​
Module praisonaiagents.agent.agent

​
Classes

​
Agent

The main class representing an AI agent with specific role, goal, and capabilities.

​
Parameters

  • name: str - Name of the agent
  • role: str - Role of the agent
  • goal: str - Goal the agent aims to achieve
  • backstory: str - Background story of the agent
  • llm: str | Any | None = 'gpt-4o' - Language model to use
  • tools: List[Any] | None = None - List of tools available to the agent
  • function_calling_llm: Any | None = None - LLM for function calling
  • max_iter: int = 20 - Maximum iterations
  • max_rpm: int | None = None - Maximum requests per minute
  • max_execution_time: int | None = None - Maximum execution time
  • memory: bool = True - Enable memory
  • verbose: bool = True - Enable verbose output
  • allow_delegation: bool = False - Allow task delegation
  • step_callback: Any | None = None - Callback for each step
  • cache: bool = True - Enable caching
  • system_template: str | None = None - System prompt template
  • prompt_template: str | None = None - Prompt template
  • response_template: str | None = None - Response template
  • allow_code_execution: bool | None = False - Allow code execution
  • max_retry_limit: int = 2 - Maximum retry attempts
  • respect_context_window: bool = True - Respect context window size
  • code_execution_mode: Literal['safe', 'unsafe'] = 'safe' - Code execution mode
  • embedder_config: Dict[str, Any] | None = None - Embedder configuration
  • knowledge_sources: List[Any] | None = None - Knowledge sources
  • use_system_prompt: bool | None = True - Use system prompt
  • markdown: bool = True - Enable markdown
  • self_reflect: bool = True - Enable self reflection
  • max_reflect: int = 3 - Maximum reflections
  • min_reflect: int = 1 - Minimum reflections
  • reflect_llm: str | None = None - LLM for reflection

​
Methods

  • chat(self, prompt, temperature=0.2, tools=None, output_json=None) - Chat with the agent
  • achat(self, prompt, temperature=0.2, tools=None, output_json=None) - Async version of chat method
  • clean_json_output(self, output: str) → str - Clean and extract JSON from response text
  • clear_history(self) - Clear chat history
  • execute_tool(self, function_name, arguments) - Execute a tool dynamically based on the function name and arguments
  • _achat_completion(self, response, tools) - Async version of _chat_completion method

​
Async Support

The Agent class provides async support through the following methods:

  • achat: Async version of the chat method for non-blocking communication
  • _achat_completion: Internal async method for handling chat completions

Example usage:

async def main():
    agent = Agent(
        name="AsyncAgent",
        role="Async Specialist",
        goal="Handle async operations",
        backstory="Expert in async processing"
    )
    
    # Use async chat
    result = await agent.achat(
        prompt="Your prompt here",
        tools=your_tools,
        output_json=your_schema
    )
    print(result)

asyncio.run(main())