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Ollama

Ollama allows you to run open-source models locally and use them with Agent Framework. This is ideal for development, testing, and scenarios where you need to keep data on-premises.

Prerequisites

  • Install and start Ollama.
  • Download a model, such as ollama pull llama3.2.

Installation

dotnet add package OllamaSharp
dotnet add package Microsoft.Agents.AI --prerelease

Configuration

OLLAMA_ENDPOINT="http://localhost:11434"
OLLAMA_MODEL_NAME="llama3.2"

Create an Ollama agent

using Microsoft.Agents.AI;
using Microsoft.Extensions.AI;
using OllamaSharp;

var endpoint = Environment.GetEnvironmentVariable("OLLAMA_ENDPOINT") ?? throw new InvalidOperationException("OLLAMA_ENDPOINT is not set.");
var modelName = Environment.GetEnvironmentVariable("OLLAMA_MODEL_NAME") ?? throw new InvalidOperationException("OLLAMA_MODEL_NAME is not set.");

// Get a chat client for Ollama and use it to construct an AIAgent.
AIAgent agent = new OllamaApiClient(new Uri(endpoint), modelName)
    .AsAIAgent(instructions: "You are good at telling jokes.", name: "Joker");

// Invoke the agent and output the text result.
Console.WriteLine(await agent.RunAsync("Tell me a joke about a pirate."));

Prerequisites

Ensure Ollama is installed and running locally with a model downloaded before running any examples:

ollama pull llama3.2

Note

Not all models support function calling. For tool usage, try llama3.2 or qwen3:4b.

Installation

pip install agent-framework-ollama --pre

Configuration

OLLAMA_MODEL="llama3.2"

The native client connects to http://localhost:11434 by default. Override it with the OLLAMA_HOST environment variable or the host constructor argument.

Create Ollama Agents

OllamaChatClient provides native Ollama integration with full support for function tools and streaming.

import asyncio
from agent_framework import Agent
from agent_framework.ollama import OllamaChatClient

async def main():
    agent = Agent(
        client=OllamaChatClient(),
        name="HelpfulAssistant",
        instructions="You are a helpful assistant running locally via Ollama.",
    )
    result = await agent.run("What is the largest city in France?")
    print(result)

asyncio.run(main())

Tools

The Python Ollama clients (OllamaChatClient and OpenAIChatClient pointed at an Ollama-compatible endpoint) support locally invoked tools. Hosted tool types do not exist because Ollama is a local model runtime.

Tool Status Notes
Function Tools ✅ Standard Python callables or @ai_function. Whether the selected model can actually call them depends on the model itself.
Tool Approval ✅ Provided by the framework's function-invoking chat client; works with any function-tool call.
Code Interpreter ❌ No hosted code interpreter.
File Search ❌ No hosted file search.
Web Search ❌ No hosted web search.
Hosted MCP Tools ❌ Ollama does not expose hosted MCP.
Local MCP Tools ✅ Runs in your process and works with any chat client.

Function Tools

import asyncio
from datetime import datetime
from agent_framework import Agent
from agent_framework.ollama import OllamaChatClient

def get_time(location: str) -> str:
    """Get the current time."""
    return f"The current time in {location} is {datetime.now().strftime('%I:%M %p')}."

async def main():
    agent = Agent(
        client=OllamaChatClient(),
        name="TimeAgent",
        instructions="You are a helpful time agent.",
        tools=get_time,
    )
    result = await agent.run("What time is it in Seattle?")
    print(result)

asyncio.run(main())

Streaming

from agent_framework import Agent
from agent_framework.ollama import OllamaChatClient

async def streaming_example():
    agent = Agent(
        client=OllamaChatClient(),
        instructions="You are a helpful assistant.",
    )
    print("Agent: ", end="", flush=True)
    async for chunk in agent.run("Tell me about Python.", stream=True):
        if chunk.text:
            print(chunk.text, end="", flush=True)
    print()

Note

Go support for this feature is coming soon. See the Agent Framework Go repository for the latest status.

Next steps

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