How-To Guides provide practical, task-focused instructions for implementing specific features in your AI applications. Each guide walks you through a complete implementation with code examples and best practices.Documentation Index
Fetch the complete documentation index at: https://docs.freeplay.ai/llms.txt
Use this file to discover all available pages before exploring further.
How These Guides Fit Together
- Core Concepts: Understand what Freeplay features do and why they matter
- How-To Guides (this section): Learn how to implement specific patterns step-by-step
- Developer Resources: Reference documentation for SDKs, APIs, and integrations
- Code Recipes: Complete, runnable code examples you can copy and adapt
Available Guides
Integration Patterns
Common Integration Patterns
Quick reference for multi-turn chat, tool calling, metadata, and feedback
Building Agents
Structure agent workflows with traces and multi-step reasoning
Chat & Conversations
Multi-Turn Chat
Maintain conversation context across multiple exchanges
Tool Calls
Implement function calling with OpenAI, Anthropic, and other providers
Advanced Topics
Multimodal Data
Work with images, audio, and other non-text content
Aligning LLM Judges
Create and calibrate model-graded evaluations
Streaming Responses
Handle streaming LLM responses in your application
Managing LLM Provider Fallbacks
Configure backup providers for resilience
Voice & Specialized
Building Voice Agents with Pipecat
Create voice-enabled AI applications using Pipecat and Twilio