Agent Configuration

from __future__ import annotations
import asyncio
import os
from dotenv import load_dotenv
from laddr import Agent, WorkerRunner
from laddr.llms import openai
from tools.web_tools import web_search, scrape_url, extract_links

load_dotenv()

TOOLS = [web_search, scrape_url, extract_links]

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.",
    llm=openai(model="gpt-4o-mini", temperature=0.0),
    tools=TOOLS,
    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.llms import openai

agent = Agent(
    name="researcher",
    llm=openai(
        model="gpt-4o-mini",
        temperature=0.0,
        max_tokens=2000
    ),
    # ... other config
)

from laddr.llms import gemini

agent = Agent(
    name="researcher",
    llm=gemini(
        model="gemini-2.5-flash",
        temperature=0.0
    ),
    # ... other config
)

from laddr.llms import anthropic

agent = Agent(
    name="researcher",
    llm=anthropic(
        model="claude-3-5-sonnet-20241022",
        temperature=0.0
    ),
    # ... other config
)

from laddr.llms import ollama

agent = Agent(
    name="researcher",
    llm=ollama(
        model="llama3.2:latest",
        base_url="http://localhost:11434"
    ),
    # ... other config
)

# .env
# Queue Backend
QUEUE_BACKEND=redis  # or kafka, memory
REDIS_URL=redis://localhost:6379
KAFKA_BOOTSTRAP=kafka:9092

# Database
DB_BACKEND=sqlite  # or postgresql
DATABASE_URL=sqlite:///./laddr.db
# or
DATABASE_URL=postgresql://user:pass@localhost:5432/laddr

# .env
# Global LLM settings
LLM_PROVIDER=openai
LLM_MODEL=gpt-4o-mini
LLM_TEMPERATURE=0.0

# API Keys
OPENAI_API_KEY=sk-...
GEMINI_API_KEY=your_gemini_api_key
ANTHROPIC_API_KEY=sk-ant-...

# Per-agent model overrides
RESEARCHER_MODEL=gpt-4o-mini
WRITER_MODEL=claude-3-5-sonnet-20241022
COORDINATOR_MODEL=gemini-2.5-flash

# .env
SERPER_API_KEY=your_serper_api_key
WEATHER_API_KEY=your_weather_api_key
# ... other tool keys

agent = Agent(
    name="researcher",
    max_retries=3,  # Retry up to 3 times on failure
    # ... other config
)

agent = Agent(
    name="researcher",
    max_iterations=5,  # Allow up to 5 reasoning loops
    max_tool_calls=10,  # Max 10 tool calls per iteration
    # ... other config
)

agent = Agent(
    name="researcher",
    timeout=60,  # 60 second timeout
    # ... other config
)

from laddr import tool

@tool(
    name="web_search",
    description="Search the web",
    parameters={
        "type": "object",
        "properties": {
            "query": {"type": "string"}
        }
    }
)
def web_search(query: str):
    # Tool implementation
    pass

agent = Agent(
    name="researcher",
    tools=[web_search],  # Register tool
    # ... other config
)

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

agent = Agent(
    name="researcher",
    trace_enabled=True,  # Enable execution traces
    # ... other config
)

# Via dashboard
# http://localhost:5173

# Via API
curl http://localhost:8000/api/jobs/{job_id}

# Via CLI
laddr logs researcher --follow

from laddr import Agent, WorkerRunner

researcher = Agent(
    name="researcher",
    # ... config
)

async def main():
    runner = WorkerRunner(agent=researcher)
    await runner.start()

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

# Run locally without worker
laddr run-local researcher --input '{"query": "test"}'

coordinator = Agent(
    name="coordinator",
    role="Task Coordinator",
    goal="Coordinate multi-agent workflows",
    is_coordinator=True,  # Enables delegation tools
    # ... other config
)

agent = Agent(
    name="researcher",
    instructions="""
    When researching:
    1. Use web_search to find information
    2. Prioritize recent sources (last 2 years)
    3. Cite sources in your response
    4. Summarize findings in 2-3 paragraphs
    """,
    # ... other config
)

# Follow logs in real-time
laddr logs researcher --follow

# Show last 100 lines
laddr logs researcher --tail 100

# Show all services
laddr ps

# Run diagnostics
laddr check

# Check agent file exists
ls agents/researcher.py

# Verify import
python -c "from agents.researcher import researcher; print(researcher.name)"

# Verify API key
echo $OPENAI_API_KEY

# Test API connection
curl https://api.openai.com/v1/models \\
  -H "Authorization: Bearer $OPENAI_API_KEY"

# Check tool is registered
python -c "from agents.researcher import researcher; print(researcher.tools)"

# Verify tool decorator
grep -n "@tool" tools/web_tools.py

# ✅ Good
researcher = Agent(name="researcher", ...)
data_analyzer = Agent(name="data_analyzer", ...)

# ❌ Bad
agent1 = Agent(name="agent1", ...)
a = Agent(name="a", ...)

# Quick tasks
fast_agent = Agent(timeout=30, ...)

# Complex tasks
complex_agent = Agent(timeout=300, ...)

# ✅ Good - 3-5 focused tools
agent = Agent(tools=[tool1, tool2, tool3], ...)

# ❌ Bad - Too many tools
agent = Agent(tools=[tool1, ..., tool20], ...)

# ✅ Good - Configurable
llm=openai(model=os.getenv("LLM_MODEL", "gpt-4o-mini"))

# ❌ Bad - Hardcoded
llm=openai(model="gpt-4o-mini")