runtime_local_ollama.py

"""Local agent loop with Ollama LLM — runs locally, not in a managed runtime.

Uses the new canonical LocalAgent class which clearly communicates that only the
agent loop runs locally. The LLM calls go to a local Ollama instance, no managed runtime involved.
"""
from praisonai import Agent
from praisonai.integrations import LocalAgent, LocalAgentConfig

# Create a local agent using Ollama LLM
local = LocalAgent(
    config=LocalAgentConfig(
        model="ollama/llama3.2",  # Use Ollama with litellm routing prefix
        system="You are a helpful coding assistant. Be concise.",
        name="LocalOllamaAgent",
        tools=["execute_command", "read_file", "write_file"],
    ),
)

agent = Agent(name="local-ollama", backend=local)

# 1. Basic execution - agent loop runs locally, LLM calls go to local Ollama
print("[1] Local execution with Ollama LLM...")
result = agent.start("What is the capital of France? One word.", stream=True)
print(f"    Result: {result}")

# 2. Agent metadata (locally generated UUIDs)
print(f"\n[2] Agent ID: {local.agent_id}")
print(f"    Version: {local.agent_version}")
print(f"    Env ID:  {local.environment_id}")
print(f"    Session: {local.session_id}")

# 3. Multi-turn conversation (session state maintained locally)
print("\n[3] Multi-turn conversation...")
result = agent.start("What country is that city in?", stream=True)
print(f"    Result: {result}")

# 4. Usage tracking (accumulated locally)
info = local.retrieve_session()
print(f"\n[4] Usage: in={info['usage']['input_tokens']}, out={info['usage']['output_tokens']}")

# 5. Tool execution (runs in local subprocess)
print("\n[5] Tool execution example...")
result = agent.start("Create a file called hello_ollama.txt with 'Hello from Ollama agent!'", stream=True)
print(f"    Tool result: {result}")

print("\nDone! Agent loop ran locally with Ollama LLM calls.")