# docs-agno-com

> This skill contains 1528 reference documents organized by topic. When you need information about Agno:

- Author: Casey
- Repository: NovaAI-innovation/mail-gent
- Version: 20260207093722
- Stars: 0
- Forks: 0
- Last Updated: 2026-02-07
- Source: https://github.com/NovaAI-innovation/mail-gent
- Web: https://mule.run/skillshub/@@NovaAI-innovation/mail-gent~docs-agno-com:20260207093722

---

---
name: docs-agno-com
description: Documentation and reference materials for Agno. Contains 1528 reference documents organized into sections: Docs. Use this skill when working with Agno documentation or when the user mentions topics covered in these references.
version: 1.0.0
---

# Agno

## How to Use This Skill

This skill contains 1528 reference documents organized by topic. When you need information about Agno:

1. Claude will automatically access relevant reference files based on your question
2. Reference files are organized in the `references/` directory by topic
3. Each reference contains detailed documentation extracted from the official source

## Reference Documentation

### Docs

- [Background Hooks](references/background_hooks.md) - Run agent hooks as non-blocking background tasks in AgentOS
- [A2A Client](references/a2a_client.md) - Connect to any A2A-compatible agent server
- [AgentOS Client](references/agentos_client.md) - Connect to Agno AgentOS instances via REST API
- [Clients](references/clients.md) - Python clients for connecting to agent servers
- [AgentOS Configuration](references/agentos_configuration.md) - Customize your AgentOS instance with custom configuration
- [Connect Your AgentOS](references/connect_your_agentos.md) - Connect your AgentOS to the control plane for monitoring and management.
- [AgentOS Control Plane](references/agentos_control_plane.md) - A web interface for testing, monitoring, and managing your multi-agent system.
- [Overriding Routes](references/overriding_routes.md) - Learn how to override AgentOS routes with your own custom routes when conflicts occur
- [Bring Your Own FastAPI App](references/bring_your_own_fastapi_app.md) - Integrate your own FastAPI app with AgentOS.
- [A2A](references/a2a.md) - Expose Agno agents via the A2A protocol
- [AG-UI](references/ag_ui.md) - Expose Agno agents via the AG-UI protocol
- [Interfaces](references/interfaces.md) - Expose Agno agents through various communication protocols and platforms
- [Slack](references/slack.md) - Host agents as Slack Applications.
- [WhatsApp](references/whatsapp.md) - Host agents as WhatsApp applications
- [What is AgentOS?](references/what_is_agentos.md) - The runtime and control plane for multi-agent systems.
- [Filter Knowledge](references/filter_knowledge.md) - Use filter expressions through the Agno API for precise knowledge base filtering.
- [Manage Knowledge](references/manage_knowledge.md) - Attach Knowledge to your AgentOS instance
- [Custom Lifespan](references/custom_lifespan.md) - Customize the lifespan of your AgentOS app to handle startup and shutdown logic.
- [AgentOS as MCP Server](references/agentos_as_mcp_server.md) - Learn how and why to expose your AgentOS as an MCP server
- [MCPTools within AgentOS](references/mcptools_within_agentos.md) - Learn how to use MCPTools in the Agents, Teams and Workflows within your AgentOS
- [Custom Middleware](references/custom_middleware.md) - Create custom middleware for rate limiting, logging, security, and monitoring in AgentOS
- [JWT Middleware](references/jwt_middleware.md) - Secure your AgentOS application with JWT token validation and RBAC
- [AgentOS Middleware](references/agentos_middleware.md) - Add authentication, logging, monitoring, and security features to your AgentOS application using middleware
- [Overview](references/overview.md) - The production runtime and control plane for your agentic systems.
- [AgentOS Gateway](references/agentos_gateway.md) - Create a unified API gateway for multiple AgentOS instances
- [Remote Execution](references/remote_execution.md) - Execute agents, teams, and workflows hosted on remote AgentOS instances
- [Remote Agent](references/remote_agent.md) - Execute agents hosted on remote AgentOS instances
- [Remote Team](references/remote_team.md) - Execute teams hosted on remote AgentOS instances
- [Remote Workflow](references/remote_workflow.md) - Execute workflows hosted on remote AgentOS instances
- [Run Your AgentOS](references/run_your_agentos.md) - Run a local AgentOS in 20 lines of code.
- [AgentOS Security](references/agentos_security.md) - Secure your AgentOS with authentication and authorization.
- [Role-Based Access Control (RBAC)](references/role_based_access_control_rbac.md) - Secure your AgentOS with fine-grained permissions.
- [Agents](references/agents.md) - Build and configure agents visually in AgentOS Studio.
- [CEL Expressions](references/cel_expressions.md) - Use CEL expressions as evaluators, end conditions, and selectors in workflow steps.
- [Overview](references/overview.md) - A visual editor in AgentOS to build Agents, Teams, and Workflows.
- [Registry](references/registry.md) - Register tools, models, databases, and schemas for use in AgentOS Studio.
- [Teams](references/teams.md) - Compose multi-agent teams visually in AgentOS Studio.
- [Overview](references/overview.md) - Design step-based workflows visually in AgentOS Studio.
- [Tracing](references/tracing.md) - Configure tracing for your agents, teams, and workflows in AgentOS
- [Agent with Knowledge Tracing](references/agent_with_knowledge_tracing.md) - Trace agents with knowledge bases in AgentOS.
- [Agent with Reasoning Tools Tracing](references/agent_with_reasoning_tools_tracing.md) - Trace agents with reasoning tools in AgentOS.
- [Basic Agent Tracing](references/basic_agent_tracing.md) - Trace agents with Agno in AgentOS.
- [Basic Team Tracing](references/basic_team_tracing.md) - Trace teams with Agno in AgentOS.
- [Basic Workflow Tracing](references/basic_workflow_tracing.md) - Trace workflows with Agno in AgentOS.
- [Multi-DB Tracing with setup_tracing()](references/multi_db_tracing_with_setup_tracing.md) - Trace agents with multiple databases using setup_tracing() in AgentOS.
- [Multi-DB Tracing with tracing=True](references/multi_db_tracing_with_tracingtrue.md) - Trace agents with multiple databases using tracing=True in AgentOS.
- [Background Hooks (Per-Hook)](references/background_hooks_per_hook.md) - Run specific hooks as background tasks using the @hook decorator
- [Background Hooks (Global)](references/background_hooks_global.md) - Run all agent hooks as background tasks using AgentOS
- [Background Output Evaluation](references/background_output_evaluation.md) - Use Agent as Judge evaluation to assess responses as a background task
- [Basic Client Usage](references/basic_client_usage.md) - Connect to an AgentOS instance and perform basic operations
- [Knowledge Search](references/knowledge_search.md) - Search and manage knowledge base content
- [Memory Operations](references/memory_operations.md) - Create, update, list, and delete user memories
- [Running Agents](references/running_agents.md) - Execute agent runs with streaming and non-streaming responses
- [Running Teams](references/running_teams.md) - Execute team runs with streaming and non-streaming responses
- [Running Workflows](references/running_workflows.md) - Execute workflow runs with streaming and non-streaming responses
- [Session Management](references/session_management.md) - Create, list, and manage sessions for agents, teams, and workflows
- [Database Migrations](references/database_migrations.md) - Migrate your AgentOS database schema.
- [AgentOS Demo](references/agentos_demo.md) - AgentOS demo with agents and teams
- [AgentOS Configuration](references/agentos_configuration.md) - Passing extra configuration to your AgentOS
- [Human-in-the-Loop Example](references/human_in_the_loop_example.md) - AgentOS with tools requiring user confirmation
- [Agent with Tools](references/agent_with_tools.md) - Investment analyst agent with financial tools and web interface
- [Basic](references/basic.md) - Create a basic AI agent with A2A interface
- [Research Team](references/research_team.md) - Multi-agent research team with specialized roles and web interface
- [Agent with Tools](references/agent_with_tools.md) - Investment analyst agent with financial tools and web interface
- [Basic](references/basic.md) - Create a basic AI agent with ChatGPT-like web interface
- [Research Team](references/research_team.md) - Multi-agent research team with specialized roles and web interface
- [Slack Agent with User Memory](references/slack_agent_with_user_memory.md) - Personalized Slack agent that remembers user information and preferences
- [Basic Slack Agent](references/basic_slack_agent.md) - Create a basic AI agent that integrates with Slack for conversations
- [Slack Reasoning Finance Agent](references/slack_reasoning_finance_agent.md) - Slack agent with advanced reasoning and financial analysis capabilities
- [Slack Research Workflow](references/slack_research_workflow.md) - Integrate a research and writing workflow with Slack for structured AI-powered content creation
- [WhatsApp Agent with Media Support](references/whatsapp_agent_with_media_support.md) - WhatsApp agent that analyzes images, videos, and audio using multimodal AI
- [WhatsApp Agent with User Memory](references/whatsapp_agent_with_user_memory.md) - Personalized WhatsApp agent that remembers user information and preferences
- [Basic WhatsApp Agent](references/basic_whatsapp_agent.md) - Create a basic AI agent that integrates with WhatsApp Business API
- [WhatsApp Image Generation Agent (Model-based)](references/whatsapp_image_generation_agent_model_based.md) - WhatsApp agent that generates images using Gemini's built-in capabilities
- [WhatsApp Image Generation Agent (Tool-based)](references/whatsapp_image_generation_agent_tool_based.md) - WhatsApp agent that generates images using OpenAI's image generation tools
- [WhatsApp Reasoning Finance Agent](references/whatsapp_reasoning_finance_agent.md) - WhatsApp agent with advanced reasoning and financial analysis capabilities
- [Enable AgentOS MCP](references/enable_agentos_mcp.md) - Complete AgentOS setup with MCP support enabled
- [AgentOS with MCPTools](references/agentos_with_mcptools.md) - Complete AgentOS setup with MCPTools enabled on agents
- [Custom FastAPI App with JWT Middleware](references/custom_fastapi_app_with_jwt_middleware.md) - Custom FastAPI application with JWT middleware for authentication and AgentOS integration
- [Custom Middleware](references/custom_middleware.md) - AgentOS with custom middleware for rate limiting, logging, and monitoring
- [JWT Middleware with Cookies](references/jwt_middleware_with_cookies.md) - AgentOS with JWT middleware using HTTP-only cookies for secure web authentication
- [JWT Middleware with Authorization Headers](references/jwt_middleware_with_authorization_headers.md) - Complete AgentOS setup with JWT middleware for authentication and parameter injection using Authorization headers
- [Advanced Scopes](references/advanced_scopes.md)
- [Basic RBAC](references/basic_rbac.md)
- [Custom Scope Mappings](references/custom_scope_mappings.md)
- [Per-Agent Permissions](references/per_agent_permissions.md)
- [AgentOS Gateway](references/agentos_gateway.md) - Create a unified API gateway for multiple AgentOS instances
- [Remote Agent](references/remote_agent.md) - Execute agents hosted on a remote AgentOS instance
- [Remote Team](references/remote_team.md) - Execute teams hosted on a remote AgentOS instance
- [Using the API](references/using_the_api.md) - Call the AgentOS API to run agents, teams, and workflows.
- [Building Agents](references/building_agents.md) - Start simple: a model, tools, and instructions.
- [Debugging Agents](references/debugging_agents.md) - Inspect execution flow, tool calls, and intermediate steps.
- [What are Agents?](references/what_are_agents.md) - AI programs that reason, use tools, and maintain state.
- [Running Agents](references/running_agents.md) - Run agents and process their output.
- [Agent with Knowledge](references/agent_with_knowledge.md) - Give your agent a searchable knowledge base (Agentic RAG).
- [Agent with Memory](references/agent_with_memory.md) - Store user preferences that persist across conversations.
- [Agent with Storage](references/agent_with_storage.md) - Persist conversation history across runs.
- [Agent with Structured Output](references/agent_with_structured_output.md) - Get typed Pydantic responses instead of free-form text.
- [Agent with Tools](references/agent_with_tools.md) - Give your agent tools to interact with external services.
- [Context Compression](references/context_compression.md) - Compress tool call results to save context space while preserving critical information.
- [Token Counting](references/token_counting.md) - Token estimation for context planning and compression.
- [Providing Datetime](references/providing_datetime.md)
- [Dynamic Instructions](references/dynamic_instructions.md)
- [Few-Shot Learning](references/few_shot_learning.md)
- [Managing Tool Calls](references/managing_tool_calls.md)
- [Basic Instructions](references/basic_instructions.md)
- [Instructions via Function](references/instructions_via_function.md)
- [Providing Location](references/providing_location.md)
- [Context Engineering](references/context_engineering.md) - Configure system messages, instructions, and context for agents.
- [Context Engineering](references/context_engineering.md) - Design and control the information sent to language models to guide their behavior.
- [Managing Tool Calls](references/managing_tool_calls.md)
- [Context Engineering](references/context_engineering.md) - Configure system messages, instructions, and context for teams.
- [Agno Assist Agent](references/agno_assist_agent.md)
- [Airbnb Mcp](references/airbnb_mcp.md)
- [Competitor Analysis Agent](references/competitor_analysis_agent.md)
- [Deep Knowledge](references/deep_knowledge.md)
- [Agents](references/agents.md) - Production agents with knowledge bases, research pipelines, multimodal processing, and integrations.
- [Research Agent](references/research_agent.md)
- [Social Media Agent](references/social_media_agent.md)
- [Speech-to-Text Agent](references/speech_to_text_agent.md)
- [Startup Analyst Agent](references/startup_analyst_agent.md) - A sophisticated startup intelligence agent that leverages the `ScrapeGraph` Toolkit for comprehensive due diligence on companies
- [Translation Agent](references/translation_agent.md)
- [Web Extraction Agent](references/web_extraction_agent.md)
- [YouTube Agent](references/youtube_agent.md)
- [Chunking Strategies](references/chunking_strategies.md) - Semantic, fixed-size, recursive, and custom chunking strategies for documents.
- [Embedders](references/embedders.md) - 29 embedding providers including OpenAI, Cohere, HuggingFace, and local options.
- [Knowledge & RAG](references/knowledge__rag.md) - Build knowledge-augmented agents with vector databases, embedders, and document readers.
- [Document Readers](references/document_readers.md) - Extract text from PDF, CSV, JSON, Markdown, web content, and more.
- [Vector Databases](references/vector_databases.md) - 25+ vector databases with a unified interface for knowledge storage and retrieval.
- [Learning](references/learning.md) - Agents that learn, adapt, and improve over time using the Learning Machine.
- [Personal Assistant](references/personal_assistant.md) - A personal assistant that learns about the user over time without being asked.
- [Support Agent](references/support_agent.md) - Multi-tenant support agent that learns solutions and applies them to similar issues.
- [Anthropic](references/anthropic.md) - Use Claude Sonnet and Opus models with Agno agents.
- [AWS Bedrock](references/aws_bedrock.md) - Use Claude, Llama, and other models through AWS Bedrock.
- [Azure OpenAI](references/azure_openai.md) - Use OpenAI models through Azure with enterprise compliance and billing.
- [IBM watsonx](references/ibm_watsonx.md) - Use Granite and Llama models through IBM watsonx.ai.
- [NVIDIA](references/nvidia.md) - Use Llama and Nemotron models with NVIDIA's high-performance inference.
- [Vertex AI](references/vertex_ai.md) - Use Claude and Gemini models through Google Cloud Vertex AI.
- [Google](references/google.md) - Use Gemini models with video, audio, search, and Imagen generation.
- [llama.cpp](references/llamacpp.md) - Run GGUF models with llama.cpp for efficient CPU inference.
- [LM Studio](references/lm_studio.md) - Run models locally with LM Studio's desktop interface.
- [Ollama](references/ollama.md) - Run open-source models locally with Ollama.
- [vLLM](references/vllm.md) - High-throughput inference with vLLM for production deployments.
- [Cohere](references/cohere.md) - Use Command-R and Command-R+ models with Agno agents.
- [DeepSeek](references/deepseek.md) - Use DeepSeek-Chat and DeepSeek-Reasoner models with Agno agents.
- [Fireworks](references/fireworks.md) - Use Llama and Mistral models with optimized inference via Fireworks.
- [Groq](references/groq.md) - Use Llama, Mixtral, and DeepSeek models with ultra-fast inference.
- [Mistral](references/mistral.md) - Use Mistral Large, Medium, and Small models with Agno agents.
- [Together](references/together.md) - Use Llama, Mistral, Qwen, and other open-source models via Together.
- [OpenAI](references/openai.md) - Use OpenAI's GPT models with Agno agents.
- [Model Examples](references/model_examples.md) - 700+ examples across 40+ LLM providers. Same Agent code, use any model.
- [Overview](references/overview.md) - 2000+ examples covering 40+ models, 100+ tools and 18 vector databases.
