docs.laragent.ai

# Official LarAgent Documentation

## Docs

- [Introduction](https://docs.laragent.ai/introduction.md)
- [Quickstart](https://docs.laragent.ai/quickstart.md): Get started with LarAgent in minutes
- [What are agents?](https://docs.laragent.ai/v1/agents/overview.md): Agents are the core building blocks of LarAgent, representing AI-powered assistants that can interact with users, execute tools, and maintain conversation context.
- [Create & Configure](https://docs.laragent.ai/v1/agents/creation.md): Learn how to create agent classes and configure their behavior, model settings, and provider connections.
- [Supported Providers](https://docs.laragent.ai/v1/agents/llm-drivers.md): Connect to different AI providers like OpenAI, Anthropic, Gemini, and more while maintaining a consistent API across your application.
- [Overview](https://docs.laragent.ai/v1/responses/overview.md): Learn how to interact with agents, handle responses, and use chainable methods to customize behavior at runtime.
- [Structured Output](https://docs.laragent.ai/v1/responses/structured-output.md): Define response schemas to receive type-safe, predictable data from your AI agents using DataModels or array schemas.
- [Streaming](https://docs.laragent.ai/v1/responses/streaming.md): Receive AI responses in real-time chunks rather than waiting for the complete response, improving user experience for long interactions.
- [Overview](https://docs.laragent.ai/v1/tools/overview.md): Tools extend your agent's capabilities, allowing them to perform actions like calling APIs, querying databases, or executing any custom logic.
- [Attribute Tools](https://docs.laragent.ai/v1/tools/attribute-tools.md): Create tools by adding the #[Tool] attribute to methods in your agent class — the simplest and most flexible way to define agent capabilities.
- [Model Context Protocol (MCP)](https://docs.laragent.ai/v1/tools/mcp.md): Integrate external MCP servers to dynamically extend your agent with tools and resources
- [Classes & Inline Tools](https://docs.laragent.ai/v1/tools/other-tools.md): Create reusable tool classes for complex functionality or build tools dynamically at runtime using the fluent API.
- [Configuration & Runtime](https://docs.laragent.ai/v1/tools/configuration.md): Configure tool behavior including tool choice, parallel execution, and runtime tool management.
- [Phantom Tools](https://docs.laragent.ai/v1/tools/phantom-tools.md): Phantom Tools return control to your application instead of executing automatically, enabling external handling, user confirmation, and API integration.
- [RAG](https://docs.laragent.ai/v1/agents/rag.md): Guide about implementation of Retrival Augmented Generation in LarAgent.
- [Agent Hooks](https://docs.laragent.ai/v1/agents/hooks.md): Learn how to use lifecycle events and engine hooks to customize agent behavior
- [Expose Agents via API](https://docs.laragent.ai/v1/agents/agent-via-api.md): This document describes the feature introduced in the v0.5 and explains how to expose your agents through an OpenAI-compatible endpoint.
- [Overview](https://docs.laragent.ai/v1/context/overview.md): Configure default storage drivers for agent context persistence
- [Identity](https://docs.laragent.ai/v1/context/identity.md): Understand the Context and Identity system for managing storage isolation, session tracking, and data scoping in LarAgent
- [Storage Drivers](https://docs.laragent.ai/v1/context/storage-drivers.md): Configure persistence backends for chat history, usage tracking, and custom storages
- [Chat History](https://docs.laragent.ai/v1/context/history.md): Configure and manage conversation history storage with flexible drivers and truncation strategies
- [Usage Tracking](https://docs.laragent.ai/v1/context/usage-tracking.md): Monitor and analyze token consumption metrics from AI model responses in LarAgent
- [Data Model](https://docs.laragent.ai/v1/context/data-model.md): Create structured data objects with automatic validation, serialization, and OpenAPI schema generation
- [Context Facade](https://docs.laragent.ai/v1/context/facade.md): Manage agent contexts, chat histories, and storage operations with an Eloquent-like fluent API
- [Event Setup Guide](https://docs.laragent.ai/v1/customization/events/setup.md): Learn how to listen to and handle LarAgent events in your Laravel application
- [Agent Events](https://docs.laragent.ai/v1/customization/events/agent.md): Events dispatched during agent lifecycle and conversation flow
- [Context Events 🚧](https://docs.laragent.ai/v1/customization/events/context.md): Events dispatched during context operations and state management
- [Identity Events 🚧](https://docs.laragent.ai/v1/customization/events/identity.md): Events dispatched during identity resolution and management
- [Chat History Events 🚧](https://docs.laragent.ai/v1/customization/events/history.md): Listen to chat history lifecycle events for logging, validation, and custom behavior
- [Custom Storage 🚧](https://docs.laragent.ai/v1/customization/context/storage.md): Create custom storages with DataModels for type-safe persistence
- [Custom Storage Driver 🚧](https://docs.laragent.ai/v1/customization/context/storage-driver.md): Build custom storage drivers for specialized persistence backends
- [Guides Introduction](https://docs.laragent.ai/guides/introduction.md): Comprehensive guides to help you build powerful AI agents with LarAgent. From basic implementations to advanced patterns and real-world use cases.
- [Upgrade to v1.0](https://docs.laragent.ai/guides/upgrade-to-v1.md): Complete migration guide from LarAgent v0.8 to v1.0 with step-by-step instructions
- [Development](https://docs.laragent.ai/guides/development.md): Contribute to LarAgent development
- [Vector-Based RAG](https://docs.laragent.ai/guides/rag/vector-based.md): Learn how to implement traditional RAG using vector embeddings to build a knowledge-enhanced customer support agent.
- [Retrieval-as-Tool RAG](https://docs.laragent.ai/guides/rag/retrieval-as-tool.md): Learn how to implement advanced RAG by giving your agent tools to retrieve information on-demand from both structured databases and document collections.

## OpenAPI Specs

- [openapi](https://docs.laragent.ai/api-reference/openapi.json)

## Optional

- [Repository](https://github.com/maestroerror/laragent)
- [Community](https://discord.gg/NAczq2T9F8)
- [Blog](https://blog.laragent.ai)