fdsx 0.1.7

Declarative AI agent workflow execution framework

fdsx — Flow-Driven Stateful eXecution

A lightweight framework for building and executing complex AI agent workflows using declarative YAML definitions.

Overview

fdsx enables you to define AI agent workflows in YAML, combining the durability of LangGraph (checkpoint, interrupt, conditional routing) with the declarative structure of AWS Step Functions.

Key features:

• Declarative YAML-based workflow definition
• Stateful execution with checkpoint/resume
• Parallel execution with branch aggregation
• Batch task processing
• Multiple LLM provider support (Claude, OpenCode, and more)

Installation

pip install fdsx

Or with uv:

uv tool install fdsx

Quick Start

Create a simple YAML workflow file:

name: SimpleFlow
start_at: greet
version: "1.0"

states:
  greet:
    type: task
    provider: system
    command: "echo 'Hello from fdsx!'"
    result_path: $.message
    end: true

Run it:

fdsx run simple_flow.yaml

Feature Overview

State Types

• task — Execute LLM or CLI commands
• parallel — Run multiple branches concurrently
• choice — Conditional routing based on variables
• wait — Pause for human approval or webhook callback
• pass — Pass-through state for data transformation

Parallel Execution

Define parallel branches that execute simultaneously with aggregation strategies (majority vote, all, any).

Checkpoint & Resume

Flows automatically persist state. Resume from interruption with:

fdsx resume --thread-id <thread_id>

Batch Tasks

Process multiple tasks in batch mode:

fdsx run workflow.yaml --tasks tasks.md

Structured Logging

All execution details are logged to runs/<thread_id>.json.

Provider Support

Use any CLI-based LLM provider: Claude, Codex, OpenCode, or system commands.

CLI Reference

Command	Description
fdsx run <workflow.yaml>	Execute a workflow
fdsx run <workflow.yaml> --input key=value	Pass input variables
fdsx resume --thread-id <thread_id>	Resume from checkpoint
fdsx validate <workflow.yaml>	Validate YAML syntax
fdsx list	List recent runs

Example Workflow

name: Plan-Implement-Review Loop
start_at: plan
version: "1.0"
max_loop: 3

states:
  plan:
    type: task
    provider: claude
    model: claude-sonnet-4-6
    prompt_template: |
      You are a planning agent. Break down the following task into clear,
      actionable implementation steps.

      Task: {task}
    result_path: $.plan
    next: implement

  implement:
    type: task
    provider: opencode
    model: opencode/minimax-m2.5-free
    prompt_template: |
      You are an implementation agent. Follow this plan exactly.

      Plan: {plan}
    result_path: $.implementation
    next: review

  review:
    type: task
    provider: codex
    model: gpt-5.4
    prompt_template: |
      Review the implementation against the plan.

      Plan: {plan}
      Implementation: {implementation}
    result_path: $.review
    next: check_review

  check_review:
    type: choice
    choices:
      - variable: $.review
        operator: contains
        value: "APPROVED"
        next: done
    default: implement

  done:
    type: pass
    end: true

Run this example:

# First run in a new directory scaffolds .fdsx/ with example workflows:
fdsx run

# Then run the scaffolded example workflow:
fdsx run .fdsx/workflows/plan-implement-review/workflow.yaml --input task="Build a web calculator"

License

MIT License.