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).
## AI Agent Frameworks
- **[AgentField](../tutorials/agentfield.md)** - Open-source control plane for building and orchestrating autonomous AI agents
- **[Letta](./letta.md)** - Build stateful LLM agents with persistent memory using LiteLLM Proxy
## Development Tools
@ -15,4 +16,4 @@ This section covers integrations with various tools and services that can be use
- **[Datadog](../observability/datadog.md)**
Click into each section to learn more about the integrations.
Click into each section to learn more about the integrations.

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import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
# AgentField with LiteLLM
Use [AgentField](https://agentfield.ai) with any LLM provider through LiteLLM.
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.
## Overview
AgentField's Python SDK uses `litellm.acompletion()` under the hood, giving you access to 100+ LLM providers out of the box:
- Use any LiteLLM-supported model (OpenAI, Anthropic, Azure, Bedrock, Ollama, etc.)
- Switch between providers by changing the model string
- All LiteLLM features (caching, fallbacks, routing) work automatically
## Prerequisites
- Python 3.9+
- API keys for your LLM providers
- AgentField control plane (optional, for orchestration features)
## Installation
```bash
pip install agentfield
```
## Quick Start
### Basic Agent with OpenAI
```python
from agentfield import Agent, AgentConfig
config = AgentConfig(
name="my-agent",
model="gpt-4o", # Any LiteLLM-supported model
instructions="You are a helpful assistant."
)
agent = Agent(config)
response = await agent.run("Hello, world!")
```
### Using Anthropic
```python
config = AgentConfig(
name="claude-agent",
model="anthropic/claude-sonnet-4-20250514", # LiteLLM model format
instructions="You are a helpful assistant."
)
```
### Using Ollama (Local Models)
```python
config = AgentConfig(
name="local-agent",
model="ollama/llama3.1", # LiteLLM's ollama/ prefix
instructions="You are a helpful assistant."
)
```
### Using Azure OpenAI
```python
config = AgentConfig(
name="azure-agent",
model="azure/gpt-4o", # LiteLLM's azure/ prefix
instructions="You are a helpful assistant."
)
```
### Using with LiteLLM Proxy
Point AgentField to a LiteLLM Proxy for centralized model management:
```python
import os
os.environ["OPENAI_API_BASE"] = "http://0.0.0.0:4000" # LiteLLM Proxy URL
os.environ["OPENAI_API_KEY"] = "sk-1234" # LiteLLM Proxy key
config = AgentConfig(
name="proxy-agent",
model="gpt-4o", # Virtual model name from proxy config
instructions="You are a helpful assistant."
)
```
## Multi-Agent Orchestration
AgentField's control plane orchestrates multiple agents, each potentially using different LLM providers:
```python
from agentfield import Agent, AgentConfig, ControlPlane
# Create agents with different providers
researcher = Agent(AgentConfig(
name="researcher",
model="anthropic/claude-sonnet-4-20250514",
instructions="You research topics thoroughly."
))
writer = Agent(AgentConfig(
name="writer",
model="gpt-4o",
instructions="You write clear, concise content."
))
# Register with control plane
cp = ControlPlane(server="http://localhost:8080")
cp.register(researcher)
cp.register(writer)
```
## Links
- [Documentation](https://agentfield.ai/docs)
- [GitHub](https://github.com/Agent-Field/agentfield)
- [Python SDK](https://github.com/Agent-Field/agentfield/tree/main/sdk/python)

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@ -184,6 +184,7 @@ const sidebars = {
slug: "/agent_sdks"
},
items: [
"tutorials/agentfield",
"tutorials/openai_agents_sdk",
"tutorials/claude_agent_sdk",
"tutorials/copilotkit_sdk",