sortie-mcp
Campaign orchestration MCP server for AI agents — dependency DAGs, parallel fan-out, failure policies, and embedded notes.
README
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
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
| 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 scheduling: urgent / high / normal / low / background
- Advisory dedup: fingerprinting warns the planner of duplicate steps
- Depth limits:
spawn_and_continuehidden from agents at max depth - Embedded notes: pgvector semantic search across campaign findings
Configuration
| Env Var | Default | Description |
|---|---|---|
DATABASE_URL |
postgresql://localhost/sortie |
PostgreSQL connection string |
SORTIE_SCHEMA |
sortie |
Database schema name |
SORTIE_MAX_CONCURRENT |
4 |
Max parallel running steps |
SORTIE_ZOMBIE_TIMEOUT |
30 |
Minutes before a stuck step is reset |
LITELLM_URL |
http://localhost:4000 |
LiteLLM proxy URL (for planner) |
LITELLM_KEY |
LiteLLM API key | |
SORTIE_PLANNER_MODEL |
qwen3.5:9b |
Model for the planner LLM |
OPENCLAW_RUNTIME_URL |
http://localhost:3000 |
Agent runtime API |
Development
uv sync
uv run pytest
uv run ruff check .
uv run mypy src tests
License
GPL-3.0-or-later. See LICENSE.
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