Sequencer: Visual AI Agent Workflow Orchestrator (MAC only, cross platform capabilities coming soon)

Sequencer is a local-first workflow orchestrator for chaining prompts, agents, and OpenAI-compatible LLM calls into repeatable pipelines.

🚀 Core Concept: From Chatting to Sequencing

Most developers use AI agents (like Cline or Aider) in a linear chat. Sequencer moves you to an assembly line model:

1. Design: Create a sequence of tasks (prompts).
2. Assign: Choose the best agent for each specific task.
3. Start Sequence: Execute the entire pipeline in one click, with real-time status tracking for every step.

✨ Key Features

• Visual Pipeline Editor: Drag-and-drop interface to reorder tasks and refine your workflow.
• Multi-Agent Coordination: Assign different agents to different steps within a single project sequence.
• Hybrid LLM Support: Route requests through local servers (LM Studio) for privacy and cost, or connect to enterprise APIs for maximum intelligence.
• Real-time Orchestration: Monitor the state of your pipeline (Pending, Running, Done/Failed) as it executes.
• Project-Based Management: Organize different sequences into dedicated projects.
• Transparent Logging: Every exchange is captured in JSON format for audit and optimization.

Why I Built This

I found myself manually coordinating workflows between LM Studio, coding agents, local models, and scripts. Repeating the same multi-step AI tasks became tedious.

Sequencer is my attempt to turn those workflows into autonomous pipelines that work across both local and cloud-based models.

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📋 Table of Contents

• Installation
• Quick Start
• Docker Deployment
• Configuration
• Using the Sequencer
• OpenClaw Integration
• API Reference
• Project Structure
• Development
• Future Roadmap

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🛠️ Installation

Prerequisites

• Node.js 18+ and npm
• (Optional) Cline CLI for Cline agent tasks
• (Optional) Aider CLI for Aider agent tasks

Setup

1. Clone the repository:

   git clone https://github.com/mdoty4/sequencer.git
   cd sequencer

2. Install dependencies:

   npm install

3. Configure environment variables: Create a .env file in the root directory:

   PORT=4321
   LM_STUDIO_URL=http://localhost:1234/v1

4. Start the server:

   npm start

   The server will start on http://localhost:4321.

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🐳 Docker Deployment

Run Sequencer in a container with a single command:

docker compose up --build -d

The server will be available at http://localhost:4321.

Docker Notes

• Logs are persisted in the ./logs directory on the host
• The .env file is mounted read-only into the container
• A health check is configured at /health
• Agent CLI tools (cline, aider) must be available inside the container for agent tasks to execute. For UI-only usage the container works as-is.

Docker Commands

# Start in background
docker compose up -d

# Stop
docker compose down

# Rebuild and start
docker compose up --build -d

# View logs
docker compose logs -f

# Remove container and volumes
docker compose down -v

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⚙️ Configuration

Environment Variables

Variable	Default	Description
PORT	4321	Port the Sequencer server listens on
LM_STUDIO_URL	http://localhost:1234/v1	Base URL for LM Studio API

Agent Configuration

Configure your agents through the web UI at Settings:

• LLM Settings: API base URL, API key, model selection
• Telegram: Bot token and chat ID for Telegram agent
• Per-project overrides: Each project can have its own LLM configuration

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🎮 Using the Sequencer

1. Setting up your Agent (e.g., Cline)

To route an agent's requests through the Sequencer:

• Set the API Provider to OpenAI Compatible
• Set the Base URL to http://localhost:4321/

2. Building a Pipeline

• Navigate to the Projects tab and activate a project
• In the Pipeline editor, add prompt rows
• Assign an agent (Aider or Cline) to each row
• Toggle the "Orchestrate" switch for the tasks you want to include in the sequence

3. Executing the Sequence

Click ▶ Start Sequence. Sequencer will execute the selected prompts in order, managing the hand-off between agents and tracking progress in real-time.

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🤖 OpenClaw Integration

Sequencer includes a skill.md file that allows OpenClaw (and other AI agents) to discover and interact with Sequencer automatically. By providing the skill file, OpenClaw can:

• Start, stop, and restart the Sequencer server
• Create and manage projects and pipelines
• Assign agents and execute orchestration workflows
• Troubleshoot failed pipelines and review session logs

To use with OpenClaw, simply point it to the skill.md file in the project root. OpenClaw will use the defined workflows and API endpoints to control Sequencer programmatically.

