agframe 0.1.0

This is an playground for AI agents to code within. Constraints are clearly outlined and code is cleary displayed.

Engine

This codebase implements a Pipeline/Use Case Pattern - a workflow orchestration system that executes a sequence of steps with built-in error handling and rollback capabilities.

Architecture Overview

Command (input) → Container → [Action₁ → Action₂ → Action₃ → ...] → Result/Error
                                 ↓         ↓         ↓
                               Task      Task      Task

Core Components

1. Container (usecases/container.py)

The main orchestrator/pipeline executor.

• Purpose: Executes a sequence of Actions (steps) in order, passing accumulated results between them
• Key behaviors:
  • Receives a Command via input() method
  • Iterates through steps (Actions), calling do(context, request) on each
  • Accumulates return values from each step into returned_value dict
  • If a key already exists, converts it to a list and appends new values
  • On any exception: triggers rollback() on all previously executed steps
  • Returns either the accumulated results dict or an Error object

2. Action (actions/action.py)

Base class for individual steps in the pipeline.

• Purpose: Represents a single unit of work in the pipeline
• Interface:
  • do(context, request) - Execute the action's logic; receives accumulated context and original request
  • rollback() - Undo the action's effects (called on failure)
• Note: Actions are described as "steps that do things with tasks, like access a file, or make an API request"

3. Command (commands/command.py)

Input wrapper that carries the initial request data.

• Purpose: Encapsulates the request/input data passed into the pipeline
• Interface:
  • make_request(data) - Sets the request payload

4. Task (tasks/task.py)

Abstract base for actual work units.

• Purpose: Performs the actual operations (API calls, file I/O, etc.)
• Interface:
  • execute(event, data) - Executes the task logic

5. Error (errors/error.py)

Structured error wrapper.

• Purpose: Wraps error data as a dictionary for consistent error handling
• Contains: failure message, failure reason, and optionally rollback failure info

Execution Flow

1. Create Actions: Instantiate Action subclasses that implement do() and optionally rollback()
2. Create Container: Container(steps=[action1, action2, ...], name="use case name")
3. Create Command: Command("name") with request data
4. Execute: container.input(command) starts the pipeline
5. Result: Returns accumulated dict of results, or Error on failure

Error Handling & Rollback

• If any step throws an exception, the Container:
  1. Captures the failure index and reason
  2. Calls rollback() on all steps up to and including the failed step
  3. Returns an Error object with details
• If rollback itself fails, the error includes failed_rollback information

Usage Example

# Define actions
class GetNumber(Action):
    def do(self, context, request):
        return {"number": 42}
    def rollback(self):
        pass

# Build pipeline
steps = [GetNumber("step1"), AnotherAction("step2")]
container = Container(steps, "my-use-case")
command = Command("process")

# Execute
result = container.input(command)  # Returns {"number": 42, ...} or Error