Langchain json agent. create_json_agent ¶ langchain.

Langchain json agent. JsonToolkit [source] ¶ Bases: BaseToolkit Toolkit for interacting with a JSON spec. JSONAgentOutputParser [source] # Bases: AgentOutputParser Parses tool invocations and final answers in JSON format. chat. 📄️ AWS Step Functions Toolkit AWS Step Functions are a visual Introduction LangChain is a framework for developing applications powered by large language models (LLMs). with_structured_output() is implemented for models that provide native APIs for structuring outputs, like tool/function calling or JSON mode, and makes use of these capabilities under the hood. In the below example, we are using the OpenAPI spec for the OpenAI This walkthrough showcases using an agent to implement the ReAct logic. create_json_agent ¶ langchain_community. This article focuses on using the create_json_chat_agent function, which allows agents to interact using JSON-based responses, a A CLI tool to quickly set up a LangGraph agent chat application. If the output signals that an action should be taken, should be in the below format. Productionization JsonToolkit # class langchain_community. Parameters: Here we focus on how to move from legacy LangChain agents to more flexible LangGraph agents. \nYou have access to the following tools which help you learn more about the JSON JSONAgentOutputParser # class langchain. \nYou have access to the following OpenAI has a tool calling (we use "tool calling" and "function calling" interchangeably here) API that lets you describe tools and their arguments, and have the model return a JSON object with a tool to invoke and the inputs to that tool. It creates a prompt for the agent using the JSON tools and the provided prefix and suffix. List [~langchain_core. LangChain offers an experimental wrapper around open source models run locally via Ollama that gives it the same API as OpenAI Functions. langchain-community: Community-driven components for LangChain. The agent is able to iteratively explore the blob to find what it needs to answer the user’s question. 📄️ JSON Agent Toolkit This example shows how to load and use an agent with a JSON toolkit. Remember to respond with a markdown code snippet of a json blob with a single action, and NOTHING else - even if you just want to respond to the user. JsonToolkit [source] # Bases: BaseToolkit Toolkit for interacting with a JSON spec. prompts. The method In Agents, a language model is used as a reasoning engine to determine which actions to take and in which order. JSON Lines is a file format where each line is a valid JSON value. Do NOT respond with anything except a JSON snippet no matter what!") → Runnable [source] ¶ Create an agent that uses JSON to format its logic, build for Chat Models. LangChain Agents are fine for getting started, but past a certain point you will likely want flexibility and control that they do not offer. This will clone a frontend chat application (Next. Here's an example code snippet demonstrating how to set up and use this function: class langchain. As with any programming paradigm, one of the essentials This walkthrough demonstrates how to use an agent optimized for conversation. In the below example, we are using the OpenAPI spec for the OpenAI Jun 17, 2025 · LangChain supports the creation of agents, or systems that use LLMs as reasoning engines to determine which actions to take and the inputs necessary to perform the action. 📄️ AWS Step Remember to respond with a markdown code snippet of a json blob with a single action, and NOTHING else - even if you just want to respond to the user. The agent is able to iteratively explore the blob to find what it needs to answer the user's question. This agent uses JSON to format its outputs, and is aimed at supporting Chat Models. agents import AgentAction, AgentFinish from langchain_core. This notebook showcases an agent designed to interact with large JSON/dict objects. To get structured output from a ReAct Agent in LangChain without encountering JSON parsing errors, you can use the ReActOutputParser class. `` ` { Feb 28, 2024 · JSON-based Prompt for an LLM Agent In my implementation, I took heavy inspiration from the existing hwchase17/react-json prompt available in LangChain hub. This function creates an agent that uses JSON to format its logic, built specifically for Chat Models. language_models import BaseLanguageModel from langchain_community. create_json_agent( llm: BaseLanguageModel, toolkit: JsonToolkit, callback_manager: BaseCallbackManager | None = None, prefix: str = 'You are an agent designed to interact with JSON. tools. code-block:: python from langchain_core. Conclusion In conclusion, JSON-based agents with Ollama and LangChain represent a powerful fusion of natural language understanding and graph database querying. Parameters: spec – The JSON spec. This will result in an AgentAction being returned. Use with caution, especially when granting access to users. JSON (JavaScript Object Notation) is an open standard file format and data interchange format that uses human-readable text to store and transmit data objects consisting of attribute–value pairs Sep 29, 2024 · LangChain offers a powerful way to create agents that use tools. Feb 2, 2024 · You can achieve this by using the create_json_chat_agent function in LangChain. react_json_single_input. The examples below use Mistral. \nYour goal is to return a final answer by interacting with the JSON. base. JSONAgentOutputParser [source] ¶ Bases: AgentOutputParser Parses tool invocations and final answers in JSON format. 