fazer.ai agents. Apache 2.0.
C-Rank Grade: S (Elite) - 75/100
External PR Merge Rate: 87%
Response Time: 19h
First Timer Success: 100%
fazer-ai/agents has a recorded first-timer success rate of 100.0%. Repositories ranked S typically provide actionable feedback during code reviews and actively nurture new community contributors.
Maintainers in fazer-ai/agents respond to incoming external pull requests in approximately 19.2 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 74.7 places fazer-ai/agents in the S tier.
The external contributor pull request merge rate for fazer-ai/agents is 86.8%, based on public PR activity from non-core contributors.
fazer-ai/agents currently has 2 active issue(s) tagged with beginner-friendly labels like "good first issue", "beginner", or "up-for-grabs".
Active maintainer team in fazer-ai/agents 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/fazer-ai/agents?utm_source=github&utm_medium=badge)
Historical health & responsiveness evolution
External PR acceptance
First PR success rate
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Maintainer review activity heatmap & prime window prediction
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Detected: UTC
Maintainers are most responsive around 19:00 UTC (19:00 UTC in your timezone). Recommended submission window is Tuesday - Thursday from 19:00 to 23:00 UTC (19:00 - 23:00 UTC local).
Maintainer Strategy: Maintainer Sweet Spot: PRs under 150 lines merge in ~12 hours (96% approval). Large PRs (>500 lines) take ~92 hours (39% approval). Keep your diff compact for rapid review.
Split off from #283, which asked for three things. The measurement and the panel are in #285; this is the third, and it is here on its own because it is the only one that needs a decision before it needs code. What is missing The dashboard can now say how long the team took to answer. It cannot say who answered, or how the work is distributed across the people doing it. A supervisor looking at a handoff-heavy week sees one median for the whole inbox and nothing about whether it came from a team of six or from one person carrying all of it. The data is already here Conversation.assigneeId and Conversation.assigneeName are mirrored on every event that carries an assignee, well before this was ever a reporting question: they exist because the attribution gate needs them to decide whether the bot owns a conversation. So a per-assignee cut needs no new column, no new event subscription, and no extra query on the hot path. It is a GROUP BY over rows that are already written. That is exactly
Opening this wide, same as #56: the goal is to agree on the shape, and above all to find out what Chatwoot will and will not allow, before anyone writes code. The ask is easy to state. One conversation, served by different agents as its context changes: a generalist fields the opening, a scheduling specialist takes over when the customer wants a slot, billing takes over when the customer asks about an invoice. Where we are Inbox.agentId is the whole binding, and Conversation has no agentId column at all. Every path that needs to know who answers re-derives it from the inbox: the webhook gate, runAgentTurn, agentNudge, the follow-up sweep, the debounce flush. "Which agent is on this conversation" is not state we keep, and the state we do keep is a property of the channel. So splitting work across agents today means splitting across inboxes, which is the same wall #56 hits from the other side: two agents cannot serve one WhatsApp number. The Chatwoot constraint, and the direction it poin
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When evaluating whether to contribute to fazer-ai/agents, 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 fazer-ai/agents acknowledge new external contributions in approximately 19 hours. Out of all submitted pull requests from non-core authors in the last 180-day window, 86.8% were successfully merged into the primary branch.
fazer-ai/agents has a recorded first-timer success rate of 100.0%. Repositories ranked Elite typically provide actionable feedback during code reviews and actively nurture new community contributors.
The initial maintainer response time averages ~19 hours. 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 74.7 places fazer-ai/agents in the Elite tier.
The external pull request merge rate is 86.8%. GetMerged isolates non-core community contributions so external developers get an accurate benchmark of PR acceptance probability.
Yes, fazer-ai/agents 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 fazer-ai/agents 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.