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feat: New conceptual docs (langchain-ai#7068)
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| # Agents | ||
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| 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. | ||
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| [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. | ||
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| Please see the following resources for more information: | ||
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| - LangGraph docs on [common agent architectures](https://langchain-ai.github.io/langgraphjs/concepts/agentic_concepts/) | ||
| - [Pre-built agents in LangGraph](https://langchain-ai.github.io/langgraphjs/reference/functions/langgraph_prebuilt.createReactAgent.html) | ||
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| ## Legacy agent concept: AgentExecutor | ||
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| 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. | ||
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| ### Transitioning from AgentExecutor to langgraph | ||
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| If you're currently using `AgentExecutor`, don't worry! We've prepared resources to help you: | ||
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| 1. For those who still need to use `AgentExecutor`, we offer a comprehensive guide on [how to use AgentExecutor](/docs/how_to/agent_executor). | ||
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| 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. |
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| import ThemedImage from "@theme/ThemedImage"; | ||
| import useBaseUrl from "@docusaurus/useBaseUrl"; | ||
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| # Architecture | ||
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| LangChain is a framework that consists of a number of packages. | ||
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| <ThemedImage | ||
| alt="Diagram outlining the hierarchical organization of the LangChain framework, displaying the interconnected parts across multiple layers." | ||
| sources={{ | ||
| light: useBaseUrl("/svg/langchain_stack_062024.svg"), | ||
| dark: useBaseUrl("/svg/langchain_stack_062024_dark.svg"), | ||
| }} | ||
| title="LangChain Framework Overview" | ||
| style={{ width: "100%" }} | ||
| /> | ||
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| ## @langchain/core | ||
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| This package contains base abstractions for different components and ways to compose them together. | ||
| The interfaces for core components like chat models, vector stores, tools and more are defined here. | ||
| No third-party integrations are defined here. | ||
| The dependencies are very lightweight. | ||
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| ## langchain | ||
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| The main `langchain` package contains chains and retrieval strategies that make up an application's cognitive architecture. | ||
| These are NOT third-party integrations. | ||
| All chains, agents, and retrieval strategies here are NOT specific to any one integration, but rather generic across all integrations. | ||
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| ## Integration packages | ||
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| Popular integrations have their own packages (e.g. `@langchain/openai`, `@langchain/anthropic`, etc) so that they can be properly versioned and appropriately lightweight. | ||
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| For more information see: | ||
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| - A list [integrations packages](/docs/integrations/platforms/) | ||
| - The [API Reference](https://api.js.langchain.com/) where you can find detailed information about each of the integration package. | ||
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| ## @langchain/community | ||
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| This package contains third-party integrations that are maintained by the LangChain community. | ||
| Key integration packages are separated out (see above). | ||
| This contains integrations for various components (chat models, vector stores, tools, etc). | ||
| All dependencies in this package are optional to keep the package as lightweight as possible. | ||
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| ## @langchian/langgraph | ||
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| `@langchian/langgraph` is an extension of `langchain` aimed at building robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph. | ||
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| LangGraph exposes high level interfaces for creating common types of agents, as well as a low-level API for composing custom flows. | ||
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| :::info[Further reading] | ||
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| - See our LangGraph overview [here](https://langchain-ai.github.io/langgraphjs/concepts/high_level/#core-principles). | ||
| - See our LangGraph Academy Course [here](https://academy.langchain.com/courses/intro-to-langgraph). | ||
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| ::: | ||
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| ## LangSmith | ||
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| A developer platform that lets you debug, test, evaluate, and monitor LLM applications. | ||
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| For more information, see the [LangSmith documentation](https://docs.smith.langchain.com) |
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| # Callbacks | ||
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| :::note Prerequisites | ||
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| - [Runnable interface](/docs/concepts/runnables) | ||
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| ::: | ||
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| LangChain provides a callback system that allows you to hook into the various stages of your LLM application. This is useful for logging, monitoring, streaming, and other tasks. | ||
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| You can subscribe to these events by using the `callbacks` argument available throughout the API. This argument is list of handler objects, which are expected to implement one or more of the methods described below in more detail. | ||
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| ## Callback events | ||
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| | Event | Event Trigger | Associated Method | | ||
| | ---------------- | ------------------------------------------- | ---------------------- | | ||
| | Chat model start | When a chat model starts | `handleChatModelStart` | | ||
| | LLM start | When a llm starts | `handleLlmStart` | | ||
