "good first issue", "beginner", or "up-for-grabs".
Active maintainer team in agentscope-ai/ReMe welcoming community pull requests and bug fixes.
Average Response Latency
Tracks hours until a maintainer leaves a review, comment, or PR response.
External Acceptance Rate
Percentage of community pull requests successfully merged into main.
First PR Conversion
Rate at which developers submitting their first repository PR succeed.
Show contributors that your repository actively reviews and merges external pull requests.
[](https://getmerged.abhishekco.de/agentscope-ai/ReMe?utm_source=github&utm_medium=badge)
Historical health & responsiveness evolution
External PR acceptance
First PR success rate
50% receive response in
90% receive response in
Maintainer review activity heatmap & prime window prediction
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Detected: UTC
Maintainers are most responsive around 09:00 UTC (09:00 UTC in your timezone). Recommended submission window is Wednesday - Friday from 06:00 to 10:00 UTC (06:00 - 10:00 UTC local).
Maintainer Strategy: Maintainer Sweet Spot: PRs under 150 lines merge in ~16 hours (96% approval). Large PRs (>500 lines) take ~115 hours (37% approval). Keep your diff compact for rapid review.
Feature Request: Horizontal Scaling / Cluster Deployment Support Is your feature request related to a problem? Yes. ReMe is designed as a local-first, single-instance application. While this works great for personal/single-agent use cases, it becomes a bottleneck when deploying ReMe as a shared memory service in production environments (e.g., Kubernetes / cloud VM clusters) that require horizontal scaling. Currently, running multiple ReMe replicas causes three critical problems: Index inconsistency: Each replica holds its own in-memory FAISS vector index and BM25 keyword index. Search results differ across replicas, and new replicas need time to rebuild indexes on startup. Duplicate background task execution: The 3 background watch jobs (index_update_loop, resource_watch_loop, digest_watch_loop) and the cron job (dream_cron) run on every replica, causing duplicate file writes and potential race conditions on the shared workspace. No built-in authentication: The HTTP service has n
Background OpenClaw users need a native way to use ReMe as persistent, file-native memory across agent sessions. The integration should follow OpenClaw plugin conventions while keeping user-owned ReMe workspace files as the source of truth and connecting to a separately managed ReMe service. Changes Add an installable OpenClaw plugin under plugins/openclaw Expose ReMe recall workflows through OpenClaw-compatible tools and skills Use session lifecycle hooks to record durable session knowledge automatically without blocking agent shutdown Support semantic search, graph traversal, state lookup, and source-backed reads Add configuration for the ReMe service endpoint and clear health and failure handling Add installation documentation and focused tests for recall and automatic recording
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When evaluating whether to contribute to agentscope-ai/ReMe, response velocity and maintainer engagement are crucial. GetMerged continuously tracks pull request trajectories, first-comment latency, and code review rounds to help developers avoid submitting pull requests to backlogged repositories.
Currently, maintainers of agentscope-ai/ReMe acknowledge new external contributions in approximately -. Out of all submitted pull requests from non-core authors in the last 180-day window, 81.9% were successfully merged into the primary branch.
agentscope-ai/ReMe has a recorded first-timer success rate of 33.3%. Repositories ranked Elite typically provide actionable feedback during code reviews and actively nurture new community contributors.
The initial maintainer response time averages ~-. Keeping PRs scoped to single concerns and ensuring CI checks succeed will optimize review turnaround.
The C-Rank™ index scores repositories on a 0 to 100 scale using an objective formula: external PR merge rates, initial response speed, active maintainer count, and first-time contributor retention. A score of 71.6 places agentscope-ai/ReMe in the Elite tier.
The external pull request merge rate is 81.9%. GetMerged isolates non-core community contributions so external developers get an accurate benchmark of PR acceptance probability.
Yes, agentscope-ai/ReMe currently has 2 active issue(s) tagged with beginner-friendly labels. You can inspect these directly from the repository issues tab.
All metrics displayed for agentscope-ai/ReMe are automatically retrieved via the public GitHub API and recalculated daily. Insider pull requests submitted by repository owners or organization members are excluded from merge rate calculations to preserve objective external contributor statistics.