- [Storage](references/storage.md) - Persist agent sessions, chat history, and state with 13 database backends.
- [Agentic RAG](references/agentic_rag.md) - Document Q&A with knowledge base, citations, and conversation memory.
- [Sage: Answer Engine](references/sage_answer_engine.md) - Perplexity-style search with web search and deep contextual analysis.
- [Streamlit Apps](references/streamlit_apps.md) - 3 production-ready applications with Streamlit UI for RAG, SQL, and search.
- [SQL Agent](references/sql_agent.md) - Natural language to SQL with dynamic few-shot learning and Agentic RAG.
- [AI Support Team](references/ai_support_team.md)
- [Content Team](references/content_team.md)
- [HackerNews Team](references/hackernews_team.md)
- [News Agency Team](references/news_agency_team.md)
- [Teams](references/teams.md) - Multi-agent teams for content pipelines, editorial workflows, and intelligent routing.
- [Reasoning Team](references/reasoning_team.md)
- [Built-in Tools](references/built_in_tools.md) - Ready-to-use tools for search, databases, APIs, social media, and more.
- [Custom Tools](references/custom_tools.md) - Build custom tools with the @tool decorator for any functionality.
- [MCP Integration](references/mcp_integration.md) - Connect agents to external tools and data sources via the Model Context Protocol.
- [Tools](references/tools.md) - Extend agents with tools to interact with external systems, APIs, and services.
- [Tool Hooks](references/tool_hooks.md) - Run custom logic before and after tool execution for logging, validation, and more.
- [Blog Post Generator](references/blog_post_generator.md)
- [Company Description Workflow](references/company_description_workflow.md)
- [Employee Recruiter](references/employee_recruiter.md)
- [Notion Knowledge Manager](references/notion_knowledge_manager.md)
- [Workflows](references/workflows.md) - Sequential caching, parallel execution, and conditional branching patterns.
- [What is Culture?](references/what_is_culture.md) - Enable your agents to share universal knowledge, principles, and best practices that compound across all interactions.
- [Custom Logging](references/custom_logging.md) - Configure custom loggers and formatters for your Agno setup.
- [Chat History](references/chat_history.md) - Include previous messages in context for multi-turn conversations.
- [Database](references/database.md) - Give your agents persistent storage for sessions, context, memory and knowledge.
- [Async MongoDB](references/async_mongodb.md) - Use MongoDB asynchronously for agent session storage.
- [Async MongoDB for Agent](references/async_mongodb_for_agent.md)
- [Async MongoDB for Team](references/async_mongodb_for_team.md)
- [Async MongoDB for Workflow](references/async_mongodb_for_workflow.md)
- [Async MySQL](references/async_mysql.md) - Use MySQL asynchronously for agent session storage.
- [Async MySQL for Agent](references/async_mysql_for_agent.md)
- [Async MySQL for Team](references/async_mysql_for_team.md)
- [Async MySQL for Workflows](references/async_mysql_for_workflows.md)
- [Async PostgreSQL](references/async_postgresql.md) - Use PostgreSQL asynchronously for agent session storage.
- [Async Postgres for Agent](references/async_postgres_for_agent.md)
- [Async Postgres for Team](references/async_postgres_for_team.md)
- [Async Postgres for Workflows](references/async_postgres_for_workflows.md)
- [Async SQLite](references/async_sqlite.md) - Use SQLite asynchronously for agent session storage.
- [Async Sqlite for Agent](references/async_sqlite_for_agent.md)
- [Async Sqlite for Team](references/async_sqlite_for_team.md)
- [Async SQLite for Workflow](references/async_sqlite_for_workflow.md)
- [DynamoDB](references/dynamodb.md) - Use DynamoDB for agent session storage and persistence.
- [DynamoDB for Agent](references/dynamodb_for_agent.md)
- [DynamoDB for Team](references/dynamodb_for_team.md)
- [DynamoDB Workflow Storage](references/dynamodb_workflow_storage.md)
- [Firestore](references/firestore.md) - Use Firestore for agent session storage and persistence.
- [Firestore for Agent](references/firestore_for_agent.md)
- [Firestore for Team](references/firestore_for_team.md)
- [Firestore for Workflows](references/firestore_for_workflows.md)
- [JSON files as database, on Google Cloud Storage (GCS)](references/json_files_as_database_on_google_cloud_storage_gcs.md) - Use Google Cloud Storage for JSON-based agent session storage.
- [Google Cloud Storage for Agent](references/google_cloud_storage_for_agent.md)
- [GCS for Team](references/gcs_for_team.md)
- [GCS for Workflows](references/gcs_for_workflows.md)
- [In-Memory Storage](references/in_memory_storage.md) - Use in-memory storage for testing and development.
- [In-Memory Storage for Agents](references/in_memory_storage_for_agents.md)
- [In-Memory Storage for Teams](references/in_memory_storage_for_teams.md)
- [In-Memory Storage for Workflows](references/in_memory_storage_for_workflows.md)
- [JSON Files as Database](references/json_files_as_database.md) - Use local JSON files for simple agent session storage.
- [JSON for Agent](references/json_for_agent.md)
- [JSON for Team](references/json_for_team.md)
- [JSON for Workflows](references/json_for_workflows.md)
- [MongoDB Database](references/mongodb_database.md) - Use MongoDB for agent session storage and persistence.
- [MongoDB for Agent](references/mongodb_for_agent.md)
- [MongoDB for Team](references/mongodb_for_team.md)
- [MongoDB for Workflow](references/mongodb_for_workflow.md)
- [MySQL](references/mysql.md) - Use MySQL for agent session storage and persistence.
- [MySQL for Agent](references/mysql_for_agent.md)
- [MySQL for Team](references/mysql_for_team.md)
- [MySQL Workflow Storage](references/mysql_workflow_storage.md)
- [Neon](references/neon.md) - Use Neon serverless PostgreSQL for agent session storage.
- [Database Index](references/database_index.md) - Index of all databases supported by Agno.
- [PostgreSQL](references/postgresql.md) - Use PostgreSQL for agent session storage and persistence.
- [Postgres for Agent](references/postgres_for_agent.md)
- [Postgres for Team](references/postgres_for_team.md)
- [Postgres for Workflows](references/postgres_for_workflows.md)
- [Redis](references/redis.md) - Use Redis for agent session storage and persistence.
- [Redis for Agent](references/redis_for_agent.md)
- [Redis for Team](references/redis_for_team.md)
- [Redis for Workflows](references/redis_for_workflows.md)
- [Selecting Custom Table Names](references/selecting_custom_table_names.md)
- [Singlestore](references/singlestore.md) - Use SingleStore for agent session storage and persistence.
- [Singlestore for Agent](references/singlestore_for_agent.md)
- [Singlestore for Team](references/singlestore_for_team.md)
- [Singlestore for Workflow](references/singlestore_for_workflow.md)
- [SQLite](references/sqlite.md) - Use SQLite for local agent session storage.
- [Sqlite for Agent](references/sqlite_for_agent.md)
- [Sqlite for Team](references/sqlite_for_team.md)
- [SQLite for Workflow](references/sqlite_for_workflow.md)
- [Supabase](references/supabase.md) - Use Supabase PostgreSQL for agent session storage.
- [SurreabDB](references/surreabdb.md) - Use SurrealDB for agent session storage.
- [SurrealDB for Agent](references/surrealdb_for_agent.md)
- [SurrealDB for Team](references/surrealdb_for_team.md)
- [SurrealDB for Workflow](references/surrealdb_for_workflow.md)
- [Session Storage](references/session_storage.md) - Store and retrieve agent sessions from your database.
- [Access Dependencies in Tool](references/access_dependencies_in_tool.md) - This example demonstrates how tools can access dependencies passed to the agent, allowing tools to utilize dynamic context like user profiles and current time information for enhanced functionality.
- [Add Dependencies to Agent Run](references/add_dependencies_to_agent_run.md) - This example demonstrates how to inject dependencies into agent runs, allowing the agent to access dynamic context like user profiles and current time information for personalized responses.
- [Add Dependencies to Agent Context](references/add_dependencies_to_agent_context.md) - This example demonstrates how to create a context-aware agent that can access real-time HackerNews data through dependency injection, enabling the agent to provide current information.
- [Dependencies with Agents](references/dependencies_with_agents.md) - Inject variables into agent context with dependencies.
- [Dependencies](references/dependencies.md) - Inject variables into agent and team context with dependencies.
- [Access Dependencies in Team Tool](references/access_dependencies_in_team_tool.md) - This example demonstrates how team tools can access dependencies passed to the team, allowing tools to utilize dynamic context like team metrics and current time information while team members collaborate with shared data sources.
- [Adding Dependencies to Team Run](references/adding_dependencies_to_team_run.md) - This example demonstrates how to add dependencies to a specific team run. Dependencies are functions that provide contextual information (like user profiles and current context) that get passed to the team during execution for personalized responses.
- [Adding Dependencies to Team Context](references/adding_dependencies_to_team_context.md) - This example demonstrates how to add dependencies directly to the team context. Unlike adding dependencies per run, this approach makes the dependency functions available to all team runs by default, providing consistent access to contextual information across all interactions.
- [Dependencies with Teams](references/dependencies_with_teams.md) - Inject variables into team context with dependencies.
- [Using Reference Dependencies in Team Instructions](references/using_reference_dependencies_in_team_instructions.md) - This example demonstrates how to use reference dependencies by defining them in the team constructor and referencing them directly in team instructions. This approach allows dependencies to be automatically injected into the team's context and referenced using template variables in instructions.
- [Accuracy Evals](references/accuracy_evals.md) - Accuracy evals measure how well your Agents and Teams perform against a gold-standard answer using LLM-as-a-judge methodology.
- [Async Accuracy Evaluation](references/async_accuracy_evaluation.md) - Example showing how to run accuracy evaluations asynchronously for better performance.
- [Comparison Accuracy Evaluation](references/comparison_accuracy_evaluation.md) - Example showing how to evaluate agent accuracy on comparison tasks.
- [Accuracy with Database Logging](references/accuracy_with_database_logging.md) - Example showing how to store evaluation results in the database for tracking and analysis.
- [Accuracy with Given Answer](references/accuracy_with_given_answer.md) - Example showing how to evaluate the accuracy of an Agno Agent's response with a given answer.
- [Accuracy with Teams](references/accuracy_with_teams.md) - Example showing how to evaluate the accuracy of an Agno Team.
- [Accuracy with Tools](references/accuracy_with_tools.md) - Example showing an evaluation that runs the provided agent with the provided input and then evaluates the answer that the agent gives.
- [Basic Accuracy](references/basic_accuracy.md) - Example showing how to check how complete, correct and accurate an Agno Agent's response is.
- [Agent as Judge Evals](references/agent_as_judge_evals.md) - Agent as Judge evals measure custom quality criteria for your Agents and Teams using LLM-as-a-judge methodology.
- [Async Agent as Judge](references/async_agent_as_judge.md) - Asynchronous evaluation with Agent as Judge
- [Basic Agent as Judge](references/basic_agent_as_judge.md) - Basic usage of Agent as Judge evaluation with numeric scoring and failure callbacks
- [Batch Agent as Judge](references/batch_agent_as_judge.md) - Evaluate multiple input/output pairs in a single batch
- [Binary Agent as Judge](references/binary_agent_as_judge.md) - Binary pass/fail evaluation without numeric scoring
- [Agent as Judge with Custom Evaluator](references/agent_as_judge_with_custom_evaluator.md) - Using a custom evaluator agent with specific instructions
- [Agent as Judge as Post-Hook](references/agent_as_judge_as_post_hook.md) - Using Agent as Judge evaluation as a post-hook for automatic evaluation
- [Agent as Judge with Teams](references/agent_as_judge_with_teams.md) - Evaluating team outputs with Agent as Judge
- [Async Team Post-Hook Agent as Judge](references/async_team_post_hook_agent_as_judge.md) - Automatic async evaluation of team outputs using post-hooks
- [Agent as Judge with Guidelines](references/agent_as_judge_with_guidelines.md) - Using additional guidelines for more detailed evaluation criteria
- [What is Evals](references/what_is_evals.md) - Evals is a way to measure the quality of your Agents and Teams.<br/> Agno provides multiple dimensions for evaluating Agents.
- [Performance Evals](references/performance_evals.md) - Performance evals measure the latency and memory footprint of an Agent or Team.
- [Performance on Agent Instantiation](references/performance_on_agent_instantiation.md) - Example showing how to analyze the runtime and memory usage of an Agent.
- [Async Performance Evaluation](references/async_performance_evaluation.md) - Example showing how to run performance evaluations on async functions.
- [Performance with Database Logging](references/performance_with_database_logging.md) - Example showing how to store performance evaluation results in the database.
- [Performance on Agent Instantiation with Tool](references/performance_on_agent_instantiation_with_tool.md) - Example showing how to analyze the runtime and memory usage of an Agent that is using tools.
- [Performance on Agent Response](references/performance_on_agent_response.md) - Example showing how to analyze the runtime and memory usage of an Agent's run, given its response.
- [Performance with Teams](references/performance_with_teams.md) - Example showing how to analyze the runtime and memory usage of an Agno Team.
- [Team Performance with Memory](references/team_performance_with_memory.md) - Example showing how to evaluate team performance with memory tracking and growth monitoring.
- [Performance with Memory Updates](references/performance_with_memory_updates.md) - Example showing how to evaluate performance when memory updates are involved.
- [Performance on Agent with Storage](references/performance_on_agent_with_storage.md) - Example showing how to analyze the runtime and memory usage of an Agent that is using storage.
- [Reliability Evals](references/reliability_evals.md) - Reliability evals measure how well your Agents and Teams handle tool calls and error scenarios.
- [Reliability with Single Tool](references/reliability_with_single_tool.md) - Example showing how to assert an Agent is making the expected tool calls.
- [Async Reliability Evaluation](references/async_reliability_evaluation.md) - Example showing how to run reliability evaluations asynchronously.
- [Reliability with Database Logging](references/reliability_with_database_logging.md) - Example showing how to store reliability evaluation results in the database.
- [Single Tool Reliability](references/single_tool_reliability.md) - Example showing how to evaluate reliability of single tool calls.
- [Team Reliability with Stock Tools](references/team_reliability_with_stock_tools.md) - Example showing how to evaluate team reliability with real-world tools like stock price lookup.
- [Reliability with Multiple Tools](references/reliability_with_multiple_tools.md) - Example showing how to assert an Agno Agent is making multiple expected tool calls.
- [Reliability with Teams](references/reliability_with_teams.md) - Example showing how to assert an Agno Team is making the expected tool calls.
- [When to use a Workflow vs a Team in Agno](references/when_to_use_a_workflow_vs_a_team_in_agno.md)
- [AgentOS Connection Issues](references/agentos_connection_issues.md)
- [Connecting to Tableplus](references/connecting_to_tableplus.md)
- [Could Not Connect To Docker](references/could_not_connect_to_docker.md)
- [Setting Environment Variables](references/setting_environment_variables.md)
- [OpenAI Key Request While Using Other Models](references/openai_key_request_while_using_other_models.md)
- [Authorization Failed - JWT Verification](references/authorization_failed___jwt_verification.md)
- [Structured outputs](references/structured_outputs.md)
- [How to Switch Between Different Models](references/how_to_switch_between_different_models.md)
- [Tokens-per-minute rate limiting](references/tokens_per_minute_rate_limiting.md)
- [Your First Agent](references/your_first_agent.md) - Build your first learning agent.
- [Your First Multi-Agent System](references/your_first_multi_agent_system.md) - Build your first multi-agent system.
- [Get Help](references/get_help.md) - Support, community, and enterprise options.
- [OpenAI Moderation Guardrail](references/openai_moderation_guardrail.md) - Detect content policy violations using OpenAI's moderation API.
- [PII Detection Guardrail](references/pii_detection_guardrail.md) - Detect personally identifiable information in agent inputs.
- [Prompt Injection Guardrail](references/prompt_injection_guardrail.md) - Detect prompt injection attempts in agent inputs.
- [Guardrails](references/guardrails.md) - Built-in safeguards for input validation, PII detection, and prompt injection defense.
- [OpenAI Moderation Guardrail](references/openai_moderation_guardrail.md) - This example demonstrates how to use Agno's built-in OpenAI moderation guardrail to detect and block content that violates OpenAI's content policy.
- [PII Detection Guardrail](references/pii_detection_guardrail.md) - This example demonstrates how to use Agno's built-in PII detection guardrail to protect sensitive data like SSNs, credit cards, emails, and phone numbers.
- [Prompt Injection Guardrail](references/prompt_injection_guardrail.md) - This example demonstrates how to use Agno's built-in prompt injection guardrail to detect and stop prompt injection and jailbreak attempts.
