> ## Documentation Index
> Fetch the complete documentation index at: https://laddr.agnetlabs.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Agent Schema

> Complete schema reference for Laddr agent configuration - required fields, optional fields, and examples

Complete reference for the Laddr agent configuration schema.

***

## Agent Configuration

```python theme={null}
from laddr import Agent

agent = Agent(
    name="researcher",
    role="Web Research Specialist",
    goal="Search the web and summarize findings",
    backstory="You are a research-focused agent...",
    llm=llm_config,
    tools=[tool1, tool2],
    max_retries=1,
    max_iterations=3,
    max_tool_calls=10,
    timeout=300,
    trace_enabled=True,
    instructions="Custom instructions...",
    is_coordinator=False
)

```

***

## Required Fields

| Field       | Type  | Description                  |
| ----------- | ----- | ---------------------------- |
| `name`      | `str` | Unique agent identifier      |
| `role`      | `str` | Agent's role description     |
| `goal`      | `str` | Primary objective or task    |
| `backstory` | `str` | Context that shapes behavior |
| `llm`       | `LLM` | LLM configuration object     |

***

## Optional Fields

| Field            | Type   | Default | Description                        |
| ---------------- | ------ | ------- | ---------------------------------- |
| `tools`          | `list` | `[]`    | List of tool functions             |
| `max_retries`    | `int`  | `1`     | Number of retries for failed tasks |
| `max_iterations` | `int`  | `3`     | Maximum reasoning loops            |
| `max_tool_calls` | `int`  | `10`    | Maximum tool calls per iteration   |
| `timeout`        | `int`  | `300`   | Task timeout in seconds            |
| `trace_enabled`  | `bool` | `True`  | Enable execution tracing           |
| `instructions`   | `str`  | `None`  | Custom instructions for behavior   |
| `is_coordinator` | `bool` | `False` | Enable delegation tools            |

***

## LLM Configuration

### OpenAI

```python theme={null}
from laddr.llms import openai

llm = openai(
    model="gpt-4o-mini",
    temperature=0.0,
    max_tokens=2000
)

```

### Gemini

```python theme={null}
from laddr.llms import gemini

llm = gemini(
    model="gemini-2.5-flash",
    temperature=0.0
)

```

### Anthropic

```python theme={null}
from laddr.llms import anthropic

llm = anthropic(
    model="claude-3-5-sonnet-20241022",
    temperature=0.0
)

```

### Ollama

```python theme={null}
from laddr.llms import ollama

llm = ollama(
    model="llama3.2:latest",
    base_url="http://localhost:11434"
)

```

***

## Example Configuration

```python theme={null}
from laddr import Agent
from laddr.llms import gemini
from tools.web_tools import web_search

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,
    instructions="""
    When researching:
    1. Use web_search to find information
    2. Prioritize recent sources
    3. Cite sources in your response
    """
)

```

***

## Next Steps

* [Tool Schema](/reference/schemas/tool-schema) - Tool configuration schema
* [Agent Configuration](/guides/agents/agent-config) - Configuration guide
* [Agent Authoring](/guides/agents/authoring) - Best practices