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The core idea of agents is to use a language model to choose a sequence of actions to take. In chains, a sequence of actions is hardcoded (in code). In agents, a language model is used as a reasoning engine to determine which actions to take and in which order.
Agents are systems that use LLMs as reasoning engines to determine which actions to take and the inputs to pass them. After executing actions, the results can be fed back into the LLM to determine whether more actions are needed, or whether it is okay to finish. In this tutorial we will build an agent that can interact with a search engine.
Design agents with control. Add human oversight and create stateful, scalable workflows with AI agents.
LangChain comes with a number of built-in agents that are optimized for different use cases. Read about all the agent types here. We'll use the tool calling agent, which is generally the most reliable kind and the recommended one for most use cases.
Jun 2, 2024 · LangChain offers a robust framework for working with agents, including: - A standard interface for agents. - A variety of pre-built agents to choose from. - Examples of end-to-end implementations...
Agents are systems that use an LLM as a reasoning enginer to determine which actions to take and what the inputs to those actions should be. The results of those actions can then be fed back into the agent and it determine whether more actions are needed, or whether it is okay to finish.
LangChain comes with a number of built-in agents that are optimized for different use cases. Read about all the available agent types here. For this example, let’s try out the OpenAI tools agent, which makes use of the new OpenAI tool-calling API (this is only available in the latest OpenAI models, and differs from function-calling in that ...
3 days ago · In this tutorial, we will use the LangChain Python package to build an AI agent that uses its custom tools to return a URL directing to NASA's Astronomy Picture of the Day. An artificial intelligence (AI) agent is a system that performs tasks on behalf of a user or another system by designing its own workflow and utilizing available tools.
LangChain offers a number of tools and functions that allow you to create SQL Agents which can provide a more flexible way of interacting with SQL databases. The main advantages of using SQL Agents are: It can answer questions based on the databases schema as well as on the databases content (like describing a specific table).
Jul 12, 2024 · LangChain’s Agent Framework allows developers to create intelligent systems that can reason, make decisions, and take autonomous actions based on a language model (LLM). The key components of an Agent include a Language Model (the cognitive center), Tools (for interacting with external systems), and an Agent Executor (the runtime environment).