1. Parameters spec – The JSON spec. , some pre-built chains). This notebook provides a quick overview for getting started with JSON document loader. getLogger(__name__) May 23, 2024 · Checked other resources I added a very descriptive title to this question. We can use an output parser to help users to specify an arbitrary JSON schema via the prompt, query a model for outputs that conform to that schema, and finally parse that schema as JSON. Some language models are particularly good at writing JSON. chat_models import ChatOpenAI from langchain_core. Consider adding limitations to what actions can be performed via the agent, what APIs it can Deprecated Create a specific agent with a custom tool instead. Callable [ [~typing. run(user_message). This class is designed to handle ReAct-style LLM calls and ensures that the output is parsed correctly This is the easiest and most reliable way to get structured outputs. """raiseNotImplementedErrordefdict(self,**kwargs:Any)->Dict:"""Return dictionary import base64 import json from langchain_community. param callback_manager: Optional[BaseCallbackManager] = None ¶ [DEPRECATED] Use callbacks instead. Dec 9, 2024 · class langchain. param agent: Union[BaseSingleActionAgent, BaseMultiActionAgent, Runnable] [Required] ¶ The agent to run for creating a plan and determining actions to take at each step of the execution loop. langchain_community. Feb 5, 2025 · Learn how to create JSON-based chat agents in LangChain that can interact with tools and handle complex conversations. Agent # class langchain. I was recently provided a challenge: Develop a chatbot that can answer questions about a JSON dataset using an LLM and pre-defined student data in JSON format. Dec 9, 2024 · The tools the human can use are:\n\n { {tools}}\n\n {format_instructions}\n\nUSER'S INPUT\n--------------------\nHere is the user's input (remember to respond with a markdown code snippet of a json blob with a single action, and NOTHING else):\n\n { { { {input}}}}", input_variables: Optional[List[str]] = None, output_parser: Optional This section will cover building with LangChain Agents. I used the GitHub search to find a similar question and Dec 9, 2024 · langchain_community. json. structured_chat. This notebook showcases an agent interacting with large JSON/dict objects. You can use this code to get started with a LangGraph application, or to test out the pre-built agents! Usage: create-agent-chat-app Structured chat The structured chat agent is capable of using multi-input tools. Dec 13, 2023 · I would like to have a few shot learning (few example) on top of my json_agent meaning my json agent already has seen some examples this is the way I hve done it so far from langchain. Sequence [~langchain_core. JSON (JavaScript Object Notation) is an open standard file format and data interchange format that uses human-readable text to store and transmit data objects consisting of attribute–value pairs and arrays (or other serializable values). Be aware that this agent could theoretically send requests with provided credentials or other sensitive data to unverified or potentially malicious URLs --although it should never in theory. output_parser. JSON mode: Returning responses in JSON format. The… create_structured_chat_agent # langchain. I searched the LangChain documentation with the integrated search. How to: pass in callbacks at runtime How to: attach callbacks to a module How to: pass callbacks into a module constructor How to: create custom callback handlers How to: use callbacks in Contains previous agent actions and tool outputs as messages. \nYou have access to the Dec 9, 2024 · class langchain. After initializing the the LLM and the agent (the csv agent is initialized with a csv file containing data from an online retailer), I run the agent with agent. prompts import ChatPromptTemplate, MessagesPlaceholder, SystemMessagePromptTemplate from langchain_core. ReActJsonSingleInputOutputParser [source] ¶ Bases: AgentOutputParser Parses ReAct-style LLM calls that have a single tool input in json format. Apr 11, 2024 · Quickstart To best understand the agent framework, let's build an agent that has two tools: one to look things up online, and one to look up specific data that we've loaded into a index. Includes base interfaces and in-memory implementations. Use LangGraph to build stateful agents with first-class streaming and human-in-the-loop support. Deprecated Create a specific agent with a custom tool instead. create_json_agent(llm: BaseLanguageModel, toolkit: JsonToolkit, callback_manager: Optional[BaseCallbackManager] = None, prefix: str = 'You are an agent designed to interact with JSON. May 30, 2023 · This article quickly goes over the basics of agents in LangChain and goes on to a couple of examples of how you could make a LangChain agent use other agents. Jul 1, 2024 · Developing a chatbot to answer questions about a JSON dataset. prompts import ChatPromptTemplate, MessagesPlaceholder