Dify vs LangGraph
Spec-Driven
An agentic workflow builder platform for developing, deploying, and managing autonomous agents, RAG pipelines, and AI applications at scale.
Agent-Assisted
A framework for building stateful, multi-actor applications with LLMs using directed graphs and agent orchestration.
Summary
- Dify scores 4.0/5 and LangGraph scores 3.9/5 on the Dark Factory Score, a difference of 0.1.
- The widest gap is Project Complexity: Dify 4.3, LangGraph 4.0.
- Dify scores higher on integration and project complexity.
- LangGraph scores higher on self-correction.
- They score the same on autonomy and code quality.
- Dify is listed as freemium and LangGraph as open source.
- Both are listed under Orchestration and Agent Frameworks.
Dimension by dimension
| Dimension | Weight | Dify | LangGraph |
|---|---|---|---|
| Autonomy | 28% | 4.2 | 4.2 |
| Code Quality | 20% | 3.5 | 3.5 |
| Integration | 17% | 4.0 | 3.8 |
| Self-Correction | 15% | 3.8 | 4.0 |
| Project Complexity | 20% | 4.3 | 4.0 |
| Dark Factory Score | 4.0 | 3.9 |
Why Dify scores 4.0
Dify excels as an agentic workflow builder platform designed for enterprise-scale AI application development. Its visual workflow orchestration capabilities enable high autonomy in multi-step task execution, particularly for RAG pipelines and autonomous agents. The platform's focus on deployment and management at scale demonstrates strong integration capabilities with enterprise systems, though specific CI/CD and Git integration details aren't fully evident from the available information. The self-correction capabilities appear robust given its agentic nature, with built-in error handling and iteration mechanisms typical of workflow orchestration platforms. Dify's strength lies in handling complex, multi-service applications and distributed AI systems rather than simple scripts or single features. The platform is specifically designed for full-stack agent development and deployment, making it highly effective for Tier 4 (full applications) and reasonably capable for Tier 5 (distributed systems). However, it may be overkill for simple scripting tasks. The code quality scoring reflects that while Dify facilitates production-grade deployments, the actual code generation quality depends on the underlying models and templates used within the platform.
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.