Haystack vs LLMFlows
Side-by-side comparison of two AI agent tools
Short answer
- LLMFlows has had no commit in 36 months; Haystack is actively maintained (761 commits in the last 90 days).
- Haystack is growing faster: +319 GitHub stars in the last 30 days vs +0 for LLMFlows.
- Pick Haystack for: open-source AI orchestration framework for modular RAG pipelines and agent workflows. Pick LLMFlows for: lLMFlows - Simple, Explicit and Transparent LLM Apps.
From GitHub data refreshed daily.
Haystackopen-source
Open-source AI orchestration framework for modular RAG pipelines and agent workflows
LLMFlowsopen-source
LLMFlows - Simple, Explicit and Transparent LLM Apps
Metrics
| Haystack | LLMFlows | |
|---|---|---|
| Stars | 26.6k | 708 |
| Star velocity /mo | 318.73015873015873 | 0.15873015873015872 |
| Commits (90d) | 761 | 0 |
| Releases (6m) | 10 | 0 |
| Overall score | 0.8002744436599727 | 0.1431426946004791 |
Pros
- +Production-ready architecture with robust testing and type safety (Mypy, comprehensive test coverage)
- +Modular pipeline design allows for flexible composition and customization of AI workflows
- +Strong community adoption with 24,000+ GitHub stars and active development by deepset
- +Complete transparency with no hidden prompts or LLM calls, making debugging and monitoring straightforward
- +Minimalistic design with clear abstractions that don't compromise on flexibility or capabilities
- +Explicit API design that promotes clean, readable code and easy maintenance of complex LLM workflows
Cons
- -Learning curve may be steep for developers new to AI orchestration frameworks
- -Complexity might be overkill for simple LLM integration use cases
- -Relatively small community with 707 GitHub stars, which may limit community support and resources
- -Minimalistic approach might require more manual setup compared to more feature-rich frameworks
- -Limited built-in integrations compared to larger LLM frameworks, requiring more custom implementation
Use Cases
- •Building production RAG systems with sophisticated document retrieval and context management
- •Creating AI agent workflows with explicit control over routing and decision-making processes
- •Developing modular AI pipelines that require custom retrieval and context engineering components
- •Building transparent chatbots where every LLM interaction needs to be traceable and debuggable
- •Creating question-answering systems that combine multiple LLMs with vector stores for document retrieval
- •Developing AI agents with complex multi-step workflows that require explicit control over each LLM call
FAQ
- Which is more popular, Haystack or LLMFlows?
- Haystack has more GitHub stars (26,641 vs 708).
- Which is more actively developed, Haystack or LLMFlows?
- Haystack had more commits in the last 90 days (761 vs 0).
- Should I use Haystack or LLMFlows?
- Compare their capabilities, limitations and "best for" notes above. Both are open source, so trying each on a small task is the fastest way to decide.