docs: clean up, split up, restructure README.md

Cargo.toml

@@ -8,7 +8,7 @@ edition = "2021"
 rust-version = "1.81"
 authors = ["Stephen Waits <steve@waits.net>"]
 license = "MIT"
-description = "A minimal, type-safe, runtime-agnostic async DAG executor with compile-time dependency validation"
+description = "A minimal, type-safe, runtime-agnostic async DAG (Directed Acyclic Graph) executor with compile-time cycle prevention and true parallel execution"
 repository = "https://github.com/swaits/dagx"
 homepage = "https://github.com/swaits/dagx"
 documentation = "https://docs.rs/dagx"

docs/COMPARISONS.md

@@ -0,0 +1,274 @@
+# Comparison with Similar Projects
+
+The Rust ecosystem offers several DAG execution libraries, each optimized for different use cases. This comparison helps you choose the right tool for your needs.
+
+## Developer Experience: API Comparison
+
+The same task (compute 2 + 3) implemented across different libraries shows how API complexity varies:
+
+**dagx** (Simple: macro + builder):
+
+```rust
+#[task]
+impl Value { async fn run(&self) -> i32 { self.0 } }
+
+#[task]
+impl Add { async fn run(a: &i32, b: &i32) -> i32 { a + b } }
+
+let dag = DagRunner::new();
+let x = dag.add_task(Value(2));
+let y = dag.add_task(Value(3));
+let sum = dag.add_task(Add).depends_on((&x, &y));
+dag.run(|fut| tokio::spawn(fut)).await?;
+```
+
+**dagrs** (Complex: traits + channels + IDs + manual wiring):
+
+```rust
+#[async_trait]
+impl Action for Value {
+    async fn run(&self, _: &mut InChannels, out: &mut OutChannels, _: Arc<EnvVar>) -> Output {
+        out.broadcast(Content::new(self.0)).await;
+        Output::Out(Some(Content::new(self.0)))
+    }
+}
+
+let mut table = NodeTable::new();
+let node1 = DefaultNode::with_action("x".into(), Value(2), &mut table);
+let id1 = node1.id();
+let node2 = DefaultNode::with_action("y".into(), Value(3), &mut table);
+let id2 = node2.id();
+let node3 = DefaultNode::with_action("add".into(), Add, &mut table);
+let id3 = node3.id();
+let mut graph = Graph::new();
+graph.add_node(node1); graph.add_node(node2); graph.add_node(node3);
+graph.add_edge(id1, vec![id3]); graph.add_edge(id2, vec![id3]);
+graph.set_env(EnvVar::new(table)); graph.start().unwrap();
+```
+
+**async_dag** (Medium: slots + indices):
+
+```rust
+let mut graph = Graph::new();
+let x = graph.add_task(|| async { 2 });
+let y = graph.add_task(|| async { 3 });
+let sum = graph.add_child_task(x, |a: i32| async move { a }, 0)?;
+graph.update_dependency(y, sum, 1)?;  // Must specify slot index
+let sum = graph.add_child_task(sum, |a: i32, b: i32| async move { a + b }, 0)?;
+graph.update_dependency(y, sum, 1)?;
+```
+
+**Key differences**:
+
+- **dagx**: Type-safe dependencies, automatic wiring, no manual ID tracking, minimal boilerplate
+- **dagrs**: Manual channel management, node ID tracking, Content wrapping, Action trait boilerplate
+- **async_dag**: Slot indices must be tracked manually, dependencies updated separately
+
+## Quick Comparison
+
+| Project                                                       | License        | Runtime             | Type Safety           | API Complexity | Performance vs dagx          | Key Features                                                                         |
+| ------------------------------------------------------------- | -------------- | ------------------- | --------------------- | -------------- | ---------------------------- | ------------------------------------------------------------------------------------ |
+| **dagx**                                                      | MIT            | Any async runtime   | Compile-time          | Simple         | Baseline (see benchmarks)    | Primitives-as-scheduler, inline fast-path, automatic Arc wrapping, up to 8 deps/task |
+| [**dagrs**](https://github.com/dagrs-dev/dagrs) (v0.5)        | MIT/Apache-2.0 | Creates own runtime | Runtime (async_trait) | Complex        | 1.04-129x slower (see below) | Flow-based Programming, cyclic graphs, loops, conditional nodes, YAML config         |
+| [**async_dag**](https://github.com/chubei-oppen/async_dag)    | MIT            | Any async runtime   | Compile-time          | Medium         | No benchmarks                | Slot-based dependencies, Graph/TryGraph modes, maximum parallelism                   |
