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Agent Class Reference

The Agent class is the core orchestrator in AgentForge. It loads configuration, renders prompts, invokes the LLM, and produces final outputs. Agents can be subclassed for custom logic.

Constructor and Attributes

class Agent:
    def __init__(self, agent_name: Optional[str] = None, log_file: Optional[str] = 'agentforge'):
        self.agent_name: str
        self.logger: Logger
        self.config: Config
        self.prompt_processor: PromptProcessor
        self.parsing_processor: ParsingProcessor
        self.agent_config: AgentConfig
        self.prompt_template: dict
        self.template_data: dict
        self.prompt: dict
        self.result: Optional[str]
        self.parsed_result: Optional[Any]
        self.output: Optional[str]
        self.persona: Optional[dict]
        self.images: list
        self.model: Optional[BaseModel]
        self._initialize_data_attributes()
        self._initialize_agent_config()
  • agent_name determines which prompt/config file to load.
  • logger handles logging.
  • config loads and manages all configuration.
  • prompt_processor and parsing_processor handle prompt rendering and result parsing.
  • agent_config holds all loaded config, including model, params, prompts, persona, and custom fields.
  • prompt_template and template_data are used for prompt rendering.
  • images can be attached to model calls if supported.

Lifecycle: run()

def run(self, **kwargs) -> Optional[str]:
    self.logger.info(f"{self.agent_name} - Running...")
    self._execute_workflow(**kwargs)
    self.logger.info(f"{self.agent_name} - Done!")
    return self.output

The workflow includes:

  • Loading and merging config and runtime data
  • Processing data (optional hook)
  • Rendering prompts
  • Running the model
  • Parsing and post-processing results
  • Building the final output

Configuration Loading

Configuration is loaded from the .agentforge/prompts/ folder and merged with system defaults. The agent loads:

  • prompts: System and user prompt templates
  • params: Model parameters
  • persona: Persona data if enabled
  • settings: System and agent settings
  • simulated_response: Used if debug mode is enabled
  • parse_response_as: Format for parsing model output (e.g., json)
  • custom_fields: Any extra fields from the YAML config

Prompt Rendering

Prompts are rendered by substituting variables from template_data into the prompt_template using PromptProcessor. If any required variable is missing or empty, the corresponding prompt section is skipped.

Extension Points

The following methods are designed to be overridden in subclasses:

def load_additional_data(self):
    pass

def process_data(self):
    pass

def parse_result(self):
    self.parsed_result = self.parsing_processor.parse_by_format(self.result, self.agent_config.parse_response_as)

def post_process_result(self):
    pass

def build_output(self):
    self.output = self.parsed_result
  • load_additional_data: Add custom data to template_data.
  • process_data: Preprocess data before prompt rendering.
  • parse_result: Parse the LLM output after model execution.
  • post_process_result: Additional processing after parsing.
  • build_output: Format the final output returned by run().

Usage Example

from agentforge.agent import Agent
import json

class MyCustomAgent(Agent):
    def load_additional_data(self):
        self.template_data["custom_var"] = "custom value"
    def parse_result(self):
        try:
            self.parsed_result = json.loads(self.result)
        except Exception:
            self.parsed_result = {"text": self.result}
    def build_output(self):
        self.output = f"Processed: {self.parsed_result.get('key', self.result)}"

agent = MyCustomAgent("template_file")
result = agent.run(dynamic_var="value")

Where .agentforge/prompts/template_file.yaml contains:

prompts:
  system: "You are a helpful assistant."
  user: "The user asks: {dynamic_var}"

Supported Config Keys

  • prompts: System and user prompt templates
  • params: Model parameters
  • persona: Persona data (optional)
  • settings: System and agent settings
  • simulated_response: Used for debug mode
  • parse_response_as: Format for parsing model output (e.g., json)
  • custom_fields: Any extra fields from the YAML config