<div align="center">
<img src="https://raw.githubusercontent.com/nassor/cano/main/docs/static/logo.png" alt="Cano Logo" width="200">
<h1>Cano: Type-Safe Async Workflow Engine</h1>
[](https://crates.io/crates/cano)
[](https://docs.rs/cano)
[](https://nassor.github.io/cano/)
[](https://crates.io/crates/cano)
[](https://github.com/nassor/cano/blob/main/LICENSE)
[](https://github.com/nassor/cano/actions)
[](https://www.rust-lang.org)
<em>**Orchestrate complex async processes with finite state machines, parallel execution, and built-in scheduling.**</em>
<em>Cano is still far from a 1.0 release. The API is subject to changes and may include breaking changes.</em>
</div>
# Overview
Cano is a high-performance orchestration engine designed for building resilient, self-healing systems in Rust. Unlike simple task queues, Cano uses **Finite State Machines (FSM)** to define strict, type-safe transitions between processing steps.
It excels at managing complex lifecycles where state transitions matter:
* **Data Pipelines**: ETL jobs with parallel processing (Split/Join) and aggregation.
* **AI Agents**: Multi-step inference chains with shared context and memory.
* **Background Systems**: Scheduled maintenance, periodic reporting, and distributed cron jobs.
The engine is built on three core concepts: **Tasks/Nodes** for logic, **Workflows** for state transitions, and **Schedulers** for timing.
## Features
- **Type-Safe State Machines**: Enum-driven transitions with compile-time guarantees.
- **Flexible Processing Units**: Choose between simple `Task`s or structured `Node`s (Prep/Exec/Post lifecycle).
- **Parallel Execution (Split/Join)**: Run tasks concurrently and join results with strategies like `All`, `Any`, `Quorum`, or `PartialResults`.
- **Robust Retry Logic**: Configurable strategies including exponential backoff with jitter and per-attempt timeouts.
- **Circuit Breaker**: Shared `CircuitBreaker` short-circuits calls to failing dependencies before the retry loop, with configurable failure threshold, cool-down, and half-open probing.
- **Built-in Scheduling**: Cron-based, interval, and manual triggers for background jobs.
- **Observability**: Integrated `tracing` support for deep insights into workflow execution.
- **Performance-Focused**: Minimizes heap allocations by leveraging stack-based objects wherever possible, giving you control over where allocations occur.
## Simple Example: Parallel Processing
Here is a real-world example showing how to split execution into parallel tasks and join them back together.
```mermaid
graph TD
Start([Start]) --> Split{Split}
Split -->|Source 1| T1[FetchSourceTask 1]
Split -->|Source 2| T2[FetchSourceTask 2]
Split -->|Source 3| T3[FetchSourceTask 3]
T1 --> Join{Join All}
T2 --> Join
T3 --> Join
Join --> Complete([Complete])
```
```rust
use cano::prelude::*;
use std::time::Duration;
#[derive(Debug, Clone, PartialEq, Eq, Hash)]
enum FlowState {
Start,
Complete,
}
// A task that simulates fetching data from a source.
#[derive(Clone)]
struct FetchSourceTask {
source_id: u32,
}
#[task]
impl Task<FlowState> for FetchSourceTask {
async fn run(&self, res: &Resources) -> Result<TaskResult<FlowState>, CanoError> {
// Look up the shared store from the workflow's resources.
let store = res.get::<MemoryStore, str>("store")?;
// Simulate async work.
tokio::time::sleep(Duration::from_millis(100)).await;
// Store the per-source result for downstream aggregation.
let key = format!("source_{}", self.source_id);
store.put(&key, format!("data_from_{}", self.source_id))?;
Ok(TaskResult::Single(FlowState::Complete))
}
}
#[tokio::main]
async fn main() -> Result<(), CanoError> {
// 1. Register shared resources (the store is one resource among many).
let resources = Resources::new().insert("store", MemoryStore::new());
// 2. Define parallel tasks.
let sources = vec![
FetchSourceTask { source_id: 1 },
FetchSourceTask { source_id: 2 },
FetchSourceTask { source_id: 3 },
];
// 3. Configure the join strategy.
// Wait for ALL tasks to complete successfully before moving to Complete.
let join_config = JoinConfig::new(JoinStrategy::All, FlowState::Complete)
.with_timeout(Duration::from_secs(5));
// 4. Build the workflow: Start -> Split into parallel tasks -> Complete.
let workflow = Workflow::new(resources)
.register_split(FlowState::Start, sources, join_config)
.add_exit_state(FlowState::Complete);
// 5. Run.
let result = workflow.orchestrate(FlowState::Start).await?;
println!("Workflow finished: {:?}", result);
Ok(())
}
```
## Documentation
For complete documentation, examples, and guides, please visit our website:
👉 **[https://nassor.github.io/cano/](https://nassor.github.io/cano/)**
You can also find:
- **[API Documentation](https://docs.rs/cano)** on docs.rs
- **[Examples Directory](./examples/)** in the repository
## Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
## License
Licensed under either of
- Apache License, Version 2.0 ([LICENSE-APACHE](LICENSE-APACHE) or <http://www.apache.org/licenses/LICENSE-2.0>)
- MIT license ([LICENSE-MIT](LICENSE-MIT) or <http://opensource.org/licenses/MIT>)
at your option.
### Contribution
Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.