random-agent
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@random-agentOrchestrate: research best practices for API design"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Random Agent
A powerful MCP server for multi-worker autonomous agent orchestration. Decompose complex tasks, run parallel workers, auto-review, and generate follow-up tasks — all through the Model Context Protocol.
Features
Parallel Workers — Run up to 5 concurrent AI agent processes
Task Decomposition — Auto-split complex tasks into subtasks with dependencies
Auto-Review & Reflection — Coordinator generates reviews and reflections on completed tasks
Follow-up Generation — Reflections auto-create new tasks for continuous improvement
22 Assertion Types — Validate outputs with equals, contains, regex, file-exists, json-path, and more
Stall Detection — Detect stuck workers, kill them, and auto-retry
Real-time Monitoring — Live status dashboard with queue counts and worker states
Metrics Collection — Track scores, issues, and patterns over time
Related MCP server: A2A Client MCP Server
Installation
git clone https://github.com/randomchips/random_agent.git
cd random_agent
npm install
npm run buildUsage
Add to your OpenCode config (~/.config/opencode/opencode.jsonc):
{
"mcp": {
"random-agent": {
"type": "local",
"command": ["node", "/path/to/random_agent/dist/index.js"],
"enabled": true,
"environment": {
"AGENT_OS_BASE_PATH": "C:\\agent-os"
}
}
}
}Tools
Tool | Description |
| Queue a task with name, command, priority, and optional assertions |
| Auto-decompose complex task → inject subtasks → monitor → return results |
| Live view: coordinator state, queue counts, active workers |
| Start / stop / restart the background coordinator loop |
| Detect stuck workers, kill them, auto-retry failed tasks |
| Run assertions on task output (22 assertion types) |
| Read coordinator logs, filter by search term or worker ID |
| Read metrics database (scores, issues, patterns) |
Pipeline Stages
inject → pending → in-progress → completed → review → reflection → follow-upInject — Task queued in
task-queue/pending/Worker — Spawns AI process, executes command
Completed — Task moved to
task-queue/completed/Review — AI generates review saved to
reviews/Reflection — AI generates reflection saved to
reflections/Follow-up — New tasks auto-generated for next cycle
Configuration
Environment variables:
Variable | Default | Description |
|
| Base path for all agent data |
|
| Max concurrent worker processes |
|
| Coordinator loop interval (seconds) |
|
| Seconds before worker is considered stuck |
Project Structure
random_agent/
├── src/
│ ├── index.ts # MCP server entry point
│ ├── types.ts # TypeScript type definitions
│ ├── services/
│ │ ├── assertions.ts # Assertion engine (22 types)
│ │ ├── coordinator.ts # Coordinator lifecycle
│ │ ├── decomposer.ts # Task decomposition
│ │ ├── file-reader.ts # File system operations
│ │ └── worker-monitor.ts # Worker health monitoring
│ └── tools/
│ ├── assert.ts # Assert tool
│ ├── handle-stuck.ts # Handle stuck workers
│ ├── inject.ts # Inject tasks
│ ├── lifecycle.ts # Coordinator control
│ ├── logs.ts # Read logs
│ ├── metrics.ts # Read metrics
│ ├── orchestrate.ts # Full orchestration
│ └── status.ts # System status
├── package.json
├── tsconfig.json
└── README.mdLicense
MIT
Contributing
Contributions welcome! Open an issue or submit a PR at github.com/randomchips/random_agent.
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