Comparison Preset
LangGraph is the only viable choice for an enterprise setting. Its design for stateful, durable agents with features for persistence and human-in-the-loop oversight directly meets enterprise requirements for reliability and governance. The framework carries a lower risk profile with an MIT license, a high bus factor of 8/10, and only one moderate known vulnerability. In contrast, SmolAgents' critical vulnerability is a non-starter for any security-conscious organization. LangGraph's active maintenance and deep integration with LangSmith for tracing and evaluation provide the long-term support and observability essential for production systems.
Overview
The bottom line — what this framework is, who it's for, and when to walk away.
Bottom Line Up Front
LangGraph is a low-level orchestration framework for building stateful, long-running agents, offering fine-grained control over mixed deterministic and LLM-driven steps. It emphasizes durable execution, persistence, and human-in-the-loop capabilities, integrating with LangChain components for models and tools.
smolagents is an open-source Python library focused on simplifying AI agent development with minimal code. It supports both code-executing and tool-calling agents, offering model, modality, and tool agnosticism. The framework integrates seamlessly with the Hugging Face Hub for sharing and loading agents.
Best For
Building bespoke, stateful, long-running agents needing fine-grained control over mixed deterministic and LLM steps.
Rapidly developing and deploying simple, composable AI agents with code execution and diverse tools.
Avoid If
Seeking high-level abstractions or prebuilt agent architectures for common LLM and tool-calling loops.
Requiring deeply custom agent internal architectures or complex, high-performance stateful orchestration.
Strengths
- +Offers fine-grained control to mix deterministic, hand-coded steps with LLM-driven agentic steps in a single graph.
- +Provides durable execution, allowing agents to persist through failures and resume operations.
- +Supports human-in-the-loop interaction, enabling inspection and modification of agent state at any point.
- +Includes comprehensive memory for both short-term working memory and long-term memory across sessions.
- +Integrates with LangSmith for deep visibility, tracing, debugging, and production-ready deployment of agents.
- +Can be used standalone without requiring the broader LangChain framework.
- +Extremely simple to use with minimal code and abstractions.
- +Native support for Code Agents executing Python code for complex, composable actions.
- +Supports secure code execution in sandboxed environments (Modal, Blaxel, E2B, Docker).
- +Includes support for traditional JSON/text-based tool-calling agents.
- +Seamlessly integrates with Hugging Face Hub for sharing and loading agents/tools.
- +Highly model-agnostic, supporting diverse LLMs via Hub, APIs, or local execution.
- +Supports multimodal inputs, including vision, video, and audio.
- +Highly tool-agnostic, integrating tools from MCP servers, LangChain, or Hub Spaces.
- +Provides CLI tools for quickly running agents without boilerplate.
Weaknesses
- −It is a very low-level framework, requiring familiarity with agent components like models and tools.
- −It does not abstract prompts or agent architecture, focusing solely on orchestration.
- −Beginners or those seeking higher-level abstractions may find it too complex and are recommended to use LangChain's prebuilt agents.
- −Requires explicit integration with external components for LLM models and tools.
- −Minimal abstractions might necessitate more manual implementation for highly custom or complex agent behaviors.
Project Health
Is this project alive, well-maintained, and safe to bet on long-term?
Bus Factor Score
Maintainers
Open Issues
Fit
Does it support the workflows, patterns, and capabilities your team actually needs?
State Management
LangGraph manages state for long-running agents by providing durable execution, persistence, and comprehensive memory for both short-term reasoning and long-term sessions.
no data
Cost & Licensing
What does it actually cost? License type, pricing model, and hidden fees.
License
Perspective
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