From 7aa67885cffc9e9d125e010b55b4b93fac4d7242 Mon Sep 17 00:00:00 2001 From: Eugene Yurtsev Date: Tue, 22 Oct 2024 13:48:00 -0400 Subject: [PATCH] Update --- docs/docs/concepts/agents.mdx | 29 ++++------------------------- 1 file changed, 4 insertions(+), 25 deletions(-) diff --git a/docs/docs/concepts/agents.mdx b/docs/docs/concepts/agents.mdx index 84c3cecff01..98c28534109 100644 --- a/docs/docs/concepts/agents.mdx +++ b/docs/docs/concepts/agents.mdx @@ -1,32 +1,11 @@ # 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. -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! +Please see the following resources for more information: -This is one definition of an agent: an agent is a system that uses an LLM to decide the control flow of an application. -There are many ways that an LLM can control application: - -- 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). - -::: +* LangGraph docs for conceptual architecture about [Agents](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/) +* [Pre-built agent in LangGraph](https://langchain-ai.github.io/langgraph/reference/prebuilt/#langgraph.prebuilt.chat_agent_executor.create_react_agent) ## Legacy Agent Concept: AgentExecutor