| | LLM new token | When an llm OR chat model emits a new token | `handleLlmNewToken` | | ||
| | LLM ends | When an llm OR chat model ends | `handleLlmEnd` | | ||
| | LLM errors | When an llm OR chat model errors | `handleLlmError` | | ||
| | Chain start | When a chain starts running | `handleChainStart` | | ||
| | Chain end | When a chain ends | `handleChainEnd` | | ||
| | Chain error | When a chain errors | `handleChainError` | | ||
| | Tool start | When a tool starts running | `handleToolStart` | | ||
| | Tool end | When a tool ends | `handleToolEnd` | | ||
| | Tool error | When a tool errors | `handleToolError` | | ||
| | Retriever start | When a retriever starts | `handleRetrieverStart` | | ||
| | Retriever end | When a retriever ends | `handleRetrieverEnd` | | ||
| | Retriever error | When a retriever errors | `handleRetrieverError` | | ||
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| ## Callback handlers | ||
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| - Callback handlers implement the [BaseCallbackHandler](https://api.js.langchain.com/classes/_langchain_core.callbacks_base.BaseCallbackHandler.html) interface. | ||
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| During run-time LangChain configures an appropriate callback manager (e.g., [CallbackManager](https://api.js.langchain.com/classes/_langchain_core.callbacks_manager.BaseCallbackManager.html)) which will be responsible for calling the appropriate method on each "registered" callback handler when the event is triggered. | ||
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| ## Passing callbacks | ||
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| The `callbacks` property is available on most objects throughout the API (Models, Tools, Agents, etc.) in two different places: | ||
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| - **Request time callbacks**: Passed at the time of the request in addition to the input data. | ||
| Available on all standard `Runnable` objects. These callbacks are INHERITED by all children | ||
| of the object they are defined on. For example, `await chain.invoke({ number: 25 }, { callbacks: [handler] })`. | ||
| - **Constructor callbacks**: `const chain = new TheNameOfSomeChain({ callbacks: [handler] })`. These callbacks | ||
| are passed as arguments to the constructor of the object. The callbacks are scoped | ||
| only to the object they are defined on, and are **not** inherited by any children of the object. | ||
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| :::warning | ||
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| Constructor callbacks are scoped only to the object they are defined on. They are **not** inherited by children | ||
| of the object. | ||
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| ::: | ||
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| If you're creating a custom chain or runnable, you need to remember to propagate request time | ||
| callbacks to any child objects. | ||
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| For specifics on how to use callbacks, see the [relevant how-to guides here](/docs/how_to/#callbacks). |
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| # Chat history | ||
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| :::info Prerequisites | ||
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| - [Messages](/docs/concepts/messages) | ||
| - [Chat models](/docs/concepts/chat_models) | ||
| - [Tool calling](/docs/concepts/tool_calling) | ||
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| ::: | ||
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| Chat history is a record of the conversation between the user and the chat model. It is used to maintain context and state throughout the conversation. The chat history is sequence of [messages](/docs/concepts/messages), each of which is associated with a specific [role](/docs/concepts/messages#role), such as "user", "assistant", "system", or "tool". | ||
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| ## Conversation patterns | ||
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|  | ||
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| Most conversations start with a **system message** that sets the context for the conversation. This is followed by a **user message** containing the user's input, and then an **assistant message** containing the model's response. | ||
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| The **assistant** may respond directly to the user or if configured with tools request that a [tool](/docs/concepts/tool_calling) be invoked to perform a specific task. | ||
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| So a full conversation often involves a combination of two patterns of alternating messages: | ||
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| 1. The **user** and the **assistant** representing a back-and-forth conversation. | ||
| 2. The **assistant** and **tool messages** representing an ["agentic" workflow](/docs/concepts/agents) where the assistant is invoking tools to perform specific tasks. | ||
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| ## Managing chat history | ||
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| Since chat models have a maximum limit on input size, it's important to manage chat history and trim it as needed to avoid exceeding the [context window](/docs/concepts/chat_models#context_window). | ||
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| While processing chat history, it's essential to preserve a correct conversation structure. | ||
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| Key guidelines for managing chat history: | ||
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| - The conversation should follow one of these structures: | ||
| - The first message is either a "user" message or a "system" message, followed by a "user" and then an "assistant" message. | ||
| - The last message should be either a "user" message or a "tool" message containing the result of a tool call. | ||
| - When using [tool calling](/docs/concepts/tool_calling), a "tool" message should only follow an "assistant" message that requested the tool invocation. | ||
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| :::tip | ||
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| Understanding correct conversation structure is essential for being able to properly implement | ||
| [memory](https://langchain-ai.github.io/langgraphjs/concepts/memory/) in chat models. | ||
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| ::: | ||
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| ## Related resources | ||
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| - [How to trim messages](/docs/how_to/trim_messages/) | ||
| - [Memory guide](https://langchain-ai.github.io/langgraphjs/concepts/memory/) for information on implementing short-term and long-term memory in chat models using [LangGraph](https://langchain-ai.github.io/langgraphjs/). |
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