+| [**dag-flow**](https://github.com/makisevon/dag-flow)         | MIT/Apache-2.0 | Any async runtime   | Runtime (HashMap)     | Complex        | No benchmarks                | Experimental, all tasks run simultaneously, weak dependencies                        |
+| [**RenovZ/dag-runner**](https://github.com/RenovZ/dag-runner) | MIT            | Tokio only          | Unclear               | Simple         | No benchmarks                | Edge-based API, cycle detection, stops on first error                                |
+| [**tasksitter**](https://github.com/lionkor/tasksitter)       | Unspecified    | Unclear             | Unclear               | Medium         | No benchmarks                | Cyclic graphs, dynamic runtime modification, pause/resume                            |
+
+_GitHub stars (as of 2025): dagrs (449), async_dag (25), dag-flow (2), others (0-2)_
+
+## Performance Benchmarks vs dagrs
+
+Direct comparison benchmarks (lower is better):
+
+| Workload          | Tasks  | dagx    | dagrs   | Speedup            |
+| ----------------- | ------ | ------- | ------- | ------------------ |
+| Sequential chain  | 5      | 3.0 µs  | 385 µs  | **129x faster** 🚀 |
+| Sequential chain  | 100    | 79 µs   | 703 µs  | **8.9x faster**    |
+| Diamond pattern   | 4      | 11 µs   | 387 µs  | **34x faster**     |
+| Fan-out (1→100)   | 101    | 155 µs  | 595 µs  | **3.85x faster**   |
+| Independent tasks | 10,000 | 12.7 ms | 13.3 ms | **1.04x faster**   |
+
+**Summary**: dagx is **1.04-129x faster** than dagrs across all benchmark patterns. The inline fast-path optimization eliminates spawning overhead for sequential workloads while maintaining excellent parallel performance.
+
+**How does dagx achieve this?** The inline fast-path detects single-task layers (common in sequential chains) and executes them directly without spawning overhead. For parallel workloads, tasks still spawn to maximize concurrency. This adaptive execution strategy combines the best of both worlds: sub-microsecond overhead for sequential work, true parallelism for concurrent work.
+
+**Key insight**: Most real-world DAGs mix sequential and parallel patterns. dagx automatically optimizes for both, delivering 1.04-129x better performance than dagrs regardless of workload shape.
+
+_Benchmarks run on AMD Ryzen 7 7840U (Zen 4) @ 3.3GHz. Run `cargo bench` to test on your hardware._
+
+## Detailed Comparison
+
+### dagrs (Most Mature)
+
+**Best for**: Complex workflows requiring advanced flow control, Tokio-based applications, machine learning pipelines.
+
+**Strengths**:
+
+- Most mature (449 GitHub stars, active community)
+- Rich feature set: Flow-based Programming, cyclic graphs, loops, conditional nodes
+- YAML configuration support for declarative workflows
+- Designed for complex orchestration patterns
+
+**Trade-offs**:
+
+- Creates own Tokio runtime internally (not runtime-agnostic, cannot be nested)
+- More complex API: `Action` trait, `InChannels`/`OutChannels`, `NodeTable`, `Content` wrappers, manual node ID tracking
+- Uses `async_trait` for type erasure (runtime overhead)
+- **1.04-129x slower than dagx** across all benchmark patterns (see comparison benchmarks)
+
+**API Style** (v0.5):
+
+```rust
+// Define action with channels for communication
+#[async_trait]
+impl Action for MyTask {
+    async fn run(&self, input: &mut InChannels,
+                 output: &mut OutChannels, env: Arc<EnvVar>) -> Output {
+        let inputs: Vec<T> = input.map(|c| ...).await;  // Manually extract
+        output.broadcast(Content::new(result)).await;   // Manually wrap
+        Output::Out(Some(Content::new(result)))
+    }
+}
+
+let mut table = NodeTable::new();
+let node = DefaultNode::with_action("name".into(), MyTask, &mut table);
+let node_id = node.id();  // Must capture ID before moving
+graph.add_node(node);
+graph.add_edge(source_id, vec![target_id]);
+graph.set_env(EnvVar::new(table)); graph.start().unwrap();
+```
+
+### async_dag (Clean Type Safety)
+
+**Best for**: Runtime flexibility with compile-time type safety, fail-fast workflows.
+
+**Strengths**:
+
+- Runtime-agnostic (works with any async runtime)
