| # Agents | |
| By themselves, language models can't take actions - they just output text. Agents are systems that take a high-level task and use an LLM as a reasoning engine to decide what actions to take and execute those actions. | |
| [LangGraph](/docs/concepts/architecture#langgraph) is an extension of LangChain specifically aimed at creating highly controllable and customizable agents. We recommend that you use LangGraph for building agents. | |
| Please see the following resources for more information: | |
| * LangGraph docs on [common agent architectures](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/) | |
| * [Pre-built agents in LangGraph](https://langchain-ai.github.io/langgraph/reference/prebuilt/#langgraph.prebuilt.chat_agent_executor.create_react_agent) | |
| ## Legacy agent concept: AgentExecutor | |
| LangChain previously introduced the `AgentExecutor` as a runtime for agents. | |
| While it served as an excellent starting point, its limitations became apparent when dealing with more sophisticated and customized agents. | |
| As a result, we're gradually phasing out `AgentExecutor` in favor of more flexible solutions in LangGraph. | |
| ### Transitioning from AgentExecutor to langgraph | |
| If you're currently using `AgentExecutor`, don't worry! We've prepared resources to help you: | |
| 1. For those who still need to use `AgentExecutor`, we offer a comprehensive guide on [how to use AgentExecutor](/docs/how_to/agent_executor). | |
| 2. However, we strongly recommend transitioning to LangGraph for improved flexibility and control. To facilitate this transition, we've created a detailed [migration guide](/docs/how_to/migrate_agent) to help you move from `AgentExecutor` to LangGraph seamlessly. | |