CrewAI vs LangGraph
Agent-Assisted
A multi-agent platform that enables developers to build, deploy, and manage AI agents that work together to accomplish complex tasks.
Agent-Assisted
A framework for building stateful, multi-actor applications with LLMs using directed graphs and agent orchestration.
Summary
- CrewAI scores 3.6/5 and LangGraph scores 3.9/5 on the Dark Factory Score, a difference of 0.3.
- The widest gap is Self-Correction: CrewAI 3.2, LangGraph 4.0.
- LangGraph scores higher on code quality, integration and self-correction.
- They score the same on autonomy and project complexity.
- Both are listed as open source.
- Both are listed under Orchestration and Agent Frameworks.
Dimension by dimension
| Dimension | Weight | CrewAI | LangGraph |
|---|---|---|---|
| Autonomy | 28% | 4.2 | 4.2 |
| Code Quality | 20% | 2.8 | 3.5 |
| Integration | 17% | 3.5 | 3.8 |
| Self-Correction | 15% | 3.2 | 4.0 |
| Project Complexity | 20% | 4.0 | 4.0 |
| Dark Factory Score | 3.6 | 3.9 |
Why CrewAI scores 3.6
CrewAI excels as a multi-agent orchestration platform, scoring highest in autonomy (4.2/5) due to its ability to coordinate multiple AI agents working collaboratively on complex, multi-step tasks. The platform's strength lies in breaking down sophisticated workflows into agent-specific roles that can operate independently while maintaining coordination. However, as primarily an orchestration framework rather than a direct code generation tool, it scores lower on code quality (2.8/5) since it focuses more on agent coordination than producing production-ready code with comprehensive testing and typing. Integration capabilities (3.5/5) are solid through its platform approach, though specific CI/CD and deployment tooling appears limited from the available information. Self-correction (3.2/5) benefits from the multi-agent approach where agents can review and iterate on each other's work, though this depends heavily on implementation. The tool shows exceptional strength in project complexity, particularly excelling at system-level (5.0/5) and application-level (4.5/5) projects where multiple coordinated agents can manage different aspects of large-scale development, while being less suited for simple scripting tasks (2.5/5) where the multi-agent overhead would be unnecessary.
Scored by Claude AI — last evaluated February 27, 2026
Why LangGraph scores 3.9
LangGraph excels at orchestrating complex, stateful multi-agent workflows through its directed graph architecture, earning high autonomy scores for its ability to manage sophisticated task execution patterns with multiple LLM actors. The framework's agent orchestration capabilities and built-in state management enable robust self-correction mechanisms, allowing workflows to handle failures and iterate on solutions effectively. Its integration with the broader LangChain ecosystem provides solid CI/CD and deployment support, though it's primarily focused on orchestration rather than direct system integration. The tool shows strong performance across different project scales, particularly excelling at distributed systems and multi-service applications where its graph-based orchestration shines. While it may be overkill for simple scripts, LangGraph becomes increasingly valuable as project complexity grows, with its stateful architecture and multi-actor coordination being essential for enterprise-scale AI applications. Code quality is solid but varies depending on the underlying LLM implementations, with the framework providing good structure but relying on external models for actual code generation.
Scored by Claude AI — last evaluated February 27, 2026
More comparisons
Generated from Dark Factory Score data. How the score works. Comparing three or more tools at once is part of Pro.