geny-harness 0.5.0

Rust-powered agent pipeline library — drop-in replacement for geny-executor

geny-harness

Rust-core Python library — A drop-in replacement for geny-executor, providing the same 16-stage dual-abstraction agent pipeline architecture.

pip install geny-harness

geny-executor vs geny-harness: What's Different?

Both libraries provide identical Python APIs — the same classes, same methods, same import paths. You can switch between them by changing a single import. The difference is where the code runs.

Architecture Comparison

geny-executor (v0.3.0)                    geny-harness (v0.3.0)
┌─────────────────────────┐               ┌─────────────────────────┐
│     Python Application  │               │     Python Application  │
├─────────────────