- [OpenAI Moderation Guardrail for Teams](references/openai_moderation_guardrail_for_teams.md) - This example demonstrates how to use Agno's built-in OpenAI moderation guardrail with a Team to detect and block policy violations.
- [PII Detection Guardrail for Teams](references/pii_detection_guardrail_for_teams.md) - This example demonstrates how to use Agno's built-in PII detection guardrail with a Team to protect sensitive data.
- [Prompt Injection Guardrail for Teams](references/prompt_injection_guardrail_for_teams.md) - This example demonstrates how to use Agno's built-in prompt injection guardrail with a Team to stop injection attempts.
- [Chat History](references/chat_history.md)
- [Chat History in Agents](references/chat_history_in_agents.md) - Configure and access agent conversation history.
- [Chat History](references/chat_history.md) - Persist and access conversation history for multi-turn interactions.
- [Member History](references/member_history.md)
- [Chat History in Teams](references/chat_history_in_teams.md) - Manage team session history and conversation context.
- [Direct Response with Team History](references/direct_response_with_team_history.md)
- [Share Member Interactions](references/share_member_interactions.md)
- [Team History](references/team_history.md)
- [Per-Step History](references/per_step_history.md) - This example demonstrates a workflow with history enabled for a specific step.
- [History in Functions](references/history_in_functions.md) - This example demonstrates how to get workflow history in a custom function.
- [Intent Routing](references/intent_routing.md) - This example demonstrates how to use workflow history in intent routing.
- [Multi-Purpose CLI](references/multi_purpose_cli.md) - This example demonstrates how to use workflow history in a multi purpose CLI.
- [Workflow History & Continuous Execution](references/workflow_history__continuous_execution.md) - Build conversational workflows that maintain context across multiple executions, creating truly intelligent and natural interactions.
- [Single Step Workflow](references/single_step_workflow.md) - This example demonstrates a workflow with a single step that is executed continuously with access to workflow history.
- [Multi-Step Workflow](references/multi_step_workflow.md) - This example demonstrates a workflow with history enabled for specific steps.
- [Dynamic User Input](references/dynamic_user_input.md) - Let agents request user input dynamically as needed during execution.
- [External Tool Execution](references/external_tool_execution.md) - Execute tools outside of the agent's control for enhanced security and flexibility.
- [Human-in-the-Loop in Agents](references/human_in_the_loop_in_agents.md) - Control agent execution flow with human oversight and input.
- [Agentic User Input with Control Flow](references/agentic_user_input_with_control_flow.md) - This example demonstrates how to use UserControlFlowTools to allow agents to dynamically request user input when they need additional information to complete tasks.
- [Tool Confirmation Required](references/tool_confirmation_required.md) - This example demonstrates how to implement human-in-the-loop functionality by requiring user confirmation before executing sensitive tool operations.
- [Async Tool Confirmation Required](references/async_tool_confirmation_required.md) - This example demonstrates how to implement human-in-the-loop functionality with async agents, requiring user confirmation before executing tool operations.
- [Confirmation Required with Mixed Tools](references/confirmation_required_with_mixed_tools.md) - This example demonstrates human-in-the-loop functionality where only some tools require user confirmation. The agent executes tools that don't require confirmation automatically and pauses only for tools that need approval.
- [Confirmation Required with Multiple Tools](references/confirmation_required_with_multiple_tools.md) - This example demonstrates human-in-the-loop functionality with multiple tools that require confirmation. It shows how to handle user confirmation during tool execution and gracefully cancel operations based on user choice.
- [Confirmation Required with Async Streaming](references/confirmation_required_with_async_streaming.md) - This example demonstrates human-in-the-loop functionality with asynchronous streaming responses. It shows how to handle user confirmation during tool execution in an async environment while maintaining real-time streaming.
- [Confirmation Required with Toolkit](references/confirmation_required_with_toolkit.md) - This example demonstrates human-in-the-loop functionality using toolkit-based tools that require confirmation. It shows how to handle user confirmation when working with pre-built tool collections like YFinanceTools.
- [Confirmation Required with History](references/confirmation_required_with_history.md) - This example demonstrates human-in-the-loop functionality while maintaining conversation history. It shows how user confirmation works when the agent has access to previous conversation context.
- [Confirmation Required with Run ID](references/confirmation_required_with_run_id.md) - This example demonstrates human-in-the-loop functionality using specific run IDs for session management. It shows how to continue agent execution with updated tools using run identifiers.
- [External Tool Execution](references/external_tool_execution.md) - This example demonstrates how to execute tools outside of the agent using external tool execution. This pattern allows you to control tool execution externally while maintaining agent functionality.
- [External Tool Execution Async](references/external_tool_execution_async.md) - This example demonstrates how to execute tools outside of the agent using external tool execution in an asynchronous environment. This pattern allows you to control tool execution externally while maintaining agent functionality with async operations.
- [External Tool Execution Stream Async](references/external_tool_execution_stream_async.md) - This example demonstrates how to execute tools outside of the agent using external tool execution with async streaming responses. It shows how to handle external tool execution in an asynchronous environment while maintaining real-time streaming.
- [External Tool Execution Toolkit](references/external_tool_execution_toolkit.md) - This example demonstrates how to execute toolkit-based tools outside of the agent using external tool execution. It shows how to create a custom toolkit with tools that require external execution.
- [User Input Required for Tool Execution](references/user_input_required_for_tool_execution.md) - This example demonstrates how to create tools that require user input before execution, allowing for dynamic data collection during agent runs.
- [User Input Required All Fields](references/user_input_required_all_fields.md) - This example demonstrates how to use the requires_user_input parameter to collect input for all fields in a tool. It shows how to handle user input schema and collect values for each required field.
- [User Input Required Async](references/user_input_required_async.md) - This example demonstrates how to use the requires_user_input parameter with asynchronous operations. It shows how to collect specific user input fields in an async environment.
- [User Input Required Stream Async](references/user_input_required_stream_async.md) - This example demonstrates how to use the requires_user_input parameter with async streaming responses. It shows how to collect specific user input fields in an asynchronous environment while maintaining real-time streaming.
- [User Confirmation](references/user_confirmation.md) - Require explicit user approval before executing tool calls in your agents.
- [User Input](references/user_input.md) - Gather specific information from users during agent execution.
- [Pre-hooks and Post-hooks](references/pre_hooks_and_post_hooks.md) - Execute custom logic before and after agent runs with hooks.
- [Input Transformation Pre-Hook](references/input_transformation_pre_hook.md)
- [Input Validation Pre-Hook](references/input_validation_pre_hook.md)
- [Output Transformation Post-Hook](references/output_transformation_post_hook.md)
- [Output Validation Post-Hook](references/output_validation_post_hook.md)
- [Input Transformation Pre-Hook](references/input_transformation_pre_hook.md)
- [Input Validation Pre-Hook](references/input_validation_pre_hook.md)
- [Output Transformation Post-Hook](references/output_transformation_post_hook.md)
- [Output Validation Post-Hook](references/output_validation_post_hook.md)
- [Welcome to Agno](references/welcome_to_agno.md) - Build multi-agent systems that learn. Create agents, teams, and workflows with memory, knowledge, and 100+ tool integrations.
- [Agno Infra](references/agno_infra.md)
- [Multimodal I/O](references/multimodal_io.md) - Pass images, audio, video, and files to agents.
- [Output Model](references/output_model.md) - Use secondary model, custom styling to refine the final output.
- [Input & Output](references/input__output.md) - Learn how to pass data to agents and handle their responses.
- [Structured Input for Agents](references/structured_input_for_agents.md) - Validate input data for agents with Pydantic models.
- [Structured Input for Teams](references/structured_input_for_teams.md) - Validate input data for teams with Pydantic models.
- [Structured Output for Agents](references/structured_output_for_agents.md) - Get validated Pydantic object from agent instead of raw text.
- [Structured Output for Teams](references/structured_output_for_teams.md) - Get validated Pydantic object from team instead of raw text.
- [Discord Bot](references/discord_bot.md) - Host agents as Discord Bots.
- [Agent with Media](references/agent_with_media.md)
- [Agent with User Memory](references/agent_with_user_memory.md)
- [Basic](references/basic.md)
- [Memori](references/memori.md) - Memori is an open-source memory layer for AI. It automatically captures conversations, extracts meaningful facts, and makes them searchable across entities, processes, and sessions.
- [Scenario Testing](references/scenario_testing.md)
- [Scenario Testing](references/scenario_testing.md)
- [Introduction](references/introduction.md) - Build multi-agent systems that learn.
- [Agentic RAG with LanceDB](references/agentic_rag_with_lancedb.md)
- [Agentic RAG with PgVector](references/agentic_rag_with_pgvector.md)
- [Agents with Knowledge](references/agents_with_knowledge.md) - Understanding knowledge and how to use it with Agno agents
- [Traditional RAG with LanceDB](references/traditional_rag_with_lancedb.md)
- [Traditional RAG with PgVector](references/traditional_rag_with_pgvector.md)
- [Agentic Chunking](references/agentic_chunking.md)
- [Code Chunking](references/code_chunking.md)
- [CSV Row Chunking](references/csv_row_chunking.md)
- [Custom Chunking](references/custom_chunking.md)
- [Document Chunking](references/document_chunking.md)
- [Fixed Size Chunking](references/fixed_size_chunking.md)
- [Markdown Chunking](references/markdown_chunking.md)
- [Chunking](references/chunking.md) - Split documents into smaller pieces for effective vector search.
- [Recursive Chunking](references/recursive_chunking.md)
- [Semantic Chunking](references/semantic_chunking.md)
- [Contents Database](references/contents_database.md) - Track and manage the content you've added to your knowledge base.
- [Cohere Embedder](references/cohere_embedder.md)
- [Gemini Embedder](references/gemini_embedder.md)
- [Mistral Embedder](references/mistral_embedder.md)
- [Ollama Embedder](references/ollama_embedder.md)
- [OpenAI Embedder](references/openai_embedder.md)
- [Embedders](references/embedders.md) - Convert text into vector representations for semantic search.
- [VoyageAI Embedder](references/voyageai_embedder.md)
- [Filtering](references/filtering.md) - Filter knowledge searches by metadata for precise retrieval.
- [Performance Tips](references/performance_tips.md) - Optimize knowledge base performance, search quality, and content loading speed.
- [CSV Reader](references/csv_reader.md)
- [JSON Reader](references/json_reader.md)
- [Markdown Reader](references/markdown_reader.md)
- [Readers](references/readers.md) - Convert files, URLs, and text into searchable documents.
- [PDF Reader](references/pdf_reader.md)
- [Website Reader](references/website_reader.md)
- [YouTube Reader](references/youtube_reader.md)
- [Agentic RAG with Reranking](references/agentic_rag_with_reranking.md) - Combine agentic search, hybrid retrieval, and reranking for high-quality responses.
- [Custom Retriever](references/custom_retriever.md) - Implement custom retrieval logic for full control over how agents search knowledge.
- [Hybrid Search](references/hybrid_search.md) - Combine vector similarity with keyword matching for better retrieval accuracy.
- [Keyword Search](references/keyword_search.md) - Find content using exact word and phrase matching.
- [Search & Retrieval](references/search__retrieval.md) - How agents search knowledge bases to find relevant information.
- [Vector Search](references/vector_search.md) - Find content by semantic meaning using vector similarity.
- [Vector Databases](references/vector_databases.md) - Store embeddings and search for similar content.
- [Overview](references/overview.md) - Give agents access to documents, databases, and domain expertise.
- [Quickstart](references/quickstart.md) - Build a knowledge-powered agent in under 5 minutes.
- [Distributed RAG with LanceDB](references/distributed_rag_with_lancedb.md)
- [Distributed RAG with PgVector](references/distributed_rag_with_pgvector.md)
- [Teams with Knowledge](references/teams_with_knowledge.md) - Use knowledge bases with teams.
- [Team with Knowledge Base](references/team_with_knowledge_base.md)
- [Azure Cosmos DB MongoDB vCore Vector Database](references/azure_cosmos_db_mongodb_vcore_vector_database.md) - Use Azure Cosmos DB MongoDB vCore as a vector database for your Knowledge Base.
- [Cassandra Vector Database](references/cassandra_vector_database.md) - Use Cassandra as a vector database for your Knowledge Base.
- [Cassandra Async](references/cassandra_async.md)
- [Cassandra](references/cassandra.md)
- [ChromaDB Vector Database](references/chromadb_vector_database.md) - Use ChromaDB as a vector database for your Knowledge Base.
- [ChromaDB Async](references/chromadb_async.md)
- [ChromaDB](references/chromadb.md)
- [Chroma Hybrid Search](references/chroma_hybrid_search.md)
- [Clickhouse Vector Database](references/clickhouse_vector_database.md) - Use ClickHouse as a vector database for your Knowledge Base.
- [ClickHouse Async](references/clickhouse_async.md)
- [ClickHouse](references/clickhouse.md)
- [Couchbase Vector Database](references/couchbase_vector_database.md) - Use Couchbase as a vector database for your Knowledge Base.
- [Couchbase Async](references/couchbase_async.md)
- [Couchbase](references/couchbase.md)
- [Vector Store Index](references/vector_store_index.md) - Index of all vector stores supported by Agno.
- [LanceDB Vector Database](references/lancedb_vector_database.md) - Use LanceDB as a vector database for your Knowledge Base.
- [LanceDB Async](references/lancedb_async.md)
- [LanceDB](references/lancedb.md)
- [LanceDB Hybrid Search](references/lancedb_hybrid_search.md)
- [LangChain Vector Database](references/langchain_vector_database.md) - Use LangChain as a vector database for your Knowledge Base.
- [LangChain Async](references/langchain_async.md)
- [LangChain](references/langchain.md)
- [LightRAG Vector Database](references/lightrag_vector_database.md) - Use LightRAG as a vector database for your Knowledge Base.
- [LightRAG Async](references/lightrag_async.md)
- [LightRAG](references/lightrag.md)
- [LlamaIndex Vector Database](references/llamaindex_vector_database.md) - Use LlamaIndex as a vector database for your Knowledge Base.
- [LlamaIndex Async](references/llamaindex_async.md)
- [LlamaIndex](references/llamaindex.md)
- [Milvus Vector Database](references/milvus_vector_database.md) - Use Milvus as a vector database for your Knowledge Base.
- [Milvus Async](references/milvus_async.md)
- [Milvus Async Hybrid Search](references/milvus_async_hybrid_search.md)
- [Milvus](references/milvus.md)
- [Milvus Hybrid Search](references/milvus_hybrid_search.md)
- [MongoDB Vector Database](references/mongodb_vector_database.md) - Use MongoDB as a vector database for your Knowledge Base.
- [MongoDB Async](references/mongodb_async.md)
- [MongoDB](references/mongodb.md)
- [MongoDB Hybrid Search](references/mongodb_hybrid_search.md)
- [PgVector Vector Database](references/pgvector_vector_database.md) - Use PgVector as a vector database for your Knowledge Base.
- [PgVector Async](references/pgvector_async.md)
- [PgVector](references/pgvector.md)
- [PgVector Hybrid Search](references/pgvector_hybrid_search.md)
- [Pinecone Vector Database](references/pinecone_vector_database.md) - Use Pinecone as a vector database for your Knowledge Base.
- [Pinecone Async](references/pinecone_async.md)
- [Pinecone](references/pinecone.md)
- [Qdrant Vector Database](references/qdrant_vector_database.md) - Use Qdrant as a vector database for your Knowledge Base.
- [Qdrant Async](references/qdrant_async.md)
- [Qdrant](references/qdrant.md)
- [Qdrant Hybrid Search](references/qdrant_hybrid_search.md)
- [Redis Vector Database](references/redis_vector_database.md) - Use Redis as a vector database for your Knowledge Base.
- [Redis Async](references/redis_async.md)
- [Redis](references/redis.md)
- [SingleStore Vector Database](references/singlestore_vector_database.md) - Use SingleStore as a vector database for your Knowledge Base.
- [SingleStore Async](references/singlestore_async.md)
- [SingleStore](references/singlestore.md)
- [SurrealDB Vector Database](references/surrealdb_vector_database.md) - Use SurrealDB as a vector database for your Knowledge Base.
- [SurrealDB Async](references/surrealdb_async.md)
- [SurrealDB](references/surrealdb.md)
- [Upstash Vector Database](references/upstash_vector_database.md) - Use Upstash as a vector database for your Knowledge Base.
- [Upstash Async](references/upstash_async.md)
- [Upstash](references/upstash.md)
- [Weaviate Vector Database](references/weaviate_vector_database.md) - Use Weaviate as a vector database for your Knowledge Base.