system = '''Assistant is a large language model trained by OpenAI. This example shows how to load and use an agent with a JSON toolkit. py", line 636, in plan return self. For working with more advanced agents, we’d recommend checking out LangGraph. prompts impor While some model providers support built-in ways to return structured output, not all do. BaseTool], prompt: ~langchain_core. Create a new model by parsing and validating input data from keyword arguments. JsonToolkit ¶ class langchain_community. The current implementation of the create_pandas_dataframe_agent function in the LangChain codebase constructs a . note Agent # class langchain. toolkit import JsonToolkit if TYPE Contains previous agent actions and tool outputs as messages. How to: use legacy LangChain Agents (AgentExecutor) How to: migrate from legacy LangChain agents to LangGraph Callbacks Callbacks allow you to hook into the various stages of your LLM application's execution. langchain. 📄️ OpenAPI Agent Toolkit This example shows how to load and use an agent with a OpenAPI toolkit. To improve your LLM application development, pair LangChain with: LangSmith - Helpful for agent evals and observability. This is useful when you want to answer questions about a JSON blob that's too large to fit in the context window of an LLM. Parameters: Introduction LangChain is a framework for developing applications powered by large language models (LLMs). Some language models are particularly good at writing JSON. This will assume knowledge of LLMs and retrieval so if you haven't already explored those sections, it is recommended you do so. plan( ^^^^^^^^^^^^^^^^ File "C:\Users\vicen\PycharmProjects\ChatBot\venv\Lib\site-packages\langchain\agents\agent. Do NOT respond with anything except a JSON snippet no matter what!") → Runnable [source] # Create an agent that uses JSON to format its logic, build for Chat Models. LangChain agents (the AgentExecutor in particular) have multiple configuration parameters. language_models. Agent [source] # Bases: BaseSingleActionAgent Deprecated since version 0. Agents select and use Tools and Toolkits for actions. Note that more powerful and capable models will perform better with complex schema and/or multiple functions. Creates a JSON agent using a language model, a JSON toolkit, and optional prompt arguments. langchain-core: Core langchain package. Its architecture allows developers to integrate LLMs with external data, prompt engineering, retrieval-augmented generation (RAG), semantic search, and agent workflows. Parameters: create_json_agent # langchain_community. toolkit. Productionization This tutorial shows how to implement an agent with long-term memory capabilities using LangGraph. AgentExecutor [source] ¶ Bases: Chain Agent that is using tools. Luckily, LangChain has a built-in output parser of the JSON agent, so we don’t have to worry about implementing it. Dec 9, 2024 · Remember to respond with a markdown code snippet of a json blob with a single action, and NOTHING else - even if you just want to respond to the user. For detailed documentation of all JSONLoader features and configurations head to the API reference. ReActJsonSingleInputOutputParser [source] # Bases: AgentOutputParser Parses ReAct-style LLM calls that have a single tool input in json format. This is useful when you want to answer questions about a JSON blob that’s too large to fit in the context window of an LLM. prompt import JSON_PREFIX, JSON_SUFFIX from langchain_community. Dec 22, 2023 · I am using the CSV agent which is essentially a wrapper for the Pandas Dataframe agent, both of which are included in langchain-experimental. Setup: LangSmith By definition, agents take a self-determined, input-dependent Some language models (like Anthropic's Claude) are particularly good at reasoning/writing XML. agents import AgentExecutor, create_json_chat_agent from from __future__ import annotations import logging from typing import Union from langchain_core. Feb 28, 2024 · Learn to implement a Mixtral agent with Ollama and Langchain that interacts with a Neo4j graph database through a semantic layer. Dec 9, 2024 · @propertydef_agent_type(self)->str:"""Return Identifier of an agent type. \nYou have access to the following tools which help you learn more about the JSON This example shows how to load and use an agent with a JSON toolkit. parse(full_output Aug 13, 2024 · Hello, @invalidexplorer! I'm here to assist you with any bugs, questions, or contributions you might have. 0: Use new agent constructor methods like create_react_agent, create_json_agent, create_structured_chat_agent, etc. create_json_agent ¶ langchain. Parameters Feb 20, 2024 · In this example, we asked the agent to recommend a good comedy. BaseLanguageModel, tools: ~typing. agent. The agent can store, retrieve, and use memories to enhance its interactions with users. This agent can make requests to external APIs. Also see Tools page. g. Here's an example: . `` ` { create_json_agent # langchain_community. How to parse JSON output While some model providers support built-in ways to return structured output, not all do. Jul 3, 2023 · class langchain. `` ` { Remember to respond with a markdown code snippet of a json blob with a single action, and NOTHING else - even if you just want to respond to the user. JSONAgentOutputParser # class langchain. JSON Agent # This notebook showcases an agent designed to interact with large JSON/dict objects. json import parse_json_markdown from langchain. callbacks import BaseCallbackManager from langchain_core. Since one of the available tools of the agent is a recommender tool, it decided to utilize the recommender tool by providing the JSON syntax to define its input. param 本笔记本展示了一个与大型 JSON/dict 对象进行交互的代理。