Dagger

Multi-paradigm workflow orchestration library for Rust. Execute YAML-defined DAGs, manage dynamic task graphs, or build event-driven systems with SQLite persistence and parallel execution.

Installation

[dependencies]
dagger = { path = "path/to/dagger" }
tokio = { version = "1.36", features = ["full"] }
serde_json = "1.0"
anyhow = "1.0"
sqlx = { version = "0.8.6", features = ["runtime-tokio-rustls", "sqlite"] }

Quick Start

use dagger::{action, coord::ActionRegistry, Cache, DagExecutor};
use serde_json::json;

#[tokio::main]
async fn main() -> anyhow::Result<()> {
    // Initialize (macro-registered actions are loaded here)
    let registry = ActionRegistry::new();
    let mut executor = DagExecutor::new(None, registry, "sqlite::memory:").await?;
    
    // Load workflow
    executor.load_yaml_file("workflow.yaml").await?;
    
    // Execute
    let cache = Cache::new();
    let (_tx, rx) = tokio::sync::oneshot::channel();
    let report = executor.execute_static_dag("pipeline", &cache, rx).await?;
    
    println!("Completed: {}", report.success);
    Ok(())
}

#[action(name = "process")]
async fn process_data(input: serde_json::Value) -> anyhow::Result<serde_json::Value> {
    Ok(json!({ "status": "ok", "input": input }))
}

Three Execution Paradigms

1. DAG Flow

Static workflows defined in YAML with automatic dependency resolution and parallel execution.

name: pipeline
nodes:
  - id: fetch
    action: fetch_data
  - id: process
    action: process_data
    dependencies: [fetch]

2. Task Agent

Dynamic task graphs with runtime dependency creation and agent-based execution.

#[dagger::task_agent]
async fn analyze(task: Task) -> anyhow::Result<serde_json::Value> {
    // Create subtasks dynamically based on input
    Ok(json!({ "status": "processing" }))
}

3. Pub/Sub

Event-driven communication between decoupled agents.

#[dagger::pubsub_agent(subscribe = "events", publish = "results")]
async fn handler(msg: Message) -> anyhow::Result<()> {
    // Process and publish
    Ok(())
}

Key Features

• Parallel Execution: Automatic parallelization of independent nodes
• SQLite Persistence: ACID-compliant storage with compression
• Send-Compatible: Works with Tauri and cross-thread async contexts
• Retry Logic: Configurable retry strategies with exponential backoff
• Visual Debugging: Export execution graphs as DOT files

Architecture

The library uses a Coordinator pattern for parallel execution without borrow checker issues:

• Workers: Execute nodes in parallel without mutable access to executor
• Coordinator: Single point for state mutations
• Message Passing: Event-driven communication via channels

Storage uses SQLite with zstd compression for 3-10x size reduction.

Examples

Working examples in examples/:

• dag_flow_basic.rs - YAML workflow execution with #[action] (examples/fixtures/basic_pipeline.yaml)
• dag_flow_pipeline.rs - End-to-end order processing pipeline (examples/fixtures/order_pipeline.yaml)
• dag_flow_cli.rs - CLI runner for multiple YAML DAGs (examples/fixtures/pipeline*.yaml)
• dag_flow_dot.rs - Generate DOT graph for visualization (Graphviz)
• task_agent_basic.rs - Task Agent mode with #[task_agent]
• pubsub_basic.rs - Pub/Sub mode with #[pubsub_agent]
• dynamic_nodes_demo.rs - Dynamic DAG growth
• coordinator_demo.rs - Coordinator-based execution

Run with:

cargo run --example dag_flow_basic

Sample outputs and notes on nondeterminism: examples/README.md.

Documentation

• Architecture - System design and components
• DAG Flow Guide - YAML workflows
• Task Agent Guide - Dynamic tasks
• Pub/Sub Guide - Event-driven agents
• Upgrading / Adoption Notes - Downstream migration checklist

Recent Changes

This snapshot standardizes on macro-based registration for all three modes. If you are upgrading from earlier versions, read the adoption notes in docs/UPGRADING.md.

License

MIT