LangGraph is a low-level Python framework designed for orchestrating complex, stateful LLM agents. It provides essential infrastructure for durable execution, streaming, persistence, and human-in-the-loop capabilities. Engineers gain fine-grained control over agent logic and state management.

SmolAgents is a lightweight Python library designed for building and running AI agents with minimal code. It offers first-class support for 'Code Agents' that execute Python, along with standard tool-calling agents. The framework is highly model, modality, and tool-agnostic, providing great flexibility for integration.

• +Provides durable execution, allowing agents to persist through failures and resume from where they left off.
• +Supports human-in-the-loop workflows, enabling inspection and modification of agent state at any point.
• +Offers comprehensive memory, supporting both short-term working memory and long-term memory across sessions.
• +Integrates with LangSmith for deep visibility, tracing execution paths, and debugging complex agent behavior.
• +Designed for production-ready deployment, handling challenges of stateful, long-running workflows.
• +Offers a low-level framework for maximum control over agent orchestration.

• +Extremely simple, with agent logic abstracted minimally, making it easy to learn and use.
• +First-class support for Code Agents executing Python actions in sandboxed environments for secure, composable logic.
• +Supports common JSON/text-based Tool-Calling Agents for alternative paradigms.
• +Model-agnostic, integrating easily with Hugging Face Inference, OpenAI, Anthropic, LiteLLM, Transformers, and Ollama.
• +Modality-agnostic, capable of handling vision, video, and audio inputs.
• +Tool-agnostic, allowing integration of tools from MCP servers, LangChain, or Hugging Face Spaces.
• +Includes CLI tools for quickly running agents without boilerplate code.

• −Requires familiarity with core agent components like models and tools before effective use.
• −Does not abstract prompts or architectural details, demanding manual management.
• −It is very low-level, which can increase complexity for beginners or those seeking higher abstractions.

• −Lacks explicit built-in state management for persistent agent memory or conversational context.
• −Relies on external services (Modal, Blaxel, E2B, Docker) for secure code execution sandboxing.

LangGraph manages state by providing persistence, comprehensive memory, and durable execution for long-running agent workflows.

The framework does not provide an explicit built-in state management system; state must be managed via agent code or external mechanisms.