Ollama

Use local Ollama models with PraisonAI Agents

Environment Variables

Setting MODEL_NAME=ollama/llama3.2 is enough — the default base URL http://localhost:11434/v1 is used automatically. The OLLAMA_API_KEY environment variable is consulted; OPENAI_API_KEY is not used as a fallback (though most local Ollama setups don’t need an API key).

import os
from praisonaiagents import Agent

os.environ["MODEL_NAME"] = "ollama/llama3.2"
# API key usually not needed for local Ollama

agent = Agent(name="Local Agent", instructions="You run locally via Ollama")

Models

Run models locally with Ollama. Popular options:

• Recommended: ollama/llama3.2 (latest Llama)
• Reasoning: ollama/deepseek-r1 (reasoning model)
• Small: ollama/qwen3 (efficient)
• Code: ollama/codellama (coding tasks)

Setup

# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# Start Ollama server
ollama serve

# Pull a model
ollama pull llama3.2

For inference (running agents against an Ollama model), keep ollama serve running in a terminal. For training/pushing via praisonai train, the daemon is started automatically — no manual ollama serve needed.

Python

# No API key needed - runs locally
from praisonaiagents import Agent

agent = Agent(
    instructions="You are a helpful assistant",
    llm="ollama/llama3.2"
)
agent.start("Explain deep learning")

With Tools

from praisonaiagents import Agent

def read_file(path: str) -> str:
    """Read a file's contents."""
    with open(path, 'r') as f:
        return f.read()

agent = Agent(
    instructions="You are a code assistant",
    llm="ollama/codellama",
    tools=[read_file]
)
agent.start("Read and explain main.py")

Multi-Agent

from praisonaiagents import Agent, Task, AgentTeam

researcher = Agent(
    instructions="You research topics thoroughly",
    llm="ollama/llama3.2"
)
writer = Agent(
    instructions="You write clear summaries",
    llm="ollama/qwen3"
)

task1 = Task(description="Research Python best practices", agent=researcher)
task2 = Task(description="Write a guide", agent=writer)

agents = AgentTeam(agents=[researcher, writer], tasks=[task1, task2])
agents.start()

DeepSeek Reasoning

from praisonaiagents import Agent

agent = Agent(
    instructions="You are a problem solver",
    llm="ollama/deepseek-r1"
)
agent.start("Solve this math problem: What is 15% of 240?")

CLI

# Basic prompt
python -m praisonai "Explain AI" --ollama llama3.2

# With specific model
python -m praisonai "Write code" --llm ollama/codellama

# Run agents.yaml
python -m praisonai

YAML

framework: praisonai
topic: Local AI development
agents:
  coder:
    role: Software Developer
    goal: Write clean code
    instructions: You are an expert programmer
    llm:
      model: ollama/codellama
    tasks:
      code_task:
        description: Write a Python function to sort a list
        expected_output: Clean, documented Python code

  reviewer:
    role: Code Reviewer
    goal: Review and improve code
    instructions: You review code for best practices
    llm:
      model: ollama/llama3.2
    tasks:
      review_task:
        description: Review the code and suggest improvements
        expected_output: Code review with suggestions