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🔌 API Reference

Base URL: http://localhost:4321

Health Check

Method	Endpoint	Description
GET	/health	Server health status with uptime and version

Response:

{
  "status": "ok",
  "uptime": 1234.56,
  "timestamp": "2025-04-27T20:00:00.000Z",
  "version": "1.0.0"
}

Project Management

Method	Endpoint	Description
GET	/api/projects	List all projects and active project
POST	/api/projects	Create a new project
PUT	/api/projects/:id	Update an existing project
DELETE	/api/projects/:id	Delete a project
POST	/api/projects/active	Set the active project

Create Project Request:

{
  "name": "My Project",
  "workingDirectory": "../my-project",
  "aiderConfig": { }
}

Task Management

Method	Endpoint	Description
GET	/api/project/:id/tasks	Get tasks for a project
POST	/api/project/:id/tasks	Update tasks for a project
POST	/api/project/:id/tasks/orchestrate	Start orchestration with selected tasks
POST	/api/project/:id/tasks/reset	Reset all task states to pending
POST	/api/project/:id/tasks/cancel	Cancel running orchestration
GET	/api/project/:id/tasks/stream	SSE stream for real-time orchestration events

Orchestrate Request:

{
  "taskIndices": [0, 1, 2]
}

Agent Triggers

Method	Endpoint	Description
POST	/api/project/:id/tasks/:taskIndex/send	Send task to configured agent
POST	/api/project/:id/tasks/:taskIndex/aider	Send task specifically to Aider
POST	/api/project/:id/tasks/:taskIndex/init	Initialize git in working directory

Chat & LLM

Method	Endpoint	Description
POST	/api/chat	Stream a response from the configured LLM (SSE)
POST	/api/cline/headless	Run Cline CLI in headless mode with streaming (SSE)

Configuration

Method	Endpoint	Description
GET	/api/config	Get current Aider and Telegram config
POST	/api/config	Save Aider and Telegram config
POST	/api/telegram/test	Send a test message via Telegram

Logs

Method	Endpoint	Description
GET	/api/logs	List all log sessions
GET	/api/logs/:id	Get events for a specific log session
DELETE	/api/logs/:id	Delete a specific log
POST	/api/logs/bulk-delete	Delete multiple logs at once

Proxy Status

Method	Endpoint	Description
GET	/api/proxy/status	Check if LM Studio is reachable
GET	/api/status	Check proxy/LM Studio status

LLM Proxy

Method	Endpoint	Description
POST	/v1/chat/completions	Proxy for OpenAI-compatible chat completions

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🗺️ Project Structure

sequencer/
├── sequencer.js          # Main server: Express routes, agent orchestration, execution engine
├── app.js                # Additional app logic
├── skill.md              # OpenClaw skill file for agent integration
├── index.html            # Frontend entry point
├── styles.css            # Application styles
├── prompts.json          # Project state, tasks, and configuration storage
├── .env                  # Environment variables (PORT, LM_STUDIO_URL)
├── Dockerfile            # Docker image definition
├── docker-compose.yml    # Docker Compose configuration
├── modules/              # Frontend JavaScript modules
│   ├── chat.js           # Chat interface logic
│   ├── core.js           # Core system operations
│   ├── dom-helpers.js    # DOM manipulation utilities
│   ├── json-viewer.js    # JSON log viewer
│   ├── pipeline.js       # Visual pipeline editor
│   ├── project-editor.js # Project editing UI
│   ├── projects.js       # Project management
│   ├── search.js         # Search functionality
│   ├── sessions.js       # Session management
│   ├── settings.js       # Settings panel
│   └── terminal.js       # Terminal output display
└── logs/                 # Agent exchange logs (JSONL format)

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👩‍💻 Development

Development Mode

Auto-restart on code changes with nodemon:

npm run dev

File Format

• prompts.json: Stores all projects, tasks, agent config, and execution state
• logs/*.json: Agent session logs in JSONL format (one JSON object per line)

Session ID Format

Session logs follow the pattern:

{projectTitle}_{agentName}_task_{taskIndex}_{timestamp}.json

Example: testbench_cline_task_0_2026-04-27T02-22-13.json

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🎯 Future Roadmap

• Enterprise Gateway: Native support for OpenAI, Anthropic, and Azure API keys.
• Template Library: Shareable "Golden Pipelines" for common development tasks.
• Conditional Logic: Branching pipelines based on the output of a previous step.
• MCP Server: Expose Sequencer capabilities as MCP tools for AI agents.

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📄 License

MIT - See LICENSE for details.

👤 Author

Michael Doty

• Email: michaeldoty.pro@gmail.com
• GitHub: mdoty4