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AgentField with LiteLLM

Use AgentField 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​

pip install agentfield

Quick Start​

Basic Agent with OpenAI​

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​

config = AgentConfig(
    name="claude-agent",
    model="anthropic/claude-sonnet-4-20250514",  # LiteLLM model format
    instructions="You are a helpful assistant."
)

Using Ollama (Local Models)​

config = AgentConfig(
    name="local-agent",
    model="ollama/llama3.1",  # LiteLLM's ollama/ prefix
    instructions="You are a helpful assistant."
)

Using Azure OpenAI​

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:

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:

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)