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docs: add AgentField to Agent SDKs integrations (#22901)
* docs: add AgentField to integrations index * docs: add AgentField tutorial page * docs: add AgentField to Agent SDKs sidebar
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This section covers integrations with various tools and services that can be used with LiteLLM (either Proxy or SDK).
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## AI Agent Frameworks
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- **[AgentField](../tutorials/agentfield.md)** - Open-source control plane for building and orchestrating autonomous AI agents
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- **[Letta](./letta.md)** - Build stateful LLM agents with persistent memory using LiteLLM Proxy
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## Development Tools
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@ -15,4 +16,4 @@ This section covers integrations with various tools and services that can be use
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- **[Datadog](../observability/datadog.md)**
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Click into each section to learn more about the integrations.
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Click into each section to learn more about the integrations.
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docs/my-website/docs/tutorials/agentfield.md
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docs/my-website/docs/tutorials/agentfield.md
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import Tabs from '@theme/Tabs';
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import TabItem from '@theme/TabItem';
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# AgentField with LiteLLM
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Use [AgentField](https://agentfield.ai) with any LLM provider through LiteLLM.
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AgentField is an open-source control plane for building and orchestrating autonomous AI agents, with SDKs for Python, TypeScript, and Go. AgentField's Python SDK uses LiteLLM internally for multi-provider LLM support.
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## Overview
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AgentField's Python SDK uses `litellm.acompletion()` under the hood, giving you access to 100+ LLM providers out of the box:
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- Use any LiteLLM-supported model (OpenAI, Anthropic, Azure, Bedrock, Ollama, etc.)
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- Switch between providers by changing the model string
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- All LiteLLM features (caching, fallbacks, routing) work automatically
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## Prerequisites
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- Python 3.9+
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- API keys for your LLM providers
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- AgentField control plane (optional, for orchestration features)
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## Installation
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```bash
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pip install agentfield
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```
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## Quick Start
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### Basic Agent with OpenAI
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```python
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from agentfield import Agent, AgentConfig
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config = AgentConfig(
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name="my-agent",
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model="gpt-4o", # Any LiteLLM-supported model
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instructions="You are a helpful assistant."
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)
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agent = Agent(config)
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response = await agent.run("Hello, world!")
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```
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### Using Anthropic
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```python
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config = AgentConfig(
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name="claude-agent",
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model="anthropic/claude-sonnet-4-20250514", # LiteLLM model format
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instructions="You are a helpful assistant."
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)
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```
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### Using Ollama (Local Models)
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```python
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config = AgentConfig(
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name="local-agent",
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model="ollama/llama3.1", # LiteLLM's ollama/ prefix
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instructions="You are a helpful assistant."
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)
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```
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### Using Azure OpenAI
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```python
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config = AgentConfig(
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name="azure-agent",
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model="azure/gpt-4o", # LiteLLM's azure/ prefix
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instructions="You are a helpful assistant."
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)
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```
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### Using with LiteLLM Proxy
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Point AgentField to a LiteLLM Proxy for centralized model management:
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```python
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import os
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os.environ["OPENAI_API_BASE"] = "http://0.0.0.0:4000" # LiteLLM Proxy URL
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os.environ["OPENAI_API_KEY"] = "sk-1234" # LiteLLM Proxy key
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config = AgentConfig(
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name="proxy-agent",
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model="gpt-4o", # Virtual model name from proxy config
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instructions="You are a helpful assistant."
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)
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```
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## Multi-Agent Orchestration
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AgentField's control plane orchestrates multiple agents, each potentially using different LLM providers:
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```python
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from agentfield import Agent, AgentConfig, ControlPlane
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# Create agents with different providers
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researcher = Agent(AgentConfig(
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name="researcher",
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model="anthropic/claude-sonnet-4-20250514",
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instructions="You research topics thoroughly."
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))
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writer = Agent(AgentConfig(
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name="writer",
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model="gpt-4o",
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instructions="You write clear, concise content."
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))
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# Register with control plane
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cp = ControlPlane(server="http://localhost:8080")
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cp.register(researcher)
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cp.register(writer)
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```
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## Links
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- [Documentation](https://agentfield.ai/docs)
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- [GitHub](https://github.com/Agent-Field/agentfield)
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- [Python SDK](https://github.com/Agent-Field/agentfield/tree/main/sdk/python)
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@ -184,6 +184,7 @@ const sidebars = {
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slug: "/agent_sdks"
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},
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items: [
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"tutorials/agentfield",
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"tutorials/openai_agents_sdk",
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"tutorials/claude_agent_sdk",
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"tutorials/copilotkit_sdk",
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