sortie-mcp

Campaign orchestration MCP server for AI agents — dependency DAGs, parallel fan-out, failure policies, and embedded notes.

Think make for AI agent workflows, where the LLM is the planner that generates and adapts the DAG at runtime.

Install

pip install sortie-mcp

Or with uv:

uv add sortie-mcp

Related MCP server: task-orchestrator

Quick Start

1. Set up PostgreSQL

sortie-mcp requires PostgreSQL 15+ with pgvector.

export DATABASE_URL="postgresql://user:pass@localhost:5432/mydb"
export SORTIE_SCHEMA="sortie"  # default

2. Run the MCP server

sortie-mcp
# or: python -m sortie_mcp.server

The server runs on stdio transport. Configure it in your MCP client:

{
  "sortie": {
    "command": ["sortie-mcp"],
    "env": {
      "DATABASE_URL": "postgresql://..."
    }
  }
}

3. Run the campaign runner

sortie-runner
# or: python -m sortie_mcp.runner

Add to cron for autonomous operation:

*/15 * * * * /path/to/venv/bin/sortie-runner

Architecture

One MCP server, three perspectives:

• Coordinator (e.g. a dispatcher agent): create, list, steer, pause/cancel campaigns

• Worker (specialist agents): get context, add notes, complete/fail steps, spawn subtasks

• Runner (cron): capacity-aware watchdog that dispatches ready steps and consults the planner LLM

Step Types

Key Features

• DAG splice (spawn_and_continue): agents can split work into subtasks + continuation

• Branch abort (abort_branch): scoped early return from an ancestor step

• Skip cascade: transitive propagation through the dependency graph

• Priority scheduling: urgent / high / normal / low / background

• Advisory dedup: fingerprinting warns the planner of duplicate steps

• Depth limits: spawn_and_continue hidden from agents at max depth

• Embedded notes: pgvector semantic search across campaign findings

Configuration

Development

uv sync
uv run pytest
uv run ruff check .
uv run mypy src tests

License

GPL-3.0-or-later. See LICENSE.