当您想要回答关于一个超出 LLM 上下文窗口大小的 JSON 数据块的问题时，这将非常有用。该代理能够迭代地探索数据块，找到回答用户问题所需的信息。 Sep 21, 2024 · How to Load JSON Files in LangChain LangChain is an innovative framework designed for developing applications powered by language models. langgraph: Powerful orchestration layer for LangChain. Expects output to be in one of two formats. Jul 23, 2025 · LangChain is a modular framework designed to build applications powered by large language models (LLMs). How to load JSON JSON (JavaScript Object Notation) is an open standard file format and data interchange format that uses human-readable text to store and transmit data objects consisting of attribute–value pairs and arrays (or other serializable values). ChatPromptTemplate, tools_renderer: ~typing. js or Vite), along with up to 4 pre-built agents. create_json_agent(llm: BaseLanguageModel, toolkit: JsonToolkit, callback_manager: BaseCallbackManager | None = None, prefix: str = 'You are an agent designed to interact with JSON. """Json agent. Please note that this is a simplified example and the actual implementation may vary depending on the specifics of your use case and the existing codebase. BaseTool]], str] = <function render_text Nov 26, 2023 · Thought:Traceback (most recent call last): File "C:\Users\vicen\PycharmProjects\ChatBot\venv\Lib\site-packages\langchain\agents\agent. LangChain implements a JSONLoader to convert JSON and JSONL data into Feb 29, 2024 · VIII. agent_toolkits. create_structured_chat_agent(llm: ~langchain_core. The prompt uses the following system Mar 1, 2023 · Using the OpenAPIToolkit, the agent is able to sift through the JSON representation of the spec (see JSON agent), find the required base URL, path, required parameters for a POST request to the /completions endpoint, then make the request. This method takes a schema as input which specifies the names, types, and descriptions of the desired output attributes. py", line 1032, in _take_next_step output = self. output_parsers. agent import AgentOutputParser logger = logging. Agent that calls the language model and deciding the action. agents. Let's work together to resolve your issue. exceptions import OutputParserException from langchain_core. LangChain simplifies every stage of the LLM application lifecycle: Development: Build your applications using LangChain's open-source components and third-party integrations. langchain: A package for higher level components (e. Other agents are often optimized for using tools to figure out the best response, which is not ideal in a conversational setting where you may want the agent to be able to chat with the user as well. . It then creates a ZeroShotAgent with the prompt and the JSON tools, and returns an AgentExecutor for executing the agent with the tools. 本文译自JSON agents with Ollama & LangChain一文，以电影推荐助手为实践案例，讲解了博主在工程实践中，如何基于LangChain框架和本地LLM优雅实现了Json结构化的智能体。 In Agents, a language model is used as a reasoning engine to determine which actions to take and in which order. In this notebook we will show how those parameters map to the LangGraph react agent executor using the create_react_agent prebuilt helper method. Apr 7, 2024 · Deploying agents with Langchain is a straightforward process, though it is primarily optimized for integration with OpenAI’s API. JSON Toolkit This notebook showcases an agent interacting with large JSON/dict objects. Below is a detailed walkthrough of LangChain’s main modules, their roles, and code examples, following the latest How to load JSON data JSON (JavaScript Object Notation) is an open standard file format and data interchange format that uses human-readable text to store and transmit data objects consisting of attribute–value pairs and arrays (or other serializable values). LangChain’s ecosystem While the LangChain framework can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools when building LLM applications. Raises ValidationError if the input data cannot be parsed to form a valid model. Agents and toolkits 📄️ Connery Toolkit Using this toolkit, you can integrate Connery Actions into your LangChain agent. """ from __future__ import annotations from typing import TYPE_CHECKING, Any, Dict, List, Optional from langchain_core. This goes over how to use an agent that uses XML when prompting. pydantic_v1 import Field from langserve import CustomUserType from langchain. This is driven by a LLMChain. tool-calling is extremely useful for building tool-using chains and agents, and for getting structured Oct 31, 2023 · This modification will convert the dataframe to a JSON string and then parse it into a Python dictionary before returning. Dec 5, 2024 · To generate JSON output using an agent in LangChain, you can use the create_json_chat_agent function. srcey zgiq obctuvv muqj dssnketj zocddw gbcxh izggibbi qapfccbgl oaiyw