Bisheng vs crewAI
Side-by-side comparison of two AI agent tools
Short answer
- crewAI is growing faster: +1,886 GitHub stars in the last 30 days vs +40 for Bisheng.
- Pick Bisheng for: open LLM application DevOps platform for enterprise workflows, RAG, and agents. Pick crewAI for: framework for orchestrating role-playing, autonomous AI agents.
From GitHub data refreshed daily.
B
Bishengopen-source
Open LLM application DevOps platform for enterprise workflows, RAG, and agents
crewAIopen-source
Framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.
Metrics
| Bisheng | crewAI | |
|---|---|---|
| Stars | 12.0k | 59.3k |
| Star velocity /mo | 40 | 1.9k |
| Commits (90d) | 783 | 307 |
| Releases (6m) | 7 | 10 |
| Downloads (30d, npm + PyPI) | — | 2.4M |
| Overall score | 0.6132724897362102 | 0.841095659378717 |
Pros
- +Built from scratch with no LangChain dependencies, offering clean architecture and fast performance
- +Provides both high-level simplicity for quick setup and low-level control for precise customization
- +Enterprise-ready with CrewAI Flows supporting production deployment and event-driven orchestration
Cons
- -Requires understanding of multi-agent coordination concepts and patterns
- -May be overkill for simple single-agent automation tasks
- -Learning curve associated with role-based agent orchestration design
Use Cases
- •Complex business process automation requiring multiple specialized AI agents with different roles
- •Enterprise workflows needing coordinated AI systems for tasks like content creation, research, and analysis
- •Production-grade multi-agent systems requiring event-driven control and precise task orchestration
FAQ
- Which is more popular, Bisheng or crewAI?
- crewAI has more GitHub stars (59,308 vs 12,021).
- Which is more actively developed, Bisheng or crewAI?
- Bisheng had more commits in the last 90 days (783 vs 307).
- Should I use Bisheng or crewAI?
- 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.