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Agent Configuration

This page provides detailed documentation for all agent configuration parameters, including execution controls, context management, and output formatting options.

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Core Parameters

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Execution Control Parameters

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max_iter

  • Type: int
  • Default: 15
  • Description: Maximum number of iterations an agent will attempt to complete a task
  • Range: 1-100 (recommended: 10-25)
The max_iter parameter controls how many times an agent can iterate through its execution loop when working on a task. This prevents infinite loops and ensures tasks complete within reasonable bounds. 
agent = Agent(
    name="Researcher",
    max_iter=20,  # Allow up to 20 iterations
    # other parameters...
)
Best Practices:
  • Set lower values (5-10) for simple tasks
  • Use higher values (20-30) for complex research or analysis tasks
  • Monitor iteration count to optimize performance

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max_retry_limit

  • Type: int
  • Default: 2
  • Description: Maximum number of times to retry a failed operation
  • Range: 0-10 (recommended: 2-5)
Controls how many times an agent will retry operations that fail due to transient errors like network issues or API rate limits. 
agent = Agent(
    name="DataAnalyst",
    max_retry_limit=3,  # Retry failed operations up to 3 times
    # other parameters...
)
Use Cases:
  • API calls that may face rate limits
  • Network operations that may timeout
  • Tool executions that may fail intermittently

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Context Management

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context_length

  • Type: int
  • Default: None (uses model default)
  • Description: Maximum context window size in tokens
  • Common Values:
    • GPT-4: 128000
    • GPT-3.5: 16385
    • Claude 3: 200000
Defines the maximum amount of context (in tokens) that can be included in a single LLM request. 
agent = Agent(
    name="Analyzer",
    context_length=50000,  # Use 50K token context window
    respect_context_window=True,
    # other parameters...
)
Important Considerations:
  • Larger context windows allow more information but increase costs
  • Some models perform better with focused context
  • Always validate against your LLM’s maximum context size

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Output Formatting

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markdown

  • Type: bool
  • Default: True
  • Description: Enable markdown formatting in agent outputs
Controls whether the agent formats its responses using markdown syntax for better readability. 
agent = Agent(
    name="Reporter",
    markdown=True,  # Enable markdown formatting
    response_template="""
    ## Analysis Report
    
    {content}
    
    ### Key Findings
    {findings}
    """,
    # other parameters...
)
Features When Enabled:
  • Headers using #, ##, etc.
  • Bold text with **text**
  • Code blocks with ```
  • Lists and tables
  • Links and images

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Advanced Execution Controls

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Performance Optimization

agent = Agent(
    name="HighPerformanceAgent",
    # Execution limits
    max_iter=25,
    max_retry_limit=3,
    max_execution_time=300,  # 5 minutes timeout
    
    # Performance settings
    max_rpm=60,  # Rate limit: 60 requests per minute
    cache=True,  # Enable response caching
    
    # Context optimization
    context_length=100000,
    respect_context_window=True,
    
    # Output control
    markdown=True,
    verbose=False,  # Reduce logging for performance
)

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Error Handling Configuration

agent = Agent(
    name="ResilientAgent",
    # Retry configuration
    max_retry_limit=5,
    
    # Error handling
    allow_code_execution=True,
    code_execution_mode="safe",  # Safe mode with restrictions
    
    # Fallback behavior
    llm_config={
        "temperature": 0.7,
        "fallback_models": ["gpt-3.5-turbo", "claude-2"],
        "retry_delay": 2.0,  # Seconds between retries
        "exponential_backoff": True
    }
)

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Configuration Patterns

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Research Agent Configuration

research_agent = Agent(
    name="ResearchSpecialist",
    role="Senior Research Analyst",
    goal="Conduct thorough research and provide comprehensive reports",
    backstory="Expert researcher with 10 years of experience",
    
    # Optimized for long research tasks
    max_iter=30,
    max_retry_limit=5,
    context_length=128000,
    
    # Output preferences
    markdown=True,
    verbose=True,
    stream=True,  # Stream responses
    
    # Quality controls
    self_reflect=True,
    max_reflect=3,
    reasoning_steps=True
)

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Quick Task Agent Configuration

quick_agent = Agent(
    name="QuickResponder",
    role="Rapid Task Handler",
    
    # Optimized for speed
    max_iter=5,
    max_retry_limit=1,
    context_length=16000,
    
    # Minimal output
    markdown=False,
    verbose=False,
    stream=False
)

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Environment Variable Overrides

Agent parameters can be overridden using environment variables: 
# Override default iteration limit
export PRAISONAI_AGENT_MAX_ITER=20

# Override retry limit
export PRAISONAI_AGENT_MAX_RETRY_LIMIT=5

# Set context length
export PRAISONAI_AGENT_CONTEXT_LENGTH=100000

# Disable markdown
export PRAISONAI_AGENT_MARKDOWN=false

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Validation and Constraints

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Parameter Validation Rules

ParameterValidationError Handling
max_iterMust be > 0Defaults to 15 if invalid
max_retry_limitMust be >= 0Defaults to 2 if invalid
context_lengthMust be greater than 0 and less than or equal to model maxUses model default if invalid
markdownMust be booleanDefaults to True if invalid

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Interaction Between Parameters

  1. Context Length and Max Iterations
    • Higher max_iter may require larger context_length
    • Each iteration adds to context usage
  2. Retry Limit and Execution Time
    • Higher max_retry_limit extends total execution time
    • Consider both when setting timeouts
  3. Markdown and Response Templates
    • markdown=True enables template formatting
    • Templates should use markdown syntax when enabled

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Troubleshooting

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Common Issues

  1. Agent exceeds iteration limit 
    # Solution: Increase max_iter or optimize task
agent.max_iter = 30
  2. Context window exceeded 
    # Solution: Increase context_length or enable truncation
agent.context_length = 100000
agent.respect_context_window = True
  3. Frequent retry failures 
    # Solution: Increase retry limit and add delays
agent.max_retry_limit = 5
agent.llm_config["retry_delay"] = 3.0

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See Also