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Quickstart Guide

CLI Usage

Initialize and manage configurations: 

# Create starter configuration
llmling-agent init agents.yml

# Add to your configurations
llmling-agent add agents.yml

# Start chatting
llmling-agent chat assistant

Configured Agents

Create an agent configuration:

# agents.yml
agents:
  assistant:
    name: "Technical Assistant"
    model: openai:gpt-5-mini
    system_prompts:
      - You are a helpful technical assistant.
    toolsets:
      - type: file_access

Use it in code:

from llmling_agent import AgentPool

async def main():
    async with AgentPool("agents.yml") as pool:
        agent = pool.get_agent("assistant")
        response = await agent.run("What is Python?")
        print(response.data)

if __name__ == "__main__":
    import asyncio
    asyncio.run(main())

Functional Interface

Quick model interactions without configuration:

from llmling_agent import run_with_model, run_with_model_sync

# Async usage
async def main():
    # Simple completion
    result = await run_with_model(
        "Analyze this text",
        model="openai:gpt-5-mini"
    )
    print(result)

    # With structured output
    from pydantic import BaseModel

    class Analysis(BaseModel):
        summary: str
        key_points: list[str]

    result = await run_with_model(
        "Analyze the sentiment",
        model="openai:gpt-5-mini",
        output_type=Analysis
    )
    print(f"Summary: {result.summary}")
    print(f"Key points: {result.key_points}")

# Sync usage (convenience wrapper)
result = run_with_model_sync(
    "Quick question",
    model="openai:gpt-5-mini"
)

Next Steps

Note

For details about environment configuration (tools, resources, etc.), see the LLMling documentation.