sortie-mcp

# 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 ```bash pip install sortie-mcp ``` Or with [uv](https://docs.astral.sh/uv/): ```bash uv add sortie-mcp ``` ## Quick Start ### 1. Set up PostgreSQL sortie-mcp requires PostgreSQL 15+ with [pgvector](https://github.com/pgvector/pgvector). ```bash export DATABASE_URL="postgresql://user:pass@localhost:5432/mydb" export SORTIE_SCHEMA="sortie" # default ``` ### 2. Run the MCP server ```bash sortie-mcp # or: python -m sortie_mcp.server ``` The server runs on stdio transport. Configure it in your MCP client: ```json { "sortie": { "command": ["sortie-mcp"], "env": { "DATABASE_URL": "postgresql://..." } } } ``` ### 3. Run the campaign runner ```bash 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 | Type | Description | |------|-------------| | `atomic` | Single task executed by one agent | | `parallel_group` | Fan-out: children run concurrently | | `sequence` | Pipeline: each step depends on the previous | | `for_each` | Map: apply a template to each item in a list | ### 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 …