Your First Agent

from __future__ import annotations
import asyncio
from laddr import Agent, WorkerRunner
from laddr.llms import gemini
from tools.web_tools import web_search

# Define the researcher agent
researcher = Agent(
    name="researcher",
    role="Web Research Specialist",
    goal="Search the web and summarize findings",
    backstory="""You are a research-focused agent who finds accurate 
    data online and presents it clearly.""",
    llm=gemini(model="gemini-2.5-flash", temperature=0.0),
    tools=[web_search],
    max_retries=1,
    max_iterations=3,
    max_tool_calls=2,
    timeout=45,
    trace_enabled=True,
)

async def main():
    runner = WorkerRunner(agent=researcher)
    print("Starting researcher worker...")
    await runner.start()

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

from laddr import tool
import os
import requests

@tool(
    name="web_search",
    description="Search the web for information on a given topic",
    parameters={
        "type": "object",
        "properties": {
            "query": {
                "type": "string",
                "description": "The search query"
            }
        },
        "required": ["query"]
    }
)
def web_search(query: str) -> dict:
    """Search the web using Serper API."""
    api_key = os.getenv("SERPER_API_KEY")
    if not api_key:
        return {"error": "SERPER_API_KEY not set"}
    
    url = "https://google.serper.dev/search"
    headers = {
        "X-API-KEY": api_key,
        "Content-Type": "application/json"
    }
    payload = {"q": query}
    
    try:
        response = requests.post(url, json=payload, headers=headers)
        response.raise_for_status()
        data = response.json()
        
        # Extract top results
        results = []
        if "organic" in data:
            for item in data["organic"][:5]:
                results.append({
                    "title": item.get("title", ""),
                    "snippet": item.get("snippet", ""),
                    "link": item.get("link", "")
                })
        
        return {
            "status": "success",
            "results": results,
            "total": len(results)
        }
    except Exception as e:
        return {"status": "error", "error": str(e)}

GEMINI_API_KEY=your_gemini_api_key
SERPER_API_KEY=your_serper_api_key

laddr run-local researcher --input '{"query": "What is Laddr?"}'

python agents/researcher.py

laddr prompt run researcher --input query="Latest AI trends"

Starting researcher worker...
Processing task: {'query': 'What is Laddr?'}
Using tool: web_search
Tool result: {'status': 'success', 'results': [...], 'total': 5}
Generating summary...
Agent response: Based on my research, Laddr is an open-source multi-agent framework...
Task completed successfully

Agent(
    name="researcher",           # Unique identifier
    role="Web Research Specialist",  # Agent's role
    goal="Search the web...",    # What the agent does
    backstory="...",             # Context for behavior
)

llm=gemini(
    model="gemini-2.5-flash",    # Model name
    temperature=0.0              # Lower = more deterministic
)

max_retries=1,        # Retry failed tasks once
max_iterations=3,      # Max reasoning loops
max_tool_calls=2,      # Max tool calls per iteration
timeout=45,            # Timeout in seconds

from tools.web_tools import web_search, scrape_url, extract_links

researcher = Agent(
    name="researcher",
    # ... other config
    tools=[web_search, scrape_url, extract_links],  # Multiple tools
)

researcher = Agent(
    name="researcher",
    # ... other config
    instructions="""
    When researching:
    1. Use web_search to find relevant information
    2. Focus on recent and authoritative sources
    3. Summarize findings concisely
    4. Cite sources when possible
    """,
)

# Simple query
laddr run-local researcher --input '{"query": "Python async programming"}'

# Complex query
laddr run-local researcher --input '{"query": "Compare Laddr vs LangGraph"}'