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Eugene Yurtsev 2024-10-22 13:48:00 -04:00
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docs/docs/concepts/agents.mdx
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# Agents # Agents
## What is an agent? We recommend that you use [LangGraph](/docs/concepts/architecture#langgraph) for building agents.
Many LLM applications implement a particular control flow of steps before and / or after LLM calls. Please see the following resources for more information:
As an example, [RAG](/docs/concepts/rag/) performs [retrieval](/docs/concepts/retrieval/) of relevant documents to a question, and passes those documents to an LLM in order to ground the [model](/docs/concepts/chat_models/)'s response.
Instead of hard-coding a fixed control flow, we sometimes want LLM systems that can pick its own control flow to solve more complex problems!
This is one definition of an agent: an agent is a system that uses an LLM to decide the control flow of an application. * LangGraph docs for conceptual architecture about [Agents](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/)
There are many ways that an LLM can control application: * [Pre-built agent in LangGraph](https://langchain-ai.github.io/langgraph/reference/prebuilt/#langgraph.prebuilt.chat_agent_executor.create_react_agent)
- An LLM can route between two potential paths
- An LLM can decide which of many tools to call
- An LLM can decide whether the generated answer is sufficient or more work is needed
![Agent types](/img/agent_types.png)
## LangGraph
As a result, there are many different types of [agent architectures](https://blog.langchain.dev/what-is-a-cognitive-architecture/), which give an LLM varying levels of control.
[LangGraph](https://github.com/langchain-ai/langgraph) is an extension of LangChain specifically aimed at creating highly controllable and customizable agents.
The motivation of LangGraph is to help preserve high reliability as we give the agent more control over the application.
:::info[Further reading]
* See our LangGraph overview [here](https://langchain-ai.github.io/langgraph/concepts/high_level/#core-principles).
* See our LangGraph Academy Course [here](https://academy.langchain.com/courses/intro-to-langgraph).
:::
## Legacy Agent Concept: AgentExecutor ## Legacy Agent Concept: AgentExecutor