# Xynthe
[](https://crates.io/crates/xynthe)
[](https://docs.rs/xynthe)
[](LICENSE)
**Xynthe is a unified orchestration framework and persistent human interface for autonomous intelligence systems.**
Xynthe bridges two major gaps in contemporary AI:
1. **The orchestration gap** — modern AI systems are fragmented across disconnected components
2. **The human interface gap** — humans need persistent, transparent, controllable layers for collaboration with autonomous agents
## Overview
Xynthe provides a **cognitive-execution substrate** where intelligence is modeled as orchestrated continuity rather than isolated calls. It enables long-horizon execution, multi-agent coordination, temporal memory, deterministic traceability, and bounded autonomy, while remaining anchored to human intent.
### Core Statement
> **AGI made intelligence general.**
> **GIAM made intelligence hierarchical.**
> **Xynthe makes intelligence usable.**
## Features
### Core Primitives
#### Thought Streams
Typed channels carrying structured cognitive events that enable agents to maintain multiple reasoning threads simultaneously.
```rust
use xynthe::prelude::*;
let stream = ThoughtStream::new("observations");
let event = ThoughtEvent::observation(
StructuredContent::text("User requested weather information"),
ProvenanceChain::new(),
);
stream.emit(event)?;
```
#### Capability Bindings
Type-safe interfaces that formalize tool integration through formal contracts specifying preconditions, effects, failure semantics, and reflection hooks.
```rust
let registry = CapabilityRegistry::new();
// Register and invoke capabilities with full type safety
let result = registry.invoke("weather_api", input).await?;
```
#### Context Fabrics
Temporally aware memory substrates that provide multi-layered context with asynchronous updates and unified query interfaces.
```rust
let fabric = ContextFabric::new();
// Store events with temporal awareness
fabric.store(event, ContextLayer::Working).await?;
// Query across time windows
let results = fabric.recall("user_requests", window).await?;
```
### GIAM Alignment
Xynthe is the operational interface to the **General Intelligence Ascension Model (GIAM)**, providing execution and oversight across intelligence hierarchies:
| AGI | Intent Layer | Intent interpretation and translation |
| SI | Execution Kernel | Task lifecycle and scheduling |
| UI | Persistent Memory | Cross-session continuity |
| HI | Adaptive Skills | Self-improving execution logic |
| SPI | Subagent Network | Multi-agent coordination |
| ULI | Optimization Engine | Predictive execution and resource allocation |
| TI | Universal Fabric | Cross-substrate execution |
## Installation
Add this to your `Cargo.toml`:
```toml
[dependencies]
xynthe = "0.1"
```
## Usage
### Basic Example: Creating an Execution Engine
```rust
use xynthe::prelude::*;
use std::sync::Arc;
#[tokio::main]
async fn main() -> Result<()> {
// Initialize core components
let thought_stream = Arc::new(ThoughtStream::new("main"));
let capability_registry = Arc::new(CapabilityRegistry::new());
let context_fabric = Arc::new(ContextFabric::new());
// Create the execution engine
let engine = ExecutionEngine::new(
thought_stream,
capability_registry,
context_fabric,
);
// Create an intent and execute it
let intent = ExecutionIntent::new(
"Process user request",
StructuredContent::json(serde_json::json!({
"request_type": "weather",
"location": "San Francisco"
})),
);
let result = engine.execute(intent).await?;
println!("Execution completed: {}", result.success);
Ok(())
}
```
### Advanced Example: Multi-Agent Coordination
```rust
// Create specialized thought streams for different agents
let planning_stream = ThoughtStream::new("planner");
let execution_stream = ThoughtStream::new("executor");
// Merge streams for coordinated reasoning
let merged = ThoughtStream::merge(vec![
planning_stream,
execution_stream,
]);
// Query context for shared knowledge
let query = ContextQuery::recall("action_plan", window)
.with_min_confidence(0.8);
let context = context_fabric.query(query).await?;
```
## Architecture
### The G-TEL Execution Loop
Xynthe agents operate through the **GIAM-Aligned Temporal Execution Loop (G-TEL)**:
1. **Perceive** - Intent ingestion and observation
2. **Reason** - Goal decomposition and hypothesis generation
3. **Assign** - Layer assignment based on complexity
4. **Act** - Capability invocation with trace recording
5. **Reflect** - Outcome evaluation and trust updates
6. **Adapt** - Strategy refinement and optimization
7. **Continue** - Persistent execution across time
### Safety Model
Safety in Xynthe is architectural:
- **Constraint Propagation** - Constraints flow through execution graphs
- **Approval Boundaries** - Explicit autonomy thresholds for critical actions
- **Immutable Traces** - Every execution produces an audit trail
- **Value Reflection** - Periodic alignment checks against declared values
- **Type Safety** - Linear types and bounded resource usage
- **Sandboxed Execution** - Isolated environments with strict permissions
## Documentation
- [API Documentation](https://docs.rs/xynthe)
- [Specification](https://github.com/xynthe/xynthe-spec)
- [Examples](./examples)
## Contributing
Contributions are welcome! Please read our [Contributing Guide](CONTRIBUTING.md) for details on our code of conduct and the process for submitting pull requests.
## License
This project is licensed under the Dust Open Source License v1.0 Intelligence Infrastructure Edition (DOSL-IIE-1.0) - see the [LICENSE](LICENSE) file for details.
## Acknowledgments
Xynthe is the connective tissue between cognition, memory, action, safety, and human control. It is where orchestration becomes cognition, autonomy becomes governable, and humans meet the future of execution.