Open-source AI orchestration framework for building context-engineered, production-ready LLM applications. Design modular pipelines and agent workflows with explicit control over retrieval, routing, memory, and generation. Built for scalable agents, RAG, multimodal applications, semantic search, and conversational systems.
C-Rank Grade: A (Welcoming) - 61/100
External PR Merge Rate: 66%
Response Time: 2d
First Timer Success: 58%
deepset-ai/haystack has a recorded first-timer success rate of 57.7%. Repositories ranked A typically provide actionable feedback during code reviews and actively nurture new community contributors.
Maintainers in deepset-ai/haystack respond to incoming external pull requests in approximately 41.0 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 61.1 places deepset-ai/haystack in the A tier.
The external contributor pull request merge rate for deepset-ai/haystack is 66.4%, based on public PR activity from non-core contributors.
deepset-ai/haystack currently has 1 active issue(s) tagged with beginner-friendly labels like "good first issue", "beginner", or "up-for-grabs".
Open-source AI orchestration framework for building context-engineered, production-ready LLM applications. Design modular pipelines and agent workflows with explicit control over retrieval, routing, memory, and generation. Built for scalable agents, RAG, multimodal applications, semantic search, and conversational systems.
Balanced & professional review environment in deepset-ai/haystack. 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.
[](https://getmerged.abhishekco.de/deepset-ai/haystack?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 07:00 UTC (07:00 UTC in your timezone). Recommended submission window is Monday - Wednesday from 07:00 to 11:00 UTC (07:00 - 11:00 UTC local).
Maintainer Strategy: Maintainer Sweet Spot: PRs under 150 lines merge in ~29 hours (83% approval). Large PRs (>500 lines) take ~214 hours (30% approval). Keep your diff compact for rapid review.
Describe the bug TiktokenCounter.count() raises ValueError when the conversation or tool schemas contain a literal tiktoken special-token string such as <|endoftext|>. haystack/token_counters/tiktoken_counter.py:77 encodes the rendered conversation with Encoding.encode(): text_tokens = len(self._encoder.encode(_rendered_conversation(messages) + _rendered_tools(tools))) encode() defaults to disallowed_special="all", so it raises on any text containing a special-token marker. Since allowed_special also defaults to set(), encode() can never actually emit a special token here — it can only raise. The strict check buys nothing and only turns ordinary source text into a crash. This is the same bug that #12853 fixed for DocumentSplitter and RecursiveDocumentSplitter. TiktokenCounter is the one remaining place in haystack/ that encodes user-supplied text with a bare .encode(). Error message ValueError: Encountered text corresponding to disallowed special token '<|endoftext|>'. If you want th
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When evaluating whether to contribute to deepset-ai/haystack, 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 deepset-ai/haystack acknowledge new external contributions in approximately 1 days+. Out of all submitted pull requests from non-core authors in the last 180-day window, 66.4% were successfully merged into the primary branch.
deepset-ai/haystack has a recorded first-timer success rate of 57.7%. 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 61.1 places deepset-ai/haystack in the Welcoming tier.
The external pull request merge rate is 66.4%. GetMerged isolates non-core community contributions so external developers get an accurate benchmark of PR acceptance probability.
Yes, deepset-ai/haystack currently has 1 active issue(s) tagged with beginner-friendly labels. You can inspect these directly from the repository issues tab.
All metrics displayed for deepset-ai/haystack 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.