- [Weaviate Async](references/weaviate_async.md)
- [Weaviate](references/weaviate.md)
- [Weaviate Hybrid Search](references/weaviate_hybrid_search.md)
- [Custom Schemas](references/custom_schemas.md) - Extend stores with custom fields for your domain.
- [Learning Modes](references/learning_modes.md) - Control when and how agents learn.
- [Learning Machines](references/learning_machines.md) - Agents that learn and improve with every interaction.
- [Quickstart](references/quickstart.md) - Enable learning in your agents.
- [Decision Log](references/decision_log.md) - Decisions with reasoning for auditing and learning.
- [Entity Memory](references/entity_memory.md) - Facts about companies, projects, and people.
- [Learning Stores](references/learning_stores.md) - Each store captures a different type of knowledge.
- [Learned Knowledge](references/learned_knowledge.md) - Insights that transfer across users.
- [Session Context](references/session_context.md) - Goals, plans, and progress for active sessions.
- [User Memory](references/user_memory.md) - Unstructured observations about users.
- [User Profile](references/user_profile.md) - Structured facts about users.
- [Agent with Memory](references/agent_with_memory.md) - Give agents persistent memory across sessions.
- [Agentic Memory](references/agentic_memory.md)
- [Share Memory between Agents](references/share_memory_between_agents.md)
- [Custom Memory Manager](references/custom_memory_manager.md)
- [Multi-user, Multi-session Chat](references/multi_user_multi_session_chat.md)
- [Multi-User, Multi-Session Chat Concurrently](references/multi_user_multi_session_chat_concurrently.md)
- [Agent Memory](references/agent_memory.md) - Memory gives an Agent the ability to recall information about the user.
- [Share Memory and History between Agents](references/share_memory_and_history_between_agents.md)
- [Production Best Practices](references/production_best_practices.md) - Avoid common pitfalls, optimize costs, and ensure reliable memory behavior in production.
- [What is Memory?](references/what_is_memory.md) - Give your agents the ability to remember user preferences, context, and past interactions for truly personalized experiences.
- [Teams with Memory](references/teams_with_memory.md) - Use persistent memory with teams.
- [Team with Agentic Memory](references/team_with_agentic_memory.md)
- [Team with Memory Manager](references/team_with_memory_manager.md)
- [Custom Memory Instructions](references/custom_memory_instructions.md)
- [Memory Creation](references/memory_creation.md)
- [Memory Optimization](references/memory_optimization.md)
- [Memory Search](references/memory_search.md)
- [Memory with MongoDB](references/memory_with_mongodb.md)
- [Working with Memories](references/working_with_memories.md) - Customize how memories are created, control context inclusion, share memories across agents, and use memory tools for advanced workflows.
- [Memory with PostgreSQL](references/memory_with_postgresql.md)
- [Memory with Redis](references/memory_with_redis.md)
- [Memory with SQLite](references/memory_with_sqlite.md)
- [Standalone Memory](references/standalone_memory.md)
- [Response Caching](references/response_caching.md) - Cache model responses locally to reduce costs during development and testing.
- [Compatibility Overview](references/compatibility_overview.md) - Understand which features are supported across different model providers in Agno.
- [Model as String](references/model_as_string.md) - Use the convenient provider:model_id string format to specify models without importing model classes.
- [What are Models?](references/what_are_models.md) - Language Models are machine-learning programs that are trained to understand natural language and code.
- [AWS Bedrock](references/aws_bedrock.md) - Use AWS Bedrock foundation models with Agno agents.
- [Basic Agent](references/basic_agent.md)
- [Streaming Agent](references/streaming_agent.md)
- [Agent with Image Input](references/agent_with_image_input.md)
- [Agent with Knowledge](references/agent_with_knowledge.md)
- [Agent with Storage](references/agent_with_storage.md)
- [Agent with Structured Outputs](references/agent_with_structured_outputs.md)
- [Agent with Tools](references/agent_with_tools.md)
- [AWS Claude](references/aws_claude.md) - Use Claude models through AWS Bedrock with Agno agents.
- [Basic Agent](references/basic_agent.md)
- [Streaming Agent](references/streaming_agent.md)
- [Agent with Knowledge](references/agent_with_knowledge.md)
- [Agent with Storage](references/agent_with_storage.md)
- [Agent with Structured Outputs](references/agent_with_structured_outputs.md)
- [Agent with Tools](references/agent_with_tools.md)
- [Azure AI Foundry](references/azure_ai_foundry.md) - Use Azure AI Foundry hosted models with Agno agents.
- [Basic Agent](references/basic_agent.md)
- [Basic Streaming](references/basic_streaming.md)
- [Agent with Knowledge Base](references/agent_with_knowledge_base.md)
- [Agent with Storage](references/agent_with_storage.md)
- [Agent with Structured Outputs](references/agent_with_structured_outputs.md)
- [Agent with Tools](references/agent_with_tools.md)
- [Azure OpenAI](references/azure_openai.md) - Use OpenAI models through Azure with Agno agents.
- [Basic Agent](references/basic_agent.md)
- [Basic Streaming](references/basic_streaming.md)
- [Agent with Knowledge Base](references/agent_with_knowledge_base.md)
- [Agent with Storage](references/agent_with_storage.md)
- [Agent with Structured Outputs](references/agent_with_structured_outputs.md)
- [Agent with Tools](references/agent_with_tools.md)
- [IBM WatsonX](references/ibm_watsonx.md) - Use IBM WatsonX foundation models with Agno agents.
- [Async Basic Agent](references/async_basic_agent.md)
- [Async Streaming Agent](references/async_streaming_agent.md)
- [Agent with Async Tool Usage](references/agent_with_async_tool_usage.md)
- [Basic Agent](references/basic_agent.md)
- [Streaming Basic Agent](references/streaming_basic_agent.md)
- [Image Agent](references/image_agent.md)
- [RAG Agent](references/rag_agent.md)
- [Agent with Storage](references/agent_with_storage.md)
- [Agent with Structured Output](references/agent_with_structured_output.md)
- [Agent with Tools](references/agent_with_tools.md)
- [Vertex AI Claude](references/vertex_ai_claude.md) - Use Claude models through Vertex AI with Agno agents.
- [Basic Agent](references/basic_agent.md)
- [Streaming Agent](references/streaming_agent.md)
- [Image Input Bytes Content](references/image_input_bytes_content.md)
- [Image Input URL](references/image_input_url.md)
- [PDF Input Bytes Agent](references/pdf_input_bytes_agent.md)
- [PDF Input Local Agent](references/pdf_input_local_agent.md)
- [PDF Input URL Agent](references/pdf_input_url_agent.md)
- [Agent with Structured Outputs](references/agent_with_structured_outputs.md)
- [Agent with Tools](references/agent_with_tools.md)
- [AI/ML API](references/aiml_api.md) - Use AI/ML API with access to 300+ models in Agno agents.
- [Cerebras OpenAI](references/cerebras_openai.md) - Use Cerebras via OpenAI-compatible interface in Agno agents.
- [Basic Agent](references/basic_agent.md)
- [Streaming Agent](references/streaming_agent.md)
- [Agent with Storage](references/agent_with_storage.md)
- [Agent with Storage](references/agent_with_storage.md)
- [Agent with Structured Outputs](references/agent_with_structured_outputs.md)
- [Agent with Tools](references/agent_with_tools.md)
- [Cerebras](references/cerebras.md) - Use Cerebras high-speed inference with Agno agents.
- [Basic Agent](references/basic_agent.md)
- [Streaming Agent](references/streaming_agent.md)
- [Agent with Knowledge Base](references/agent_with_knowledge_base.md)
- [Agent with Storage](references/agent_with_storage.md)
- [Agent with Structured Outputs](references/agent_with_structured_outputs.md)
- [Agent with Tools](references/agent_with_tools.md)
- [CometAPI](references/cometapi.md) - Use CometAPI models with Agno agents.
- [DeepInfra](references/deepinfra.md) - Use DeepInfra models with Agno agents.
- [Basic Agent](references/basic_agent.md)
- [Streaming Agent](references/streaming_agent.md)
- [Agent with Structured Outputs](references/agent_with_structured_outputs.md)
- [Agent with Tools](references/agent_with_tools.md)
- [Fireworks](references/fireworks.md) - Use Fireworks AI models with Agno agents.
- [Basic Agent](references/basic_agent.md)
- [Streaming Agent](references/streaming_agent.md)
- [Agent with Structured Outputs](references/agent_with_structured_outputs.md)
- [Agent with Tools](references/agent_with_tools.md)
- [Groq](references/groq.md) - Use Groq's fast inference API with Agno agents.
- [Basic Agent](references/basic_agent.md)
- [Streaming Agent](references/streaming_agent.md)
- [Browser Search Agent](references/browser_search_agent.md)
- [Deep Knowledge Agent](references/deep_knowledge_agent.md)
- [Image Agent](references/image_agent.md)
- [Agent with Knowledge](references/agent_with_knowledge.md)
- [Agent with Metrics](references/agent_with_metrics.md)
- [Reasoning Agent](references/reasoning_agent.md)
- [Agent with Storage](references/agent_with_storage.md)
- [Agent with Structured Outputs](references/agent_with_structured_outputs.md)
- [Agent with Tools](references/agent_with_tools.md)
- [Transcription Agent](references/transcription_agent.md)
- [Translation Agent](references/translation_agent.md)
- [HuggingFace](references/huggingface.md) - Use Hugging Face models with Agno agents.
- [Async Basic.Py](references/async_basicpy.md)
- [Async Basic Stream.Py](references/async_basic_streampy.md)
- [Basic Agent](references/basic_agent.md)
- [Streaming Agent](references/streaming_agent.md)
- [Llama Essay Writer](references/llama_essay_writer.md)
- [Tool Use](references/tool_use.md)
- [LangDB](references/langdb.md)
- [Basic Agent](references/basic_agent.md)
- [Basic Streaming Agent](references/basic_streaming_agent.md)
- [Data Analyst Agent](references/data_analyst_agent.md)
- [Structured Output](references/structured_output.md)
- [Web Search Agent](references/web_search_agent.md)
- [LiteLLM OpenAI](references/litellm_openai.md) - Use LiteLLM with Agno with an openai-compatible proxy server.
- [Basic Agent](references/basic_agent.md)
- [Streaming Agent](references/streaming_agent.md)
- [Agent with Tools](references/agent_with_tools.md)
- [LiteLLM](references/litellm.md) - Integrate LiteLLM with Agno for a unified LLM experience.
- [Async Basic Agent](references/async_basic_agent.md)
- [Async Basic Streaming Agent](references/async_basic_streaming_agent.md)
- [Async Tool Use](references/async_tool_use.md)
- [Audio Input Agent](references/audio_input_agent.md)
- [Basic Agent](references/basic_agent.md)
- [Streaming Agent](references/streaming_agent.md)
- [Agent with Knowledge](references/agent_with_knowledge.md)
- [Agent with Storage](references/agent_with_storage.md)
- [Agent with Structured Outputs](references/agent_with_structured_outputs.md)
- [Agent with Tools](references/agent_with_tools.md)
- [Nebius Token Factory](references/nebius_token_factory.md) - Use Nebius Token Factory models with Agno agents.
- [Basic Agent](references/basic_agent.md)
- [Streaming Agent](references/streaming_agent.md)
- [Agent with Knowledge](references/agent_with_knowledge.md)
- [Agent with Storage](references/agent_with_storage.md)
- [Agent with Structured Outputs](references/agent_with_structured_outputs.md)
- [Agent with Tools](references/agent_with_tools.md)
- [Neosantara](references/neosantara.md) - Use Neosantara models in Agno agents.
- [Async Basic](references/async_basic.md)
- [Async Basic Stream](references/async_basic_stream.md)
- [Async Tool Use](references/async_tool_use.md)
- [Basic Agent](references/basic_agent.md)
- [Basic Stream](references/basic_stream.md)
- [Structured Output](references/structured_output.md)
- [Tool Use](references/tool_use.md)
- [Nexus](references/nexus.md) - Use Nexus router models with Agno agents.
- [Async Basic Agent](references/async_basic_agent.md)
- [Async Streaming Agent](references/async_streaming_agent.md)
- [Async Agent with Tools](references/async_agent_with_tools.md)
- [Basic Agent](references/basic_agent.md)
- [Basic Streaming Agent](references/basic_streaming_agent.md)
- [Agent with Tools](references/agent_with_tools.md)
- [Nvidia](references/nvidia.md) - Use NVIDIA NeMo models with Agno agents.
- [Async Basic Agent](references/async_basic_agent.md)
- [Async Streaming Agent](references/async_streaming_agent.md)
- [Async Agent with Tools](references/async_agent_with_tools.md)
- [Basic Agent](references/basic_agent.md)
- [Basic Streaming Agent](references/basic_streaming_agent.md)
- [Agent with Tools](references/agent_with_tools.md)
- [OpenRouter](references/openrouter.md) - Use OpenRouter unified API with Agno agents.
- [Portkey](references/portkey.md) - Use Portkey AI Gateway for multi-provider routing with Agno agents.
- [Basic Agent](references/basic_agent.md)
- [Basic Agent with Streaming](references/basic_agent_with_streaming.md)
- [Structured Output Agent](references/structured_output_agent.md)
- [Agent with Tools](references/agent_with_tools.md)
- [Agent with Tools and Streaming](references/agent_with_tools_and_streaming.md)
- [Requesty](references/requesty.md) - Use Requesty AI gateway with Agno agents.
- [Basic Agent](references/basic_agent.md)
- [Streaming Agent](references/streaming_agent.md)
- [Agent with Structured Output](references/agent_with_structured_output.md)
- [Agent with Tools](references/agent_with_tools.md)
- [Sambanova](references/sambanova.md) - Use Sambanova models with Agno agents.
- [SiliconFlow](references/siliconflow.md) - Use SiliconFlow models with Agno agents.
- [Async Basic Agent](references/async_basic_agent.md)
- [Async Streaming Agent](references/async_streaming_agent.md)
- [Async Agent with Tools](references/async_agent_with_tools.md)
- [Basic Agent](references/basic_agent.md)
- [Basic Streaming Agent](references/basic_streaming_agent.md)
- [Agent with Tools](references/agent_with_tools.md)
- [Together](references/together.md) - Use Together AI models with Agno agents.
- [Basic Agent](references/basic_agent.md)
- [Streaming Agent](references/streaming_agent.md)
- [Image Agent](references/image_agent.md)
- [Image Input Bytes Content](references/image_input_bytes_content.md)
- [Image Agent with Memory](references/image_agent_with_memory.md)
- [Agent with Structured Outputs](references/agent_with_structured_outputs.md)
- [Agent with Tools](references/agent_with_tools.md)
- [LlamaCpp](references/llamacpp.md) - Run local models with LlamaCpp in Agno agents.
- [Basic](references/basic.md)
- [Basic Stream](references/basic_stream.md)
- [Structured Output](references/structured_output.md)
- [Agent with Tools](references/agent_with_tools.md)
- [Agent with Tools Stream](references/agent_with_tools_stream.md)
- [LM Studio](references/lm_studio.md) - Run local models with LM Studio in Agno agents.
- [Basic Agent](references/basic_agent.md)
- [Streaming Agent](references/streaming_agent.md)
- [Image Agent](references/image_agent.md)
- [Agent with Knowledge](references/agent_with_knowledge.md)
- [Agent with Storage](references/agent_with_storage.md)
- [Agent with Structured Outputs](references/agent_with_structured_outputs.md)
- [Agent with Tools](references/agent_with_tools.md)
- [Ollama](references/ollama.md) - Run local models with Ollama in Agno agents.
- [Async Basic](references/async_basic.md)
- [Async Basic Stream](references/async_basic_stream.md)
- [Basic](references/basic.md)
- [Basic Stream](references/basic_stream.md)
- [Ollama Cloud](references/ollama_cloud.md)
- [Db](references/db.md)
- [Demo Deepseek R1](references/demo_deepseek_r1.md)
- [Demo Gemma](references/demo_gemma.md)
- [Demo Phi4](references/demo_phi4.md)
- [Demo Qwen](references/demo_qwen.md)
- [Image Agent](references/image_agent.md)
- [Knowledge](references/knowledge.md)
- [Memory](references/memory.md)
- [Multimodal Agent](references/multimodal_agent.md)
- [Set Client](references/set_client.md)
- [Set Temperature](references/set_temperature.md)
- [Agent with Storage](references/agent_with_storage.md)
- [Structured Output](references/structured_output.md)
- [Tool Use](references/tool_use.md)
- [Tool Use Stream](references/tool_use_stream.md)
- [vLLM](references/vllm.md)
- [Async Agent](references/async_agent.md)
- [Async Agent with Streaming](references/async_agent_with_streaming.md)
- [Async Agent with Tools](references/async_agent_with_tools.md)
- [Basic Agent](references/basic_agent.md)
- [Agent with Streaming](references/agent_with_streaming.md)
- [Code Generation](references/code_generation.md)
- [Agent with Memory](references/agent_with_memory.md)
- [Agent with Storage](references/agent_with_storage.md)
- [Structured Output](references/structured_output.md)
- [Agent with Tools](references/agent_with_tools.md)
- [Model Index](references/model_index.md) - Index of all models supported by Agno.
- [Anthropic Claude](references/anthropic_claude.md) - Use Anthropic Claude models with Agno agents.
- [Basic Agent](references/basic_agent.md)
- [Streaming Agent](references/streaming_agent.md)
- [Beta Features](references/beta_features.md) - Enable experimental Anthropic beta features in Agno agents.
- [Response Caching](references/response_caching.md) - Cache model responses to reduce API calls and costs.
- [Code Execution Tool](references/code_execution_tool.md) - Execute Python code in a sandboxed environment with Anthropic's code execution tool.
- [Context Editing](references/context_editing.md) - Automatically manage context size with Anthropic's context editing.
- [File Upload](references/file_upload.md) - Upload and reference files using Anthropic's Files API in Agno.
- [Image Input Bytes Content](references/image_input_bytes_content.md)
- [Image Input URL](references/image_input_url.md)
- [Agent with Knowledge](references/agent_with_knowledge.md)
- [PDF Input Bytes Agent](references/pdf_input_bytes_agent.md)
- [PDF Input Local Agent](references/pdf_input_local_agent.md)
- [PDF Input URL Agent](references/pdf_input_url_agent.md)
- [Prompt Caching](references/prompt_caching.md) - Cache system prompts to reduce processing time and costs with Anthropic models.
- [Claude Agent Skills](references/claude_agent_skills.md) - Create PowerPoint presentations, Excel spreadsheets, Word documents, and analyze PDFs with Claude Agent Skills
- [Agent with Structured Outputs](references/agent_with_structured_outputs.md)
- [Agent with Structured Outputs Streaming](references/agent_with_structured_outputs_streaming.md)
- [Agent with Structured Outputs and Strict Tools](references/agent_with_structured_outputs_and_strict_tools.md)
- [Agent with Tools](references/agent_with_tools.md)
- [Web Fetch](references/web_fetch.md)
- [Cohere](references/cohere.md) - Use Cohere command models with Agno agents.
- [Basic Agent](references/basic_agent.md)
- [Streaming Agent](references/streaming_agent.md)
- [Image Agent](references/image_agent.md)
- [Agent with Knowledge](references/agent_with_knowledge.md)
- [Agent with Storage](references/agent_with_storage.md)
- [Agent with Structured Outputs](references/agent_with_structured_outputs.md)
- [Agent with Tools](references/agent_with_tools.md)
- [DashScope](references/dashscope.md) - Use Alibaba DashScope Qwen models with Agno agents.
- [Basic Agent](references/basic_agent.md)
- [Basic Agent with Streaming](references/basic_agent_with_streaming.md)
- [Image Agent](references/image_agent.md)
- [Image Agent with Bytes](references/image_agent_with_bytes.md)
- [Structured Output Agent](references/structured_output_agent.md)
- [Thinking Agent](references/thinking_agent.md)
- [Agent with Tools](references/agent_with_tools.md)
- [DeepSeek](references/deepseek.md) - Use DeepSeek models with Agno agents.
- [Basic Agent](references/basic_agent.md)
- [Streaming Agent](references/streaming_agent.md)
- [Agent with Structured Outputs](references/agent_with_structured_outputs.md)
- [Agent with Tools](references/agent_with_tools.md)
- [Gemini](references/gemini.md) - Use Google Gemini models with Agno agents.
- [Audio Input (Bytes Content)](references/audio_input_bytes_content.md)
- [Audio Input (Upload the file)](references/audio_input_upload_the_file.md)
- [Audio Input (Local file)](references/audio_input_local_file.md)
- [Basic Agent](references/basic_agent.md)
- [Streaming Agent](references/streaming_agent.md)
- [Agent with External URL Input](references/agent_with_external_url_input.md)
- [Flash Thinking Agent](references/flash_thinking_agent.md)
- [Agent with GCS File Input](references/agent_with_gcs_file_input.md)
- [Agent with Grounding](references/agent_with_grounding.md)
- [Image Editing Agent](references/image_editing_agent.md)
- [Image Generation Agent](references/image_generation_agent.md)
- [Image Generation Agent (Streaming)](references/image_generation_agent_streaming.md)
- [Image Agent](references/image_agent.md)
- [Image Agent with File Upload](references/image_agent_with_file_upload.md)
- [Imagen Tool with OpenAI](references/imagen_tool_with_openai.md)
- [Advanced Imagen Tool with Vertex AI](references/advanced_imagen_tool_with_vertex_ai.md)
- [Agent with Knowledge](references/agent_with_knowledge.md)
- [Agent with PDF Input (Local file)](references/agent_with_pdf_input_local_file.md)
- [Agent with PDF Input (URL)](references/agent_with_pdf_input_url.md)
- [Agent with S3 Pre-signed URL Input](references/agent_with_s3_pre_signed_url_input.md)
- [Agent with Storage](references/agent_with_storage.md)
- [Agent with Structured Outputs](references/agent_with_structured_outputs.md)
- [Agent with Tools](references/agent_with_tools.md)
- [Agent with URL Context](references/agent_with_url_context.md)
- [Agent with URL Context and Search](references/agent_with_url_context_and_search.md)
- [Agent with Vertex AI](references/agent_with_vertex_ai.md)
- [Video Input (Bytes Content)](references/video_input_bytes_content.md)
- [Video Input (File Upload)](references/video_input_file_upload.md)
- [Video Input (Local File Upload)](references/video_input_local_file_upload.md)
- [Meta](references/meta.md) - Use Meta Llama models with Agno agents.
- [Asynchronous Agent](references/asynchronous_agent.md)
- [Asynchronous Streaming Agent](references/asynchronous_streaming_agent.md)
- [Agent with Async Tool Usage](references/agent_with_async_tool_usage.md)
- [Basic Agent](references/basic_agent.md)
- [Streaming Agent](references/streaming_agent.md)
- [Asynchronous Agent with Image Input](references/asynchronous_agent_with_image_input.md)
- [Agent With Knowledge](references/agent_with_knowledge.md)
- [Agent with Memory](references/agent_with_memory.md)
- [Agent with Structured Outputs](references/agent_with_structured_outputs.md)
- [Agent with Tools](references/agent_with_tools.md)
- [Mistral](references/mistral.md) - Use Mistral models with Agno agents.
- [Async Basic Agent](references/async_basic_agent.md)
- [Async Basic Streaming Agent](references/async_basic_streaming_agent.md)
- [Async Structured Output Agent](references/async_structured_output_agent.md)
- [Async Agent with Tools](references/async_agent_with_tools.md)
- [Basic Agent](references/basic_agent.md)
- [Basic Streaming Agent](references/basic_streaming_agent.md)
- [Image Bytes Input Agent](references/image_bytes_input_agent.md)
- [Image Compare Agent](references/image_compare_agent.md)
- [Image File Input Agent](references/image_file_input_agent.md)
- [Image Ocr With Structured Output](references/image_ocr_with_structured_output.md)
- [Image Transcribe Document Agent](references/image_transcribe_document_agent.md)
- [Agent with Memory](references/agent_with_memory.md)
- [Mistral Small](references/mistral_small.md)
- [Structured Output](references/structured_output.md)
- [Structured Output With Tool Use](references/structured_output_with_tool_use.md)
- [Agent with Tools](references/agent_with_tools.md)
- [OpenAI](references/openai.md) - Use OpenAI GPT and o1 models with Agno agents.
- [Audio Input Agent](references/audio_input_agent.md)
- [Audio Output Agent](references/audio_output_agent.md)
- [Basic Agent](references/basic_agent.md)
- [Streaming Agent](references/streaming_agent.md)
- [Response Caching](references/response_caching.md) - Cache model responses to reduce API calls and costs.
- [Generate Images](references/generate_images.md)
- [Image Agent](references/image_agent.md)
- [Agent with Knowledge](references/agent_with_knowledge.md)
- [Agent with Reasoning Effort](references/agent_with_reasoning_effort.md)
- [Agent with Storage](references/agent_with_storage.md)
- [Agent with Structured Outputs](references/agent_with_structured_outputs.md)
- [Agent with Tools](references/agent_with_tools.md)
- [OpenAI Responses](references/openai_responses.md) - Use OpenAI's Responses API with Agno agents.
- [Agent Flex Tier](references/agent_flex_tier.md)
- [Async Basic](references/async_basic.md)
- [Async Basic Stream](references/async_basic_stream.md)
- [Async Tool Use](references/async_tool_use.md)
- [Basic](references/basic.md)
- [Basic Stream](references/basic_stream.md)
- [Db](references/db.md)
- [Deep Research Agent](references/deep_research_agent.md)
- [Image Agent](references/image_agent.md)
- [Image Agent Bytes](references/image_agent_bytes.md)
- [Image Agent With Memory](references/image_agent_with_memory.md)
- [Image Generation Agent](references/image_generation_agent.md)
- [Knowledge](references/knowledge.md)
- [Memory](references/memory.md)
- [Pdf Input Local](references/pdf_input_local.md)
- [Pdf Input Url](references/pdf_input_url.md)
- [Reasoning O3 Mini](references/reasoning_o3_mini.md)
- [Structured Output](references/structured_output.md)
- [Tool Use](references/tool_use.md)
- [Tool Use Gpt 5](references/tool_use_gpt_5.md)
- [Tool Use O3](references/tool_use_o3.md)
- [Tool Use Stream](references/tool_use_stream.md)
- [Verbosity Control](references/verbosity_control.md)
- [Websearch Builtin Tool](references/websearch_builtin_tool.md)
- [ZDR Reasoning Agent](references/zdr_reasoning_agent.md)
- [Perplexity](references/perplexity.md) - Use Perplexity models with built-in web search in Agno agents.
- [Async Basic Agent](references/async_basic_agent.md)
- [Async Basic Streaming Agent](references/async_basic_streaming_agent.md)
- [Basic Agent](references/basic_agent.md)
- [Basic Streaming Agent](references/basic_streaming_agent.md)
- [Agent with Knowledge](references/agent_with_knowledge.md)
- [Agent with Memory](references/agent_with_memory.md)
- [Agent with Structured Output](references/agent_with_structured_output.md)
- [Vercel v0](references/vercel_v0.md) - Use Vercel v0 models for web development with Agno agents.
- [Basic Agent](references/basic_agent.md)
- [Streaming Agent](references/streaming_agent.md)
- [Image Agent](references/image_agent.md)
- [Agent with Knowledge](references/agent_with_knowledge.md)
- [Agent with Tools](references/agent_with_tools.md)
- [xAI](references/xai.md) - Use xAI Grok models with Agno agents.
- [Async Tool Use](references/async_tool_use.md)
- [Basic](references/basic.md)
- [Async Basic Agent](references/async_basic_agent.md)
- [Async Streaming Agent](references/async_streaming_agent.md)
- [Basic Stream](references/basic_stream.md)
- [Image Agent](references/image_agent.md)
- [Image Agent Bytes](references/image_agent_bytes.md)
- [Live Search Agent](references/live_search_agent.md)
- [Live Search Agent Stream](references/live_search_agent_stream.md)
- [Reasoning Agent](references/reasoning_agent.md)
- [Structured Output](references/structured_output.md)
- [Tool Use](references/tool_use.md)
- [Tool Use Stream](references/tool_use_stream.md)
- [OpenAI-compatible models](references/openai_compatible_models.md) - Use any OpenAI-compatible endpoint with Agno agents.
- [Multimodal Agents](references/multimodal_agents.md) - Build agents that process and generate images, audio, video, and files.
- [Agent Using Multimodal Tool Response in Runs](references/agent_using_multimodal_tool_response_in_runs.md)
- [Audio Input Output](references/audio_input_output.md)
- [Audio Multi Turn](references/audio_multi_turn.md)
- [Audio Sentiment Analysis](references/audio_sentiment_analysis.md)
- [Audio Streaming](references/audio_streaming.md)
- [Audio to Text Transcription](references/audio_to_text_transcription.md)
- [Audio Generation Tools](references/audio_generation_tools.md) - Generate audio with text-to-speech tools in Agno agents.
- [Audio As Input](references/audio_as_input.md) - Process audio as input with Agno agents.
- [Audio Model Output](references/audio_model_output.md) - Get audio output from model responses in Agno agents.
- [File Input for Tools](references/file_input_for_tools.md)
- [Generate output image using DALL-E](references/generate_output_image_using_dall_e.md)
- [Generate output image using DALL-E with intermediate steps](references/generate_output_image_using_dall_e_with_intermediate_steps.md)
- [Generate Music using Models Lab](references/generate_music_using_models_lab.md)
- [Generate Video (ModelsLabTools)](references/generate_video_modelslabtools.md)
- [Generate Video (ReplicateTools)](references/generate_video_replicatetools.md)
- [Image Generation Tools](references/image_generation_tools.md) - Generate images using OpenAI tools.
- [Image As Input](references/image_as_input.md) - Pass images to agents for analysis and description.
- [Image Input for Tools](references/image_input_for_tools.md)
- [High Fidelity Image Input](references/high_fidelity_image_input.md)
- [Image Model Output](references/image_model_output.md) - Return generated images from model responses.
- [Image to Audio Story Generation](references/image_to_audio_story_generation.md)
- [Image to Image Generation Agent](references/image_to_image_generation_agent.md)
- [Image to Structured Output](references/image_to_structured_output.md)
- [Image to Text Analysis](references/image_to_text_analysis.md)
- [Video Caption Agent](references/video_caption_agent.md)
- [Shorts from Video](references/shorts_from_video.md)
- [Video Output](references/video_output.md) - Generate videos with AI tools in Agno agents.
- [Video Input](references/video_input.md) - Process video as input with Agno agents.
- [Overview](references/overview.md) - Process and generate images, audio, video, and files with agents and teams.
- [Multimodal Teams](references/multimodal_teams.md) - Create teams that process text, images, audio, video, and files.
- [Audio Sentiment Analysis Team](references/audio_sentiment_analysis_team.md)
- [Audio Transcription Team](references/audio_transcription_team.md)
- [Image Generation Team](references/image_generation_team.md)
- [Image Transformation Team](references/image_transformation_team.md)
- [Image to Structured Movie Script Team](references/image_to_structured_movie_script_team.md)
- [Image to Text Team](references/image_to_text_team.md)
- [Video Captioning Team](references/video_captioning_team.md)
- [AgentOps](references/agentops.md) - Integrate Agno with AgentOps to send traces and logs to a centralized observability platform.
- [Arize](references/arize.md) - Integrate Agno with Arize Phoenix to send traces and gain insights into your agent's performance.
- [Atla](references/atla.md) - Integrate `Atla` with Agno for real-time monitoring, automated evaluation, and performance analytics of your AI agents.
- [LangDB](references/langdb.md) - Integrate Agno with LangDB to trace agent execution, tool calls, and gain comprehensive observability into your agent's performance.
- [Langfuse](references/langfuse.md) - Integrate Agno with Langfuse to send traces and gain insights into your agent's performance.
- [LangSmith](references/langsmith.md) - Integrate Agno with LangSmith to send traces and gain insights into your agent's performance.
- [Langtrace](references/langtrace.md) - Integrate Agno with Langtrace to send traces and gain insights into your agent's performance.
- [LangWatch](references/langwatch.md) - Integrate Agno with LangWatch to send traces and gain insights into your agent's performance.
- [Maxim](references/maxim.md) - Connect Agno with Maxim to monitor, trace, and evaluate your agent's activity and performance.
- [OpenLIT](references/openlit.md) - Integrate Agno with OpenLIT for OpenTelemetry-native observability, tracing, and monitoring of your AI agents.
- [OpenTelemetry](references/opentelemetry.md) - Agno supports observability through OpenTelemetry, integrating seamlessly with popular tracing and monitoring platforms.
- [Traceloop](references/traceloop.md) - Integrate Agno with Traceloop to send traces and gain insights into your agent's performance.
- [Weave](references/weave.md) - Integrate Agno with Weave by WandB to send traces and gain insights into your agent's performance.
- [AgentUI](references/agentui.md) - An Open Source AgentUI for your AgentOS
- [Contributing to Agno](references/contributing_to_agno.md) - Contribute to Agno through the fork and pull request workflow.
- [Cursor Rules for Building Agents](references/cursor_rules_for_building_agents.md) - Use .cursorrules to improve AI coding assistant suggestions when building agents with Agno
- [Database Migrations](references/database_migrations.md) - Migrate Agno database tables between versions.
- [Install & Setup](references/install__setup.md)
- [Agno v2.0 Changelog](references/agno_v20_changelog.md)
- [Migrating to Agno v2.0](references/migrating_to_agno_v20.md) - Guide to migrate your Agno applications from v1 to v2.
- [Migrating to Workflows 2.0](references/migrating_to_workflows_20.md) - Migrate your Workflows from 1.0 to 2.0.
- [Code Review Agent](references/code_review_agent.md) - Review pull requests with context-aware suggestions and best practice enforcement.
- [Competitor Tracker](references/competitor_tracker.md) - Monitor competitor content, pricing, and features. Surface changes automatically.
- [Content Production Team](references/content_production_team.md) - Multi-agent team with Writer, Editor, SEO Optimizer, and Publisher working together.
- [Contract Review Agent](references/contract_review_agent.md) - Analyze legal documents, extract key terms, and flag risks.
- [Customer Support Agent](references/customer_support_agent.md) - Resolve support tickets with knowledge retrieval and smart escalation.
- [Software Development Team](references/software_development_team.md) - Multi-agent team with Architect, Coder, Reviewer, and Tester collaborating on features.
- [Document Summarizer](references/document_summarizer.md) - Structured document summaries with key points, entity extraction, and action item detection from PDFs, text, and URLs.
- [Inbox Agent](references/inbox_agent.md) - Gmail assistant that triages emails, drafts replies, and flags urgent items with safety controls.
- [Invoice Analyst](references/invoice_analyst.md) - Vision-based invoice data extraction from PDFs and images with validation and confidence scoring.
- [Internal Knowledge Agent](references/internal_knowledge_agent.md) - RAG-powered agent for answering questions from company docs with source citations.
- [Lead Enrichment](references/lead_enrichment.md) - Enrich CRM contacts with LinkedIn and company data automatically.
- [Meeting to Tasks](references/meeting_to_tasks.md) - Extract action items from meeting recordings and create Linear issues automatically.
- [Applications](references/applications.md) - Production-ready agents, teams, and workflows to add to your AgentOS.
- [Recipe Agent](references/recipe_agent.md) - Multi-modal RAG agent that retrieves recipes from a knowledge base and generates visual cooking guides.
- [Research Agent](references/research_agent.md) - Autonomous web research agent using Parallel API with multi-source synthesis and citation tracking.
- [RFP Response System](references/rfp_response_system.md) - Multi-agent system for generating comprehensive proposal responses.
- [Sales Call Analyzer](references/sales_call_analyzer.md) - Transcribe sales calls, extract insights, and score conversations.
- [Social Media Analyst](references/social_media_analyst.md) - Brand intelligence agent analyzing X (Twitter) sentiment with engagement metrics and brand health scoring.
- [Startup Analyst](references/startup_analyst.md) - Due diligence agent that crawls company websites and produces investment-grade analysis reports.
- [Text-to-SQL Agent](references/text_to_sql_agent.md) - Self-learning SQL agent that queries databases with knowledge-based query generation and self-improving query storage.
- [Translation Agent](references/translation_agent.md) - Emotion-aware translation agent with localized voice generation using Cartesia TTS.
- [CI/CD](references/cicd.md)
- [Database Tables](references/database_tables.md)
- [Development Application](references/development_application.md)
- [Use Custom Domain and HTTPS](references/use_custom_domain_and_https.md)
- [Environment variables](references/environment_variables.md)
- [Format & Validate](references/format__validate.md)
- [Create Git Repo](references/create_git_repo.md)
- [Infra Settings](references/infra_settings.md)
- [Install & Setup](references/install__setup.md)
- [Setup infra for new users](references/setup_infra_for_new_users.md)
- [Production Application](references/production_application.md)
- [Add Python Libraries](references/add_python_libraries.md)
- [Add Secrets](references/add_secrets.md)
- [SSH Access](references/ssh_access.md)
- [Discord Bot](references/discord_bot.md) - Create a Discord bot powered by Agno agents for community support, moderation, or custom commands.
- [Interfaces](references/interfaces.md) - Connect your agents to Slack, Discord, WhatsApp, and MCP.
- [Slack Bot](references/slack_bot.md) - Build a Slack bot that responds to messages, joins channels, and executes commands using agents.
- [WhatsApp Bot](references/whatsapp_bot.md) - Deploy agents on WhatsApp Business for customer-facing interactions.
- [Deploying to Production](references/deploying_to_production.md) - Deploy AgentOS to your cloud of choice.
- [AgentOS on AWS](references/agentos_on_aws.md) - Deploy AgentOS to AWS ECS.
- [AgentOS on Docker](references/agentos_on_docker.md) - Run AgentOS locally using Docker.
- [Templates](references/templates.md) - Production-ready codebases for deploying AgentOS to your cloud of choice.
- [AgentOS on Railway](references/agentos_on_railway.md) - Deploy AgentOS to Railway.
- [What is Reasoning?](references/what_is_reasoning.md) - Reasoning gives Agents the ability to "think" before responding and "analyze" the results of their actions (i.e. tool calls), greatly improving the Agents' ability to solve problems that require sequential tool calls.
- [Reasoning Agents](references/reasoning_agents.md) - Transform any model into a reasoning system through structured chain-of-thought processing, perfect for complex problems that require multiple steps, tool use, and self-validation.
- [Reasoning Models](references/reasoning_models.md) - Reasoning models are a class of large language models pre-trained to think before they answer. They produce a long internal chain of thought before responding.
- [Reasoning Tools](references/reasoning_tools.md) - Give any model explicit tools for structured thinking, transforming regular models into careful problem-solvers through deliberate reasoning steps.
- [Basic Reasoning Agent](references/basic_reasoning_agent.md) - Equip agents with chain-of-thought reasoning capabilities.
- [Capture Reasoning Content](references/capture_reasoning_content.md)
- [Non-Reasoning Model Agent](references/non_reasoning_model_agent.md)
- [Team with Chain of Thought](references/team_with_chain_of_thought.md)
- [Azure AI Foundry](references/azure_ai_foundry.md)
- [Azure OpenAI o1](references/azure_openai_o1.md)
- [Azure OpenAI o3](references/azure_openai_o3.md)
- [Azure OpenAI GPT 4.1](references/azure_openai_gpt_41.md)
- [DeepSeek Reasoner](references/deepseek_reasoner.md)
- [Groq DeepSeek R1](references/groq_deepseek_r1.md)
- [Groq Claude + DeepSeek R1](references/groq_claude__deepseek_r1.md)
- [Ollama DeepSeek R1](references/ollama_deepseek_r1.md)
- [OpenAI GPT-5-mini](references/openai_gpt_5_mini.md)
- [OpenAI gpt-5-mini with Tools](references/openai_gpt_5_mini_with_tools.md)
- [OpenAI o1 pro](references/openai_o1_pro.md)
- [OpenAI o4-mini](references/openai_o4_mini.md)
- [OpenAI gpt-5-mini with reasoning effort](references/openai_gpt_5_mini_with_reasoning_effort.md)
- [OpenAI GPT-4.1](references/openai_gpt_41.md)
- [OpenAI o4-mini with reasoning summary](references/openai_o4_mini_with_reasoning_summary.md)
- [xAI Grok 3 Mini](references/xai_grok_3_mini.md)
- [Azure OpenAI with Reasoning Tools](references/azure_openai_with_reasoning_tools.md)
- [Capture Reasoning Content with Knowledge Tools](references/capture_reasoning_content_with_knowledge_tools.md)
- [Capture Reasoning Content with Reasoning Tools](references/capture_reasoning_content_with_reasoning_tools.md)
- [Cerebras Llama with Reasoning Tools](references/cerebras_llama_with_reasoning_tools.md)
- [Claude with Reasoning Tools](references/claude_with_reasoning_tools.md)
- [Gemini with Reasoning Tools](references/gemini_with_reasoning_tools.md)
- [Groq with Reasoning Tools](references/groq_with_reasoning_tools.md)
- [Team with Knowledge Tools](references/team_with_knowledge_tools.md)
- [Reasoning Agent with Knowledge Tools](references/reasoning_agent_with_knowledge_tools.md)
- [Ollama with Reasoning Tools](references/ollama_with_reasoning_tools.md)
- [OpenAI with Reasoning Tools](references/openai_with_reasoning_tools.md)
- [Team with Reasoning Tools](references/team_with_reasoning_tools.md)
- [Reasoning Tools](references/reasoning_tools.md)
- [Vercel with Reasoning Tools](references/vercel_with_reasoning_tools.md)
- [AgentOS API Overview](references/agentos_api_overview.md) - Complete API reference for interacting with AgentOS programmatically
- [Get Agent Card](references/get_agent_card.md)
- [Get Team Card](references/get_team_card.md)
- [Get Workflow Card](references/get_workflow_card.md)
- [Run Message Agent](references/run_message_agent.md) - Send a message to an Agno Agent (non-streaming). The Agent is identified via the path parameter '{id}'. Optional: Pass user ID via X-User-ID header (recommended) or 'userId' in params.message.metadata.
- [Run Message Team](references/run_message_team.md) - Send a message to an Agno Team (non-streaming). The Team is identified via the path parameter '{id}'. Optional: Pass user ID via X-User-ID header (recommended) or 'userId' in params.message.metadata.
- [Run Message Workflow](references/run_message_workflow.md) - Send a message to an Agno Workflow (non-streaming). The Workflow is identified via the path parameter '{id}'. Optional: Pass user ID via X-User-ID header (recommended) or 'userId' in params.message.metadata.
- [Send Message](references/send_message.md) - [DEPRECATED] Send a message to an Agno Agent, Team, or Workflow. The Agent, Team or Workflow is identified via the 'agentId' field in params.message or X-Agent-ID header. Optional: Pass user ID via X-User-ID header (recommended) or 'userId' in params.message.metadata.
- [Stream Message](references/stream_message.md) - [DEPRECATED] Stream a message to an Agno Agent, Team, or Workflow. The Agent, Team or Workflow is identified via the 'agentId' field in params.message or X-Agent-ID header. Optional: Pass user ID via X-User-ID header (recommended) or 'userId' in params.message.metadata. Returns real-time updates as newline-delimited JSON (NDJSON).
- [Stream Message Agent](references/stream_message_agent.md) - Stream a message to an Agno Agent (streaming). The Agent is identified via the path parameter '{id}'. Optional: Pass user ID via X-User-ID header (recommended) or 'userId' in params.message.metadata. Returns real-time updates as newline-delimited JSON (NDJSON).
- [Stream Message Team](references/stream_message_team.md) - Stream a message to an Agno Team (streaming). The Team is identified via the path parameter '{id}'. Optional: Pass user ID via X-User-ID header (recommended) or 'userId' in params.message.metadata. Returns real-time updates as newline-delimited JSON (NDJSON).
- [Stream Message Workflow](references/stream_message_workflow.md) - Stream a message to an Agno Workflow (streaming). The Workflow is identified via the path parameter '{id}'. Optional: Pass user ID via X-User-ID header (recommended) or 'userId' in params.message.metadata. Returns real-time updates as newline-delimited JSON (NDJSON).
- [Cancel Agent Run](references/cancel_agent_run.md) - Cancel a currently executing agent run. This will attempt to stop the agent's execution gracefully.
- [Continue Agent Run](references/continue_agent_run.md) - Continue a paused or incomplete agent run with updated tool results.
- [Create Agent Run](references/create_agent_run.md) - Execute an agent with a message and optional media files. Supports both streaming and non-streaming responses.
- [Get Agent Details](references/get_agent_details.md) - Retrieve detailed configuration and capabilities of a specific agent.
- [List All Agents](references/list_all_agents.md) - Retrieve a comprehensive list of all agents configured in this OS instance.
- [Get Status](references/get_status.md)
- [Run Agent](references/run_agent.md)
- [Create Component](references/create_component.md) - Create a new component (agent, team, or workflow).
- [Create Config Version](references/create_config_version.md) - Create a new config version for a component.
- [Delete Component](references/delete_component.md) - Delete a component and all its config versions.
- [Delete Config Version](references/delete_config_version.md) - Delete a specific config version.
- [Get Component](references/get_component.md) - Get details of a specific component by ID.
- [Get Config Version](references/get_config_version.md) - Get a specific config version for a component.
- [Get Current Config](references/get_current_config.md) - Get the current (active) config version for a component.
- [List Components](references/list_components.md) - List all components with optional filtering and pagination.
- [List Config Versions](references/list_config_versions.md) - List all config versions for a component.
- [Set Current Config](references/set_current_config.md) - Set a specific config version as the current active version.
- [Update Component](references/update_component.md) - Update a component's metadata.
- [Update Config Version](references/update_config_version.md) - Update a specific config version.
- [Get OS Configuration](references/get_os_configuration.md) - Retrieve the complete configuration of the AgentOS instance, including:
- [Migrate All Databases](references/migrate_all_databases.md) - Migrate all database schemas to the given target version. If a target version is not provided, all databases will be migrated to the latest version.
- [Migrate Database](references/migrate_database.md) - Migrate the given database schema to the given target version. If a target version is not provided, the database will be migrated to the latest version.
- [Delete Evaluation Runs](references/delete_evaluation_runs.md) - Delete multiple evaluation runs by their IDs. This action cannot be undone.
- [Execute Evaluation](references/execute_evaluation.md) - Run evaluation tests on agents or teams. Supports accuracy, agent-as-judge, performance, and reliability evaluations. Requires either agent_id or team_id, but not both.
- [Get Evaluation Run](references/get_evaluation_run.md) - Retrieve detailed results and metrics for a specific evaluation run.
- [List Evaluation Runs](references/list_evaluation_runs.md) - Retrieve paginated evaluation runs with filtering and sorting options. Filter by agent, team, workflow, model, or evaluation type.
- [Update Evaluation Run](references/update_evaluation_run.md) - Update the name or other properties of an existing evaluation run.
- [Delete All Content](references/delete_all_content.md) - Permanently remove all content from the knowledge base. This is a destructive operation that cannot be undone. Use with extreme caution.
- [Delete Content by ID](references/delete_content_by_id.md) - Permanently remove a specific content item from the knowledge base. This action cannot be undone.
- [Get Config](references/get_config.md) - Retrieve available readers, chunkers, and configuration options for content processing. This endpoint provides metadata about supported file types, processing strategies, and filters.
- [Get Content by ID](references/get_content_by_id.md) - Retrieve detailed information about a specific content item including processing status and metadata.
- [Get Content Status](references/get_content_status.md) - Retrieve the current processing status of a content item. Useful for monitoring asynchronous content processing progress and identifying any processing errors.
- [List Content](references/list_content.md) - Retrieve paginated list of all content in the knowledge base with filtering and sorting options. Filter by status, content type, or metadata properties.
- [Search Knowledge](references/search_knowledge.md) - Search the knowledge base for relevant documents using query, filters and search type.
- [Update Content](references/update_content.md) - Update content properties such as name, description, metadata, or processing configuration. Allows modification of existing content without re-uploading.
- [Upload Content](references/upload_content.md) - Upload content to the knowledge base. Supports file uploads, text content, or URLs. Content is processed asynchronously in the background. Supports custom readers and chunking strategies.
- [Create Memory](references/create_memory.md) - Create a new user memory with content and associated topics. Memories are used to store contextual information for users across conversations.
- [Delete Memory](references/delete_memory.md) - Permanently delete a specific user memory. This action cannot be undone.
- [Delete Multiple Memories](references/delete_multiple_memories.md) - Delete multiple user memories by their IDs in a single operation. This action cannot be undone and all specified memories will be permanently removed.
- [Get Memory by ID](references/get_memory_by_id.md) - Retrieve detailed information about a specific user memory by its ID.
- [Get Memory Topics](references/get_memory_topics.md) - Retrieve all unique topics associated with memories in the system. Useful for filtering and categorizing memories by topic.
- [Get User Memory Statistics](references/get_user_memory_statistics.md) - Retrieve paginated statistics about memory usage by user. Provides insights into user engagement and memory distribution across users.
- [List Memories](references/list_memories.md) - Retrieve paginated list of user memories with filtering and search capabilities. Filter by user, agent, team, topics, or search within memory content.
- [Update Memory](references/update_memory.md) - Update an existing user memory's content and topics. Replaces the entire memory content and topic list with the provided values.
- [Get AgentOS Metrics](references/get_agentos_metrics.md) - Retrieve AgentOS metrics and analytics data for a specified date range. If no date range is specified, returns all available metrics.
- [Refresh Metrics](references/refresh_metrics.md) - Manually trigger recalculation of system metrics from raw data. This operation analyzes system activity logs and regenerates aggregated metrics. Useful for ensuring metrics are up-to-date or after system maintenance.
- [List Registry](references/list_registry.md) - List all registered components (tools, models, databases, schemas, functions) with filtering and pagination.
- [Create New Session](references/create_new_session.md) - Create a new empty session with optional configuration. Useful for pre-creating sessions with specific session_state, metadata, or other properties before running any agent/team/workflow interactions. The session can later be used by providing its session_id in run requests.
- [Delete Multiple Sessions](references/delete_multiple_sessions.md) - Delete multiple sessions by their IDs in a single operation. This action cannot be undone and will permanently remove all specified sessions and their runs.
- [Delete Session](references/delete_session.md) - Permanently delete a specific session and all its associated runs. This action cannot be undone and will remove all conversation history.
- [Get Run by ID](references/get_run_by_id.md) - Retrieve a specific run by its ID from a session. Response schema varies based on the run type (agent run, team run, or workflow run).
- [Get Session by ID](references/get_session_by_id.md) - Retrieve detailed information about a specific session including metadata, configuration, and run history. Response schema varies based on session type (agent, team, or workflow).
- [Get Session Runs](references/get_session_runs.md) - Retrieve all runs (executions) for a specific session with optional timestamp filtering. Runs represent individual interactions or executions within a session. Response schema varies based on session type.
- [List Sessions](references/list_sessions.md) - Retrieve paginated list of sessions with filtering and sorting options. Supports filtering by session type (agent, team, workflow), component, user, and name. Sessions represent conversation histories and execution contexts.
- [Rename Session](references/rename_session.md) - Update the name of an existing session. Useful for organizing and categorizing sessions with meaningful names for better identification and management.
- [Update Session](references/update_session.md) - Update session properties such as session_name, session_state, metadata, or summary. Use this endpoint to modify the session name, update state, add metadata, or update the session summary.
- [Slack Events](references/slack_events.md) - Process incoming Slack events
- [Cancel Team Run](references/cancel_team_run.md) - Cancel a currently executing team run. This will attempt to stop the team's execution gracefully.
- [Create Team Run](references/create_team_run.md) - Execute a team collaboration with multiple agents working together on a task.
- [Get Team Details](references/get_team_details.md) - Retrieve detailed configuration and member information for a specific team.
- [List All Teams](references/list_all_teams.md) - Retrieve a comprehensive list of all teams configured in this OS instance.
- [Get Trace or Span Detail](references/get_trace_or_span_detail.md) - Retrieve detailed trace information with hierarchical span tree, or a specific span within the trace.
- [Get Trace Statistics by Session](references/get_trace_statistics_by_session.md) - Retrieve aggregated trace statistics grouped by session ID with pagination.
- [List Traces](references/list_traces.md) - Retrieve a paginated list of execution traces with optional filtering.
- [Status](references/status.md)
- [Verify Webhook](references/verify_webhook.md) - Handle WhatsApp webhook verification
- [Webhook](references/webhook.md) - Handle incoming WhatsApp messages
- [Cancel Workflow Run](references/cancel_workflow_run.md) - Cancel a currently executing workflow run, stopping all active steps and cleanup.
- [Execute Workflow](references/execute_workflow.md) - Execute a workflow with the provided input data. Workflows can run in streaming or batch mode.
- [Get Workflow Details](references/get_workflow_details.md) - Retrieve detailed configuration and step information for a specific workflow.
- [List All Workflows](references/list_all_workflows.md) - Retrieve a comprehensive list of all workflows configured in this OS instance.
- [AgentOS](references/agentos.md)
- [AuthorizationConfig](references/authorizationconfig.md)
- [AgentOSConfig](references/agentosconfig.md)
- [JWTMiddleware](references/jwtmiddleware.md)
- [Agent](references/agent.md)
- [RemoteAgent](references/remoteagent.md) - Execute agents hosted on a remote AgentOS instance
- [RunOutput](references/runoutput.md)
- [AgentSession](references/agentsession.md)
- [ag infra config](references/ag_infra_config.md)
- [ag infra create](references/ag_infra_create.md)
- [ag infra delete](references/ag_infra_delete.md)
- [ag infra down](references/ag_infra_down.md)
- [ag infra patch](references/ag_infra_patch.md)
- [ag infra restart](references/ag_infra_restart.md)
- [ag infra up](references/ag_infra_up.md)
- [A2AClient](references/a2aclient.md) - Python client for communicating with A2A-compatible agent servers
- [AgentOSClient](references/agentosclient.md) - Python client for interacting with AgentOS API endpoints
- [CompressionManager](references/compressionmanager.md)
- [BaseGuardrail](references/baseguardrail.md)
- [OpenAIModerationGuardrail](references/openaimoderationguardrail.md)
- [PIIDetectionGuardrail](references/piidetectionguardrail.md)
- [Post-hooks](references/post_hooks.md)
- [Pre-hooks](references/pre_hooks.md)
- [PromptInjectionGuardrail](references/promptinjectionguardrail.md)
- [Agentic Chunking](references/agentic_chunking.md)
- [Code Chunking](references/code_chunking.md)
- [CSV Row Chunking](references/csv_row_chunking.md)
- [Document Chunking](references/document_chunking.md)
- [Fixed Size Chunking](references/fixed_size_chunking.md)
- [Markdown Chunking](references/markdown_chunking.md)
- [Recursive Chunking](references/recursive_chunking.md)
- [Semantic Chunking](references/semantic_chunking.md)
- [Azure OpenAI](references/azure_openai.md)
- [Cohere](references/cohere.md)
- [FastEmbed](references/fastembed.md)
- [Fireworks](references/fireworks.md)
- [Gemini](references/gemini.md)
- [Hugging Face](references/hugging_face.md)
- [Mistral](references/mistral.md)
- [Nebius](references/nebius.md)
- [Ollama](references/ollama.md)
- [OpenAI](references/openai.md)
- [Sentence Transformer](references/sentence_transformer.md)
- [Together](references/together.md)
- [vLLM](references/vllm.md)
- [VoyageAI](references/voyageai.md)
- [Knowledge](references/knowledge.md)
- [Arxiv Reader](references/arxiv_reader.md)
- [Reader](references/reader.md)
- [CSV Reader](references/csv_reader.md)
- [Docx Reader](references/docx_reader.md)
- [Field Labeled CSV Reader](references/field_labeled_csv_reader.md)
- [FireCrawl Reader](references/firecrawl_reader.md)
- [JSON Reader](references/json_reader.md)
- [PDF Reader](references/pdf_reader.md)
- [PPTX Reader](references/pptx_reader.md)
- [Text Reader](references/text_reader.md)
- [Web Search Reader](references/web_search_reader.md)
- [Website Reader](references/website_reader.md)
- [Wikipedia Reader](references/wikipedia_reader.md)
- [YouTube Reader](references/youtube_reader.md)
- [GCS Content](references/gcs_content.md)
- [S3 Content](references/s3_content.md)
- [Cohere Reranker](references/cohere_reranker.md)
- [Memory Manager](references/memory_manager.md)
- [AI/ML API](references/aiml_api.md)
- [Claude](references/claude.md)
- [Azure AI Foundry](references/azure_ai_foundry.md)
- [Azure OpenAI](references/azure_openai.md)
- [AWS Bedrock](references/aws_bedrock.md) - AWS Bedrock model parameters and configuration.
- [AWS Bedrock Claude](references/aws_bedrock_claude.md)
- [Cohere](references/cohere.md)
- [DeepInfra](references/deepinfra.md)
- [DeepSeek](references/deepseek.md)
- [Fireworks](references/fireworks.md)
- [Gemini](references/gemini.md)
- [Groq](references/groq.md)
- [HuggingFace](references/huggingface.md)
- [IBM WatsonX](references/ibm_watsonx.md)
- [InternLM](references/internlm.md)
- [Meta](references/meta.md)
- [Mistral](references/mistral.md)
- [Model](references/model.md)
- [N1N](references/n1n.md)
- [Nebius](references/nebius.md)
- [Nvidia](references/nvidia.md)
- [Ollama](references/ollama.md)
- [Ollama Responses](references/ollama_responses.md)
- [Open Responses](references/open_responses.md)
- [OpenAI](references/openai.md)
- [OpenAI Like](references/openai_like.md)
- [OpenRouter](references/openrouter.md)
- [OpenRouter Responses](references/openrouter_responses.md)
- [Perplexity](references/perplexity.md)
- [Requesty](references/requesty.md)
- [Sambanova](references/sambanova.md)
- [Together](references/together.md)
- [Vercel v0](references/vercel_v0.md)
- [xAI](references/xai.md)
- [RunContext](references/runcontext.md)
- [SessionSummaryManager](references/sessionsummarymanager.md)
- [DynamoDB](references/dynamodb.md)
- [FirestoreDb](references/firestoredb.md)
- [GcsJsonDb](references/gcsjsondb.md)
- [InMemoryDb](references/inmemorydb.md)
- [JsonDb](references/jsondb.md)
- [MigrationManager](references/migrationmanager.md) - API reference for the MigrationManager class used to handle database migrations.
- [MongoDB](references/mongodb.md)
- [MySQLDb](references/mysqldb.md)
- [PostgresDb](references/postgresdb.md)
- [RedisDb](references/redisdb.md)
- [SingleStoreDb](references/singlestoredb.md)
- [SqliteDb](references/sqlitedb.md)
- [SurrealDb](references/surrealdb.md)
- [RemoteTeam](references/remoteteam.md) - Execute teams hosted on a remote AgentOS instance
- [Team Session](references/team_session.md)
- [Team](references/team.md)
- [TeamRunOutput](references/teamrunoutput.md)
- [Tool Decorator](references/tool_decorator.md) - Reference for the @tool decorator.
- [RetryAgentRun](references/retryagentrun.md) - API reference for the RetryAgentRun exception used to provide feedback to the model within the tool call loop.
- [StopAgentRun](references/stopagentrun.md) - API reference for the StopAgentRun exception used to exit the tool call loop and complete the agent run.
- [Toolkit](references/toolkit.md) - Reference for the Toolkit class.
- [Span](references/span.md)
- [Trace](references/trace.md)
- [Conditional Steps](references/conditional_steps.md)
- [Loop Steps](references/loop_steps.md)
- [Parallel Steps](references/parallel_steps.md)
- [RemoteWorkflow](references/remoteworkflow.md) - Execute workflows hosted on a remote AgentOS instance
- [Router Steps](references/router_steps.md)
- [WorkflowRunOutput](references/workflowrunoutput.md)
- [WorkflowSession](references/workflowsession.md)
- [Step](references/step.md)
- [StepInput](references/stepinput.md)
- [StepOutput](references/stepoutput.md)
- [Steps](references/steps.md)
- [Workflow](references/workflow.md)
- [Agent Run Cancellation](references/agent_run_cancellation.md) - Cancel a running agent execution from another thread.
- [Cancelling a Run](references/cancelling_a_run.md) - Cancel running agent, team, or workflow executions.
- [Team Run Cancellation](references/team_run_cancellation.md) - Cancel a running team execution from another thread.
- [Workflow Run Cancellation](references/workflow_run_cancellation.md) - Cancel a running workflow execution from another thread.
- [History Management](references/history_management.md) - Control how conversation history is accessed and used
- [Agent Metrics](references/agent_metrics.md) - Agent run and session metrics for token usage and performance.
- [Metrics](references/metrics.md) - Run and session metrics for token usage, duration, and performance.
- [Team Metrics](references/team_metrics.md) - Team run and session metrics for token usage and performance.
- [Agent Extra Metrics](references/agent_extra_metrics.md)
- [Agent Metrics and Performance Monitoring](references/agent_metrics_and_performance_monitoring.md)
- [Team Metrics Analysis](references/team_metrics_analysis.md)
- [Workflow Metrics](references/workflow_metrics.md) - Workflow run and session metrics for token usage and performance.
- [Sessions](references/sessions.md) - Multi-turn conversation threads with persistent history and state.
- [Persisting Sessions](references/persisting_sessions.md) - Store session data in a database for multi-turn conversations
- [Storage Control](references/storage_control.md) - Control what session data gets persisted to your database
- [Session Management](references/session_management.md) - Manage session identifiers, names, and performance optimization
- [Session Summaries](references/session_summaries.md) - Automatically condense long conversations into concise summaries
- [Workflow Sessions](references/workflow_sessions.md) - Track multi-step workflow executions with session history.
- [Creating Skills](references/creating_skills.md) - Create skills with instructions, scripts, and reference documentation.
- [Loading Skills](references/loading_skills.md) - Load skills into agents using LocalSkills and the Skills orchestrator.
- [Introduction to Skills](references/introduction_to_skills.md) - Skills provide agents with structured domain expertise through instructions, scripts, and reference documentation.
- [Agentic State](references/agentic_state.md)
- [Change State on Run](references/change_state_on_run.md)
- [Dynamic State](references/dynamic_state.md)
- [Last N Messages](references/last_n_messages.md)
- [Agent Session State](references/agent_session_state.md) - Manage persistent state in agents across multiple runs within a session
- [Advanced State](references/advanced_state.md)
- [Basic State](references/basic_state.md)
- [State in Context](references/state_in_context.md)
- [State in Instructions](references/state_in_instructions.md)
- [Multiple Users](references/multiple_users.md)
- [State Management](references/state_management.md) - Persist and share data across agent runs, team coordination, and workflow execution
- [Agentic State](references/agentic_state.md)
- [Change State on Run](references/change_state_on_run.md)
- [Team Session State](references/team_session_state.md) - Share and coordinate state across multiple agents in a team
- [State in Instructions](references/state_in_instructions.md)
- [Share Member Interactions](references/share_member_interactions.md)
- [State in Condition](references/state_in_condition.md) - This example demonstrates how to access the run context in the evaluator function of a condition step
- [State in Custom Function](references/state_in_custom_function.md) - This example demonstrates how to access the run context in a custom python function step
- [State in Router](references/state_in_router.md) - This example demonstrates how to access the run context in the selector function of a router step
- [Workflow Session State](references/workflow_session_state.md) - Coordinate state across workflow steps, agents, teams, and custom functions
- [Building Teams](references/building_teams.md) - Define team members, roles, and structure for multi-agent coordination.
- [Debugging Teams](references/debugging_teams.md) - Troubleshoot and inspect team behavior with debug mode, tracing, and common failure patterns.
- [Delegation](references/delegation.md) - Control how the team leader delegates tasks to members.
- [What are Teams?](references/what_are_teams.md) - Groups of agents that collaborate to solve complex tasks.
- [Running Teams](references/running_teams.md) - Execute teams with Team.run() and process their output.
- [Basic Team](references/basic_team.md) - A team of AI agents working together to research topics.
- [Direct Response Mode](references/direct_response_mode.md) - Route requests to specialized agents who respond directly.
- [Team Streaming](references/team_streaming.md) - Stream responses from a team in real-time.
- [Agno Telemetry](references/agno_telemetry.md) - Control what usage data Agno collects
- [Agent Tools](references/agent_tools.md) - Equip agents with functions and toolkits for external actions.
- [Updating an Agent's Tools](references/updating_an_agents_tools.md) - Add or update tools on Agents and Teams after initialization.
- [Tool Result Caching](references/tool_result_caching.md) - Cache tool results to reduce repeated API calls and improve performance.
- [Creating your own tools](references/creating_your_own_tools.md) - Write custom tool functions and use the `@tool` decorator to modify tool behavior.
- [Python Functions as Tools](references/python_functions_as_tools.md) - Turn any Python function into an agent tool.
- [Custom Toolkits](references/custom_toolkits.md) - Bundle related tool functions into reusable toolkit classes.
- [Exceptions & Retries](references/exceptions__retries.md) - Handle tool errors with exceptions and automatic retries.
- [Tool Hooks](references/tool_hooks.md) - Use pre and post hooks to modify tool behavior.
- [Dynamic Headers](references/dynamic_headers.md) - Setting dynamic headers with Agno MCP tools
- [MCP Toolbox](references/mcp_toolbox.md) - Connect to MCP Toolbox for Databases with tool filtering capabilities.
- [Multiple MCP Servers](references/multiple_mcp_servers.md) - Understanding how to connect to multiple MCP servers with Agno
- [Model Context Protocol (MCP)](references/model_context_protocol_mcp.md) - Connect agents to external systems through the standardized MCP interface.
- [Understanding Server Parameters](references/understanding_server_parameters.md) - Understanding how to configure the server parameters for the MCPTools and MultiMCPTools classes
- [SSE Transport](references/sse_transport.md)
- [Stdio Transport](references/stdio_transport.md)
- [Streamable HTTP Transport](references/streamable_http_transport.md)
- [Airbnb MCP agent](references/airbnb_mcp_agent.md)
- [GitHub MCP agent](references/github_mcp_agent.md)
- [Notion MCP agent](references/notion_mcp_agent.md)
- [Parallel MCP agent](references/parallel_mcp_agent.md)
- [Pipedream Auth](references/pipedream_auth.md) - This example shows how to add authorization when integrating Pipedream MCP servers with Agno Agents.
- [Pipedream Google Calendar](references/pipedream_google_calendar.md) - This example shows how to use the Google Calendar Pipedream MCP server with Agno Agents.
- [Pipedream LinkedIn](references/pipedream_linkedin.md) - This example shows how to use the LinkedIn Pipedream MCP server with Agno Agents.
- [Pipedream Slack](references/pipedream_slack.md) - This example shows how to use the Slack Pipedream MCP server with Agno Agents.
- [Stagehand MCP agent](references/stagehand_mcp_agent.md) - A web scraping agent that uses the Stagehand MCP server to automate browser interactions and create a structured content digest from Hacker News.
- [Stripe MCP agent](references/stripe_mcp_agent.md)
- [Supabase MCP agent](references/supabase_mcp_agent.md)
- [What are Tools?](references/what_are_tools.md) - Tools are functions Agents call to interact with external systems.
- [Knowledge Tools](references/knowledge_tools.md)
- [Memory Tools](references/memory_tools.md)
- [Reasoning Tools](references/reasoning_tools.md)
- [Workflow Tools](references/workflow_tools.md)
- [Including and excluding tools](references/including_and_excluding_tools.md) - Include and exclude specific tools from a Toolkit.
- [Tool Call Limit](references/tool_call_limit.md) - Limit the number of tool calls an agent can make.
- [CSV](references/csv.md) - The CsvTools toolkit enables an Agent to read and write CSV files.
- [DuckDb](references/duckdb.md) - The DuckDbTools toolkit enables an Agent to run SQL and analyze data using DuckDb.
- [Google BigQuery](references/google_bigquery.md) - GoogleBigQueryTools enables agents to interact with Google BigQuery for large-scale data analysis and SQL queries.
- [Neo4j](references/neo4j.md) - The Neo4jTools toolkit enables agents to interact with Neo4j graph databases for querying and managing graph data.
- [Pandas](references/pandas.md) - The PandasTools toolkit enables an Agent to perform data manipulation tasks using the Pandas library.
- [Postgres](references/postgres.md) - The PostgresTools toolkit enables an Agent to interact with a PostgreSQL database.
- [Redshift](references/redshift.md) - The RedshiftTools toolkit enables an Agent to interact with Amazon Redshift data warehouses.
- [SQL](references/sql.md) - The SQLTools toolkit enables an Agent to run SQL queries and interact with databases.
- [Zep](references/zep.md) - The ZepTools toolkit enables an Agent to interact with a Zep memory system, providing capabilities to store, retrieve, and search memory data associated with user sessions.
- [File Generation](references/file_generation.md) - The `FileGenerationTools` toolkit enables Agents and Teams to generate files in multiple formats.
- [Calculator](references/calculator.md)
- [Docker](references/docker.md)
- [File](references/file.md) - The FileTools toolkit enables Agents to read and write files on the local file system.
- [Local File System](references/local_file_system.md) - LocalFileSystemTools enables agents to write files to the local file system with automatic directory management.
- [Python](references/python.md)
- [Shell](references/shell.md)
- [Sleep](references/sleep.md)
- [Azure OpenAI](references/azure_openai.md) - AzureOpenAITools provides access to Azure OpenAI services including DALL-E image generation.
- [Gemini](references/gemini.md)
- [Groq](references/groq.md)
- [Morph](references/morph.md) - MorphTools provides advanced code editing capabilities using Morph's Fast Apply API for intelligent code modifications.
- [Nebius](references/nebius.md) - NebiusTools provides access to Nebius Token Factory's text-to-image generation capabilities with advanced AI models.
- [OpenAI](references/openai.md)
- [Airflow](references/airflow.md)
- [Apify](references/apify.md)
- [AWS Lambda](references/aws_lambda.md)
- [AWS SES](references/aws_ses.md)
- [Bitbucket](references/bitbucket.md) - BitbucketTools enables agents to interact with Bitbucket repositories for managing code, pull requests, and issues.
- [Brandfetch](references/brandfetch.md) - BrandfetchTools provides access to brand data and logo information through the Brandfetch API.
- [Cal.com](references/calcom.md)
- [Cartesia](references/cartesia.md) - Tools for interacting with Cartesia Voice AI services including text-to-speech and voice localization
- [ClickUp](references/clickup.md) - ClickUpTools enables agents to interact with ClickUp workspaces for project management and task organization.
- [Composio](references/composio.md)
- [Confluence](references/confluence.md)
- [Custom API](references/custom_api.md)
- [Dalle](references/dalle.md)
- [Daytona](references/daytona.md) - Enable your Agents to run code in a remote, secure sandbox.
- [Desi Vocal](references/desi_vocal.md) - DesiVocalTools provides text-to-speech capabilities using Indian voices through the Desi Vocal API.
- [E2B](references/e2b.md) - Enable your Agents to run code in a remote, secure sandbox.
- [Eleven Labs](references/eleven_labs.md)
- [EVM (Ethereum Virtual Machine)](references/evm_ethereum_virtual_machine.md) - EvmTools enables agents to interact with Ethereum and EVM-compatible blockchains for transactions and smart contract operations.
- [Fal](references/fal.md)
- [Financial Datasets API](references/financial_datasets_api.md)
- [Giphy](references/giphy.md)
- [Github](references/github.md)
- [Google Maps](references/google_maps.md) - Tools for interacting with Google Maps services including place search, directions, geocoding, and more
- [Google Sheets](references/google_sheets.md)
- [Google Calendar](references/google_calendar.md)
- [Jira](references/jira.md)
- [Knowledge Tools](references/knowledge_tools.md) - KnowledgeTools provide intelligent search and analysis capabilities over knowledge bases with reasoning integration.
- [Linear](references/linear.md)
- [Lumalabs](references/lumalabs.md)
- [Mem0](references/mem0.md) - Mem0Tools provides intelligent memory management capabilities for agents using the Mem0 memory platform.
- [MLX Transcribe](references/mlx_transcribe.md)
- [ModelsLabs](references/modelslabs.md)
- [MoviePy Video Tools](references/moviepy_video_tools.md) - Agno MoviePyVideoTools enable an Agent to process videos, extract audio, generate SRT caption files, and embed rich, word-highlighted captions.
- [Nano Banana](references/nano_banana.md)
- [Notion Tools](references/notion_tools.md) - The NotionTools toolkit enables Agents to interact with your Notion pages.
- [OpenBB](references/openbb.md)
- [OpenCV](references/opencv.md) - OpenCVTools enables agents to capture images and videos from webcam using OpenCV computer vision library.
- [OpenWeather](references/openweather.md)
- [Reasoning](references/reasoning.md) - ReasoningTools provides step-by-step reasoning capabilities for agents to think through complex problems systematically.
- [Replicate](references/replicate.md)
- [Resend](references/resend.md)
- [Shopify](references/shopify.md) - Tools to interact with Shopify.
- [Spotify](references/spotify.md)
- [Todoist](references/todoist.md)
- [Trello](references/trello.md) - Agno TrelloTools helps to integrate Trello functionalities into your agents, enabling management of boards, lists, and cards.
- [User Control Flow](references/user_control_flow.md) - UserControlFlowTools enable agents to pause execution and request input from users during conversations.
- [Visualization](references/visualization.md) - VisualizationTools enables agents to create various types of charts and plots using matplotlib.
- [Web Browser Tools](references/web_browser_tools.md) - WebBrowser Tools enable an Agent to open a URL in a web browser.
- [Web Tools](references/web_tools.md) - WebTools provides utilities for working with web URLs including URL expansion and web-related operations.
- [Yfinance](references/yfinance.md)
- [Youtube](references/youtube.md)
- [Zendesk](references/zendesk.md)
- [Toolkit Index](references/toolkit_index.md) - Index of all toolkits supported by Agno.
- [Arxiv](references/arxiv.md)
- [BaiduSearch](references/baidusearch.md)
- [Brave Search](references/brave_search.md)
- [DuckDuckGo](references/duckduckgo.md)
- [Exa](references/exa.md)
- [Hacker News](references/hacker_news.md)
- [Linkup](references/linkup.md) - LinkupTools provides advanced web search capabilities with deep search options and structured results.
- [Parallel](references/parallel.md) - Use Parallel with Agno for AI-optimized web search and content extraction.
- [Pubmed](references/pubmed.md)
- [Searxng](references/searxng.md)
- [Serpapi](references/serpapi.md)
- [SerperApi](references/serperapi.md)
- [Tavily](references/tavily.md)
- [Valyu](references/valyu.md) - ValyuTools provides academic and web search capabilities with advanced filtering and relevance scoring.
- [Web Search](references/web_search.md)
- [Wikipedia](references/wikipedia.md)
- [Discord](references/discord.md)
- [Email](references/email.md)
- [Gmail](references/gmail.md)
- [Reddit](references/reddit.md) - RedditTools enables agents to interact with Reddit for browsing posts, comments, and subreddit information.
- [Slack](references/slack.md)
- [Telegram](references/telegram.md)
- [Twilio](references/twilio.md)
- [Webex](references/webex.md)
- [WhatsApp](references/whatsapp.md)
- [X (Twitter)](references/x_twitter.md)
- [Zoom](references/zoom.md)
- [AgentQL](references/agentql.md)
- [BrightData](references/brightdata.md)
- [Browserbase](references/browserbase.md)
- [Crawl4AI](references/crawl4ai.md)
- [Firecrawl](references/firecrawl.md) - Use Firecrawl with Agno to scrape and crawl the web.
- [Jina Reader](references/jina_reader.md)
- [Newspaper](references/newspaper.md)
- [Newspaper4k](references/newspaper4k.md)
- [Oxylabs](references/oxylabs.md)
- [ScrapeGraph](references/scrapegraph.md) - ScrapeGraphTools enable an Agent to extract structured data from webpages, convert content to markdown, and retrieve raw HTML content.
- [Spider](references/spider.md)
- [Trafilatura](references/trafilatura.md) - TrafilaturaTools provides advanced web scraping and text extraction capabilities with support for crawling and content analysis.
- [Website Tools](references/website_tools.md)
- [Async Team with Tools](references/async_team_with_tools.md)
- [Team with Custom Tools](references/team_with_custom_tools.md)
- [Team with Tool Hooks](references/team_with_tool_hooks.md)
- [Basic Setup](references/basic_setup.md) - Configure and enable tracing for your Agno agents
- [Accessing your Traces](references/accessing_your_traces.md) - Database convenience functions for querying traces and spans
- [Tracing](references/tracing.md) - Gain deep visibility into your Agno agents with OpenTelemetry-based observability
- [Basic Agent Tracing](references/basic_agent_tracing.md) - Enable tracing and observability for agents.
- [Basic Team Tracing](references/basic_team_tracing.md) - Enable tracing and observability for teams.
- [Basic Workflow Tracing](references/basic_workflow_tracing.md) - Enable tracing and observability for workflows.
- [Accessing Multiple Previous Steps](references/accessing_multiple_previous_steps.md) - How to access multiple previous steps
- [Additional Data and Metadata](references/additional_data_and_metadata.md) - How to pass additional data to workflows
- [Background Workflow Execution](references/background_workflow_execution.md) - How to execute workflows as non-blocking background tasks
- [Building Workflows](references/building_workflows.md) - Define steps, loops, conditions, and parallel execution in workflows.
- [Conversational Workflows](references/conversational_workflows.md) - Build multi-turn conversational workflows in Agno.
- [Early Stopping](references/early_stopping.md) - How to early stop workflows
- [What are Workflows?](references/what_are_workflows.md) - Workflows orchestrate agents, teams, and functions through defined steps for repeatable tasks.
- [Running Workflows](references/running_workflows.md) - Execute workflows with Workflow.run() and process their output.
- [Access Multiple Previous Steps Output](references/access_multiple_previous_steps_output.md) - This example demonstrates **Workflows 2.0** advanced data flow capabilities
- [Async Events Streaming](references/async_events_streaming.md) - This example demonstrates how to stream events from a workflow.
- [Basic Conversational Workflow](references/basic_conversational_workflow.md) - This example demonstrates a basic conversational workflow with a WorkflowAgent.
- [Class-based Executor](references/class_based_executor.md) - This example demonstrates how to use a class-based executor in a workflow.
- [Condition and Parallel Steps Workflow](references/condition_and_parallel_steps_workflow.md) - This example demonstrates **Workflows 2.0** advanced pattern combining conditional execution with parallel processing.
- [Conditional Workflow](references/conditional_workflow.md) - Execute steps based on content analysis or business logic.
- [Condition with list of steps](references/condition_with_list_of_steps.md) - This example demonstrates how to use conditional step to execute multiple steps in parallel.
- [Early Stop a Workflow](references/early_stop_a_workflow.md) - This example demonstrates **Workflows 2.0** early termination of a running workflow.
- [Function instead of steps](references/function_instead_of_steps.md) - This example demonstrates how to use just a single function instead of steps in a workflow.
- [Loop Steps Workflow](references/loop_steps_workflow.md) - This example demonstrates **Workflows 2.0** loop execution for quality-driven iterative processes.
- [Loop with Parallel Steps Workflow](references/loop_with_parallel_steps_workflow.md) - This example demonstrates **Workflows 2.0** most sophisticated pattern combining loop execution with parallel processing and real-time streaming.
- [Parallel Workflow](references/parallel_workflow.md) - Execute independent tasks simultaneously to reduce total execution time.
- [Conditional Branching Workflow](references/conditional_branching_workflow.md) - This example demonstrates **Workflows 2.0** router pattern for intelligent, content-based workflow routing.
- [Router with Loop Steps](references/router_with_loop_steps.md) - This example demonstrates **Workflows 2.0** advanced pattern combining Router-based intelligent path selection with Loop execution for iterative quality improvement.
- [Router with Step Choices](references/router_with_step_choices.md) - Router step_choices parameter and string, Step, and List[Step] returns.
- [Sequential Workflow](references/sequential_workflow.md) - Use named steps for sequential execution with clear tracking.
- [Step with function](references/step_with_function.md) - This example demonstrates how to use named steps with custom function executors.
- [Step with Function using Additional Data](references/step_with_function_using_additional_data.md) - This example demonstrates **Workflows 2.0** support for passing metadata and contextual information to steps via `additional_data`.
- [Step with custom function streaming on AgentOS](references/step_with_custom_function_streaming_on_agentos.md) - This example demonstrates how to use named steps with custom function executors and streaming on AgentOS.
- [Store Events and Events to Skip in a Workflow](references/store_events_and_events_to_skip_in_a_workflow.md) - This example demonstrates **Workflows 2.0** event storage capabilities
- [Structured I/O](references/structured_io.md) - Type-safe data flow between agents, teams, and custom functions using Pydantic models.
- [Workflow Cancellation](references/workflow_cancellation.md) - This example demonstrates **Workflows 2.0** support for cancelling running workflow executions, including thread-based cancellation and handling cancelled responses.
- [Workflow with Input Schema Validation](references/workflow_with_input_schema_validation.md) - This example demonstrates **Workflows** support for input schema validation using Pydantic models to ensure type safety and data integrity at the workflow entry point.
- [Advanced Workflow Patterns](references/advanced_workflow_patterns.md) - Combine multiple workflow patterns to build sophisticated, production-ready automation systems
- [Branching Workflow](references/branching_workflow.md) - Complex decision trees requiring dynamic path selection based on content analysis
- [Conditional Workflow](references/conditional_workflow.md) - Deterministic branching based on input analysis or business rules
- [Custom Functions in Workflows](references/custom_functions_in_workflows.md) - How to use custom functions in workflows
- [Fully Python Workflow](references/fully_python_workflow.md) - Keep it Simple with Pure Python, in v1 workflows style
- [Grouped Steps Workflow](references/grouped_steps_workflow.md) - Organize multiple steps into reusable, logical sequences for complex workflows with clean separation of concerns
- [Iterative Workflow](references/iterative_workflow.md) - Quality-driven processes requiring repetition until specific conditions are met
- [Workflow Patterns](references/workflow_patterns.md) - Master deterministic workflow patterns including sequential, parallel, conditional, and looping execution for reliable multi-agent automation.
- [Parallel Workflow](references/parallel_workflow.md) - Independent, concurrent tasks that can execute simultaneously for improved efficiency
- [Sequential Workflows](references/sequential_workflows.md) - Linear, deterministic processes where each step depends on the output of the previous step.
- [Step-Based Workflows](references/step_based_workflows.md) - Named steps for better logging and support on the AgentOS chat page
- [Workflow Tools](references/workflow_tools.md) - How to execute a workflow inside an Agent or Team