+- Compile-time type checking on task connections
+- Both standard (`Graph`) and fail-fast (`TryGraph`) modes
+- Designed for maximum parallelism
+
+**Trade-offs**:
+
+- Medium API complexity: slot-based dependency management
+- Must manually specify slot indices (0, 1, etc.) when connecting tasks
+- Less mature (25 stars)
+- No performance benchmarks
+
+**API Style**:
+
+```rust
+let mut graph = Graph::new();
+let _1 = graph.add_task(|| async { 1 });
+let _2 = graph.add_task(|| async { 2 });
+
+// add_child_task with slot index
+let _3 = graph.add_child_task(_1, sum, 0).unwrap();
+graph.update_dependency(_2, _3, 1).unwrap();  // Specify slot 1
+
+graph.run().await;
+```
+
+### dag-flow (Experimental)
+
+**Best for**: Experimental projects, flexible dependency awaiting patterns.
+
+**Strengths**:
+
+- Runtime-agnostic
+- All tasks run simultaneously (not in dependency layers)
+- Weak dependencies support
+- Flexible input awaiting at any point in task execution
+
+**Trade-offs**:
+
+- Explicitly experimental
+- Runtime type safety via `HashMap<String, Input>`
+- Complex API: implement `Task` trait with `id()`, `dependencies()`, `run()`
+- Named dependencies (string-based lookup)
+- Very early stage (2 stars)
+
+**API Style**:
+
+```rust
+impl Task<String, Bytes> for MyTask {
+    fn id(&self) -> String { "task_name".into() }
+    fn dependencies(&self) -> Option<Vec<String>> { Some(vec!["dep1".into()]) }
+
+    async fn run(&self, inputs: HashMap<String, Input<'_, Bytes>>) -> Option<Bytes> {
+        let dep_value = inputs.get("dep1").unwrap().await;
+        // Process
+    }
+}
+```
+
+### RenovZ/dag-runner (Simple Edge-Based)
+
+**Best for**: Simple DAGs with Tokio, straightforward edge-based dependencies.
+
+**Strengths**:
+
+- Simple API: `add_vertex()`, `add_edge()`
+- Cycle detection
+- Stops on first error
+
+**Trade-offs**:
+
+- Requires Tokio runtime
+- Manual channel setup for task communication
+- Type safety model unclear
+- Very early stage (0 stars)
+
+**API Style**:
+
+```rust
+let mut dag = Dag::default();
+dag.add_vertex("one", || async move { /* task */ });
+dag.add_edge("one", "two");
+dag.run().await?;
+```
+
+### tasksitter (Dynamic Workflows)
+
+**Best for**: Dynamic workflow modification, cyclic graphs, runtime introspection.
+
+**Strengths**:
+
+- Supports cyclic graphs (not just DAGs)
+- Dynamic graph modification at runtime
+- Pause/resume capabilities
+- Graph introspection
+
+**Trade-offs**:
+
+- Limited documentation
+- Runtime and type safety model unclear
+- Very early stage (0 stars)
+
+## When to Choose dagx
+
+Choose dagx when you value:
+
+- **Performance**: 1.04-129x faster than dagrs across all workload patterns (see benchmarks)
+- **Runtime flexibility**: Works with Tokio, async-std, smol, or any async runtime
+- **Compile-time safety**: Full type safety with no runtime type errors in the public API
+- **Minimal overhead**: ~790 ns per task (sequential), ~1.3 µs per task (parallel)
+- **Simple, ergonomic API**: `#[task]` macro, `add_task()`, `depends_on()` - that's it
+- **Automatic optimizations**: Arc wrapping, inline execution, adaptive spawning - all transparent
+- **Predictable performance**: Linear scaling, no hidden complexity, consistent sub-µs overhead
+
+dagx is **not** the right choice if you need:
+
+- Cyclic graphs or dynamic flow control (loops, conditions) → Consider **dagrs** or **tasksitter**
+- More than 8 dependencies per task → Consider **dagrs** or **async_dag**
+- YAML-based configuration → Consider **dagrs**
+
+## When to Consider Alternatives
+
+- **Choose dagrs** if you need advanced flow control (loops, conditionals, cyclic graphs), YAML configuration, or are already committed to Tokio and want a mature, feature-rich solution
+- **Choose async_dag** if you want compile-time type safety with runtime flexibility and the slot-based API appeals to you
+- **Choose dag-flow** if you're building experimental projects and the all-tasks-run-simultaneously model fits your use case
+- **Choose RenovZ/dag-runner** if you need the simplest possible edge-based API and are already using Tokio
+- **Choose tasksitter** if you need dynamic graph modification at runtime or cyclic workflow support

docs/CYCLE_PREVENTION.md

@@ -0,0 +1,82 @@
+# Compile-Time Cycle Prevention
+
+**dagx guarantees acyclic graphs at compile time with zero runtime overhead.** Unlike other DAG libraries that detect cycles at runtime, dagx uses Rust's type system to make cycles impossible to express in the first place.
+
+## Why This Matters
+
+Most DAG libraries check for cycles when you execute the graph:
+
+```rust
+// Other libraries: cycle detected at RUNTIME
+let dag = DagRunner::new();
+let a = dag.add_task(TaskA);
+let b = dag.add_task(TaskB);
+a.depends_on(b);
+b.depends_on(a);  // Compiles fine...
+dag.run().await?;  // ❌ Error: "Cycle detected!" (at runtime)
+```
+
+**dagx prevents this at compile time:**
+
+```rust
+// dagx: cycle prevented at COMPILE TIME
+let dag = DagRunner::new();
+let a_builder = dag.add_task(TaskA);
+let b_builder = dag.add_task(TaskB);
+
+// To make A depend on B, we need B as a TaskHandle.
+// But calling depends_on() consumes the builder!
+let b_handle = b_builder.depends_on(&some_source);
+
+// Now we can't make B depend on A because b_builder was moved!
+// This won't compile:
+// a_builder.depends_on(&b_handle);  // ✓ OK: A→B
+// b_handle.depends_on(&a_handle);   // ❌ ERROR: TaskHandle has no depends_on() method!
+```
+
+## How It Works: The Type-State Pattern
+
+dagx uses two types to enforce acyclic structure:
+
+1. **`TaskBuilder<T, Deps>`**: Mutable builder that can have dependencies added via `depends_on()`
+   - Consumed (moved) when you call `depends_on()` or convert to `TaskHandle`
+   - Can only be used once for wiring
+
+2. **`TaskHandle<T>`**: Immutable reference to a finalized task
+   - Copy type (just wraps a task ID)
+   - Can be used as a dependency for other tasks
+   - **Has no `depends_on()` method** - can't be modified after creation
+
+This creates an impossible ordering constraint for cycles:
+
+```rust
+// Attempting a cycle A→B→A:
+let a_builder = dag.add_task(TaskA);  // TaskBuilder
+let b_builder = dag.add_task(TaskB);  // TaskBuilder
+
+// To wire A→B, we need B as a TaskHandle
+let b_handle = b_builder.into();  // Convert B to TaskHandle (consumes builder)
+
+// To wire B→A, we need A as a TaskHandle
+let a_handle = a_builder.depends_on(&b_handle);  // A now depends on B
+
+// Now if we try to complete the cycle:
+// a_handle.depends_on(&...);  // ❌ ERROR: no method named `depends_on` found
+```
+
+**The type system enforces strict topological ordering!** You can't create a `TaskHandle` until all its dependencies exist as handles, which prevents cycles.
+
+## Proof & Examples
+
+See [`src/cycle_prevention.rs`](../src/cycle_prevention.rs) for comprehensive documentation with `compile_fail` tests proving various cycle attempts don't compile.
+
+See [`tests/cycle_prevention.rs`](../tests/cycle_prevention.rs) for runtime tests demonstrating the type-state pattern in action.
+
+## Benefits
+
+- ✅ **Zero runtime cost**: No cycle detection code to execute
+- ✅ **Catches errors early**: Cycles are caught at compile time, not in production
+- ✅ **Compiler-verified**: The type system proves your graph is acyclic
+- ✅ **No mental overhead**: You can't accidentally create cycles even if you try
+
+This is a true **zero-cost abstraction**—the type system provides safety guarantees without any runtime checks or performance penalty.

docs/DESIGN_PHILOSOPHY.md

@@ -0,0 +1,113 @@
+# Design Philosophy: Primitives as Scheduler
+
+dagx takes an unconventional approach to task orchestration: **there is no traditional scheduler**. Instead of complex scheduling logic managing when tasks run, the entire system is built on communication and synchronization primitives that do the scheduling themselves.
+
+## How It Works
+
+Traditional DAG executors contain substantial scheduling code—algorithms that track task states, manage dependencies, coordinate execution order, and handle synchronization. This code is complex, error-prone, and difficult to verify.
+
+dagx eliminates this entirely:
+
+1. **Wire up tasks with primitives**: During DAG construction, tasks are connected using channels and organized into topological layers based on their dependencies
+2. **Start everything simultaneously**: When you call `run()`, all tasks spawn at once—there's no scheduler deciding when each task should start
+3. **Let the primitives handle coordination**: Channels naturally enforce execution order—tasks wait on their input channels until upstream tasks send data. No custom orchestration needed.
+4. **Runtime joins on completion**: The runtime simply spawns all tasks and waits for the completion channel to close. That's it.
+
+## The Implementation
+
+Under the hood, dagx uses:
+
+- **Oneshot channels**: Each edge in the DAG gets a `futures::channel::oneshot` - producer sends once, consumer receives once
+- **Ownership model**: Tasks take ownership (`self`) and are consumed during execution - no Mutex needed for task state
+- **Direct data flow**: Outputs flow from producer to consumer via channels, never stored in shared memory during execution
+- **Fast-path optimization**: Single-task layers execute inline without spawning overhead
+
+The core execution logic in `run()` is remarkably simple:
+
+```rust
+// Create oneshot channel for each edge
+for (producer, consumer) in edges {
+    let (tx, rx) = oneshot::channel();
+    // Wire tx to producer, rx to consumer
+}
+
+// Execute layer by layer
+for layer in layers {
+    if layer.len() == 1 {
+        // Fast path: Execute inline, no spawning overhead
+        // IMPORTANT: Panic handling is required here!
+        let result = task.execute().catch_unwind().await;
+        // Convert panic to error to match spawned task behavior
+    } else {
+        // Parallel path: Spawn all tasks in layer
+        for task in layer {
+            spawner(async {
+                let input = await_on_input_channels();  // Blocks until deps complete
+                let output = task.run(input);           // Consumes task
+                send_to_output_channels(output);        // Sends to dependents
+            });
+        }
+        // Wait for layer completion
+    }
+}
+
+// Tasks coordinate themselves via channels - no scheduler needed
+```
+
+No state machines. No task queues. No wake-up logic. No Mutex locks. Just channels doing what channels do, with an inline fast-path for sequential execution.
+
+## Inline Execution Fast-Path
+
+**Performance optimization for sequential workloads**: When a layer contains only a single task (common in deep chains and linear pipelines), dagx executes it inline rather than spawning it. This eliminates spawning overhead, context switching, and channel creation, resulting in 10-100x performance improvements for sequential patterns.
+
+**Panic handling guarantee**: To maintain behavioral consistency between inline and spawned execution, panics in inline tasks are caught using `FutureExt::catch_unwind()` and converted to errors. This matches the behavior of all major async runtimes (Tokio, async-std, smol, embassy-rs), which catch panics in spawned tasks and convert them to `JoinError` or equivalent.
+
+**Why this matters**:
+
+- **Spawned tasks** (layer.len() > 1): Runtime catches panics automatically → becomes error
+- **Inline tasks** (layer.len() == 1): We catch panics manually → becomes error
+- **Result**: Identical behavior regardless of execution path
+
+This ensures your code behaves the same whether a task runs inline or spawned, making dagx's optimizations transparent and predictable.
+
+## Benefits
+
+**Simplicity**: The runtime is straightforward: create channels, spawn tasks, let them coordinate via awaiting. No complex scheduler code to maintain, debug, or optimize.
+
+**Reliability**: Built on battle-tested primitives (oneshot channels, async/await) from Rust's standard library and the futures crate. These have been used in production by thousands of projects and are orders of magnitude more reliable than custom scheduling logic.
+
+**Bug resistance**: Fewer moving parts means fewer places for bugs to hide. The type system enforces correct wiring at compile time. Channels handle synchronization. The ownership model prevents data races. What's left to break?
+
+**Performance**: Near zero-overhead. No Mutex locks during execution. Arc reference counting for efficient fanout (atomic operations, not locks). Oneshot channels are often lock-free. Tasks start as soon as their dependencies complete - maximum parallelism.
+
+**Auditability**: Want to verify correctness? Check the channel wiring, verify tasks await their inputs, done. No need to trace through complex state machine transitions or wake-up cascades.
+
+## The Insight
+
+The key insight is that **dependencies ARE the schedule**. If task B depends on task A's output, a channel naturally enforces that B waits for A. The dependency graph already encodes all the scheduling information—we just need to wire up channels to match it.
+
+This is dagx's core philosophy: leverage the type system for correctness, use primitives for coordination, and let the compiler optimize everything else away.
+
+## Measured Overhead
+
+How much overhead does this approach actually add? Benchmarks on an AMD 7840U (Zen 4 laptop CPU) show:
+
+**DAG Construction**:
+
+- Empty DAG creation: **~20 nanoseconds**
+- Adding tasks: **~100 nanoseconds per task**
+- Building a 10,000-task DAG: **~1.0 milliseconds** (100 ns/task)
+
+**Execution Overhead** (framework coordination, excluding actual task work):
+
+- Sequential workloads: **~790 nanoseconds per task** (inline execution fast-path)
+- Parallel workloads: **~1.3 microseconds per task**
+- 100-task sequential chain: **~79 microseconds total**
+- 100 independent tasks: **~122 microseconds total**
+- 10,000 independent tasks: **~12.7 milliseconds total**
+
+**Scaling**: Sub-microsecond per-task overhead across all workload patterns. Linear scaling verified to 10k+ tasks.
+
+**Comparison**: dagx is **1.04-129x faster** than dagrs (v0.5) across all benchmark patterns (see [comparisons](COMPARISONS.md)).
+
+The primitives-as-scheduler approach with inline fast-path optimization delivers exceptional performance: coordination overhead is sub-microsecond per task, and for real-world workloads where tasks do meaningful work (I/O, computation, etc.), framework overhead is negligible—typically well under 1% of total execution time.

docs/TRACING.md

@@ -0,0 +1,55 @@
+# Tracing Support
+
+dagx provides optional observability through the `tracing` crate with **zero runtime overhead when disabled**. The tracing instrumentation is conditionally compiled using feature flags, meaning when the feature is off, the logging code doesn't exist in the compiled binary at all.
+
+## Enabling Tracing
+
+```toml
+[dependencies]
+dagx = { version = "0.3", features = ["tracing"] }
+tracing-subscriber = "0.3"
+```
+
+## Usage Example
+
+```rust
+use tracing_subscriber::{fmt, EnvFilter};
+
+// Initialize tracing subscriber
+fmt()
+    .with_env_filter(
+        EnvFilter::try_from_default_env()
+            .unwrap_or_else(|_| EnvFilter::new("dagx=info"))
+    )
+    .init();
+
+// Your DAG code here - tracing will log execution details
+```
+
+## Log Levels
+
+- **INFO**: DAG execution start/completion
+- **DEBUG**: Task additions, dependency wiring, layer computation
+- **TRACE**: Individual task execution (inline vs spawned), detailed execution flow
+- **ERROR**: Task panics, concurrent execution attempts
+
+Run with different levels:
+
+```bash
+RUST_LOG=dagx=info  cargo run    # High-level execution info
+RUST_LOG=dagx=debug cargo run    # Task and layer details
+RUST_LOG=dagx=trace cargo run    # All execution details
+```
+
+See [`examples/tracing_example.rs`](../examples/tracing_example.rs) for a complete working example.
+
+## Zero-Cost Guarantee
+
+When the `tracing` feature is disabled (the default), there is **literally 0ns overhead**:
+
+- Logging code is removed at compile time via `#[cfg(feature = "tracing")]`
+- No branches, no function calls, nothing
+- The `tracing` crate isn't even linked
+- Benchmarks verify identical performance with/without the feature
+
+This follows the same pattern used by Tokio, Hyper, and other performance-critical Rust async libraries.

src/runner.rs

@@ -238,7 +238,13 @@ impl DagRunner {
     ///
     /// # Errors
     ///
-    /// Returns `DagError::CycleDetected` if the DAG contains a cycle.
+    /// - Returns `DagError::ConcurrentExecution` if the DAG is already executing
+    /// - Returns `DagError::TaskPanicked` if any task panics during execution
+    ///
+    /// # Note on Cycles
+    ///
+    /// Cycles are impossible via the public API due to compile-time prevention.
+    /// See the [`crate::cycle_prevention`] module for details.
     ///
     /// # Examples
     ///