---
title: 'Agents'
description: 'Understanding AI agents in PandaAGI SDK'
---

## What are Agents?

Agents are the core building blocks of the PandaAGI SDK. They are autonomous AI entities that can understand natural language, make decisions, use tools, and interact with their environment to accomplish tasks.

<img
  className="block dark:hidden"
  src="/images/agent-architecture-light.svg"
  alt="Agent Architecture"
/>
<img
  className="hidden dark:block"
  src="/images/agent-architecture-dark.svg"
  alt="Agent Architecture"
/>

## Agent Components

<CardGroup cols={2}>
  <Card
    title="Core Intelligence"
    icon="brain"
  >
    The LLM-powered reasoning engine that processes queries and makes decisions
  </Card>
  <Card
    title="Tool Integration"
    icon="wrench"
  >
    Access to various tools for web search, file operations, and more
  </Card>
  <Card
    title="Memory System"
    icon="database"
  >
    Persistent memory for context and conversation history
  </Card>
  <Card
    title="Environment"
    icon="globe"
  >
    Isolated execution environment for safe code execution
  </Card>
</CardGroup>

## Creating Agents

### Basic Agent

The simplest way to create an agent:

```python
from panda_agi.client import Agent

agent = Agent(
    name="MyAgent",
    description="A helpful assistant"
)

response = agent.run("What's the weather like today?")
```

### Agent with Configuration

For more control over agent behavior:

```python
from panda_agi.client import Agent, AgentConfig

config = AgentConfig(
    model="gpt-4",
    temperature=0.7,
    max_iterations=10,
    timeout=60
)

agent = Agent(
    name="ConfiguredAgent",
    description="A precisely configured assistant",
    config=config
)
```

### Agent with Tools

Agents become powerful when equipped with tools:

```python
from panda_agi.client import Agent
from panda_agi.tools.search import WebSearchTool
from panda_agi.tools.navigation import WebNavigationTool
from panda_agi.tools.file_system import FileSystemTool

agent = Agent(
    name="ResearchAgent",
    description="An agent that can research and analyze information",
    tools=[
        WebSearchTool(),
        WebNavigationTool(),
        FileSystemTool()
    ]
)
```

## Agent Configuration

<AccordionGroup>
  <Accordion title="Model Selection">
    Choose the underlying language model:
    
    ```python
    config = AgentConfig(
        model="gpt-4",           # OpenAI GPT-4
        # model="gpt-3.5-turbo", # OpenAI GPT-3.5
        # model="claude-3",      # Anthropic Claude
    )
    ```
  </Accordion>

  <Accordion title="Temperature Control">
    Control creativity vs consistency:
    
    ```python
    config = AgentConfig(
        temperature=0.0,  # Deterministic, consistent
        # temperature=0.7,  # Balanced
        # temperature=1.0,  # Creative, varied
    )
    ```
  </Accordion>

  <Accordion title="Execution Limits">
    Set safety boundaries:
    
    ```python
    config = AgentConfig(
        max_iterations=10,    # Max tool calls per query
        timeout=60,          # Max execution time (seconds)
        max_tokens=4000,     # Max response length
    )
    ```
  </Accordion>

  <Accordion title="Memory Settings">
    Configure memory behavior:
    
    ```python
    config = AgentConfig(
        memory_size=1000,        # Max conversation history
        persistent_memory=True,   # Save between sessions
        memory_compression=True,  # Compress old memories
    )
    ```
  </Accordion>
</AccordionGroup>

## Agent Lifecycle

Understanding how agents work internally:

<Steps>
  <Step title="Initialization">
    Agent loads configuration, tools, and establishes environment connection
  </Step>
  
  <Step title="Query Processing">
    Agent analyzes the user query and determines the best approach
  </Step>
  
  <Step title="Planning">
    Agent creates a plan of action, identifying which tools to use
  </Step>
  
  <Step title="Execution">
    Agent executes the plan, using tools and processing results iteratively
  </Step>
  
  <Step title="Response Generation">
    Agent synthesizes results into a coherent response for the user
  </Step>
</Steps>

## Agent Patterns

### Research Agent

Perfect for information gathering and analysis:

```python
research_agent = Agent(
    name="ResearchAgent",
    description="""
    A thorough research assistant that can:
    - Search the web for current information
    - Visit and analyze web pages
    - Synthesize findings into comprehensive reports
    """,
    tools=[WebSearchTool(), WebNavigationTool()],
    config=AgentConfig(
        model="gpt-4",
        temperature=0.3,  # More focused
        max_iterations=15
    )
)
```

### Code Assistant

Specialized for programming tasks:

```python
code_agent = Agent(
    name="CodeAssistant",
    description="""
    A programming assistant that can:
    - Write and debug code
    - Analyze codebases
    - Create documentation
    - Run tests and scripts
    """,
    tools=[FileSystemTool(), CodeExecutionTool()],
    config=AgentConfig(
        model="gpt-4",
        temperature=0.1,  # Very precise
        max_iterations=20
    )
)
```

### Creative Agent

For content creation and brainstorming:

```python
creative_agent = Agent(
    name="CreativeAgent",
    description="""
    A creative assistant that can:
    - Generate original content
    - Brainstorm ideas
    - Create stories and scripts
    - Design concepts
    """,
    tools=[WebSearchTool(), ImageGenerationTool()],
    config=AgentConfig(
        model="gpt-4",
        temperature=0.8,  # More creative
        max_iterations=10
    )
)
```

## Advanced Features

### Custom Personalities

Give your agent a unique personality:

```python
agent = Agent(
    name="FriendlyBot",
    description="""
    You are a friendly, enthusiastic assistant who loves helping people.
    Always be positive, use emojis occasionally, and provide encouraging
    responses. You're knowledgeable but humble, and you love learning
    new things from users.
    """,
    config=AgentConfig(temperature=0.7)
)
```

### Multi-Agent Collaboration

Agents can work together:

```python
# Specialist agents
researcher = Agent(name="Researcher", tools=[WebSearchTool()])
analyst = Agent(name="Analyst", tools=[DataAnalysisTool()])
writer = Agent(name="Writer", tools=[FileSystemTool()])

# Orchestrator agent
coordinator = Agent(
    name="Coordinator",
    description="Coordinates work between specialist agents",
    agents=[researcher, analyst, writer]
)
```

### Persistent Memory

Agents can remember across sessions:

```python
agent = Agent(
    name="PersonalAssistant",
    config=AgentConfig(
        persistent_memory=True,
        memory_file="assistant_memory.json"
    )
)

# First session
agent.run("My name is Alice and I love hiking")

# Later session (agent remembers)
agent.run("What outdoor activities do I enjoy?")
# Response: "You mentioned that you love hiking!"
```

## Best Practices

<AccordionGroup>
  <Accordion title="Clear Descriptions">
    Write clear, specific agent descriptions:
    
    ```python
    # Good
    description = """
    A financial analysis agent that can:
    - Analyze stock market data
    - Calculate financial ratios
    - Generate investment reports
    - Track portfolio performance
    """
    
    # Avoid
    description = "A helpful agent"
    ```
  </Accordion>

  <Accordion title="Appropriate Tools">
    Only include tools the agent actually needs:
    
    ```python
    # For a research agent
    tools = [WebSearchTool(), WebNavigationTool()]
    
    # Don't include unrelated tools
    # tools = [WebSearchTool(), ImageGenerationTool(), MusicTool()]
    ```
  </Accordion>

  <Accordion title="Resource Management">
    Set appropriate limits to prevent runaway execution:
    
    ```python
    config = AgentConfig(
        timeout=120,        # 2 minutes max
        max_iterations=15,  # Reasonable limit
        max_tokens=2000     # Control response length
    )
    ```
  </Accordion>

  <Accordion title="Error Handling">
    Always handle potential errors:
    
    ```python
    try:
        response = agent.run(query, timeout=60)
        return response
    except AgentError as e:
        logger.error(f"Agent error: {e}")
        return "I encountered an error processing your request."
    except TimeoutError:
        return "The request took too long to process."
    ```
  </Accordion>
</AccordionGroup>

## Next Steps

<CardGroup cols={2}>
  <Card
    title="Tools"
    icon="wrench"
    href="/concepts/tools"
  >
    Learn about the tools agents can use
  </Card>
  <Card
    title="Events"
    icon="bolt"
    href="/concepts/events"
  >
    Understand agent event streaming
  </Card>
</CardGroup> 