Framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.
C-Rank Grade: A (Welcoming) - 59/100
External PR Merge Rate: 40%
Response Time: 1d
First Timer Success: 13%
crewAIInc/crewAI has a recorded first-timer success rate of 12.9%. Repositories ranked A typically provide actionable feedback during code reviews and actively nurture new community contributors.
Maintainers in crewAIInc/crewAI respond to incoming external pull requests in approximately 25.8 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 58.7 places crewAIInc/crewAI in the A tier.
The external contributor pull request merge rate for crewAIInc/crewAI is 39.6%, based on public PR activity from non-core contributors.
crewAIInc/crewAI does not currently have active "good first issue" tags indexed, but accepts external contributions through standard GitHub issue tracking.
Balanced & professional review environment in crewAIInc/crewAI. Maintains strict focus on technical quality and test standards.
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.
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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 12:00 UTC (12:00 UTC in your timezone). Recommended submission window is Tuesday - Thursday from 12:00 to 16:00 UTC (12:00 - 16:00 UTC local).
Maintainer Strategy: Maintainer Sweet Spot: PRs under 150 lines merge in ~17 hours (50% approval). Large PRs (>500 lines) take ~124 hours (18% approval). Keep your diff compact for rapid review.
Problem AWS Bedrock throttling responses such as ThrottlingException and “Too many tokens, please wait before trying again” are currently being mapped as context-window errors. This sends execution into context recovery instead of retrying the request. Expected behavior Treat Bedrock throttling as a retryable rate-limit error and retry with exponential backoff, consistent with the provider's throttling semantics. The retry behavior should: Detect the relevant Bedrock throttling responses. Avoid triggering context-length recovery for throttling. Apply bounded exponential backoff between attempts. Preserve the existing behavior for genuine context-window errors. Include unit tests covering throttling and non-throttling error classification. Related Linear ticket: OSS-169 This issue is intentionally separate from guarding the context-recovery path when llm.acall() is unavailable (OSS-170).
Feature Area Core functionality Is your feature request related to an existing bug? Please link it here. Related: #7303 (o1 / o1-pro / o3 fall back to the 8k default because they are missing from some of the duplicated tables). That class of bug is what this issue is meant to stop. Describe the solution you'd like Context window sizes are hardcoded in six places. Adding or updating a model means editing llm.py and the native provider (and sometimes Azure or Bedrock too). The copies have already drifted. Current maps Location Used by lib/crewai/src/crewai/llm.py — LLM_CONTEXT_WINDOW_SIZES LiteLLM fallback lib/crewai/src/crewai/llms/providers/openai/completion.py Native OpenAI lib/crewai/src/crewai/llms/providers/azure/completion.py Native Azure lib/crewai/src/crewai/llms/providers/gemini/completion.py Native Gemini lib/crewai/src/crewai/llms/providers/anthropic/completion.py Native Anthropic lib/crewai/src/crewai/llms/providers/bedrock/completion.py Native Bedrock
Feature Area Integration with external tools Is your feature request related to a an existing bug? Please link it here. NA — tracked internally as OSS-153. Describe the solution you'd like Add first-class support for Google's Gemini 3.8 Flash (gemini-3.8-flash) so users can run crews with: from crewai import LLM llm = LLM(model="gemini/gemini-3.8-flash") Official model ID: gemini-3.8-flash (GA, 1,048,576 token context window). The native Gemini path already routes unknown gemini-* models to the Gemini SDK, but the model is missing from the allowlists, context-window tables, and CLI picker. That means typed constants, documented context size, and the crewai create model list do not include it. Suggested implementation (good first issue) This is a constants + table update. Follow the same pattern as gemini-3-flash-preview / gemini-3-pro-preview. Register the model id in lib/crewai/src/crewai/llms/constants.py Add "gemini-3.8-flash" to the GeminiModels Literal (near the top, with th
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When evaluating whether to contribute to crewAIInc/crewAI, 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 crewAIInc/crewAI acknowledge new external contributions in approximately 1 days+. Out of all submitted pull requests from non-core authors in the last 180-day window, 39.6% were successfully merged into the primary branch.
crewAIInc/crewAI has a recorded first-timer success rate of 12.9%. Repositories ranked Welcoming typically provide actionable feedback during code reviews and actively nurture new community contributors.
The initial maintainer response time averages ~1 days+. 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 58.7 places crewAIInc/crewAI in the Welcoming tier.
The external pull request merge rate is 39.6%. GetMerged isolates non-core community contributions so external developers get an accurate benchmark of PR acceptance probability.
crewAIInc/crewAI does not have open beginner labels indexed currently, but external PRs for bugs and documentation improvements are evaluated via normal issue triage.
All metrics displayed for crewAIInc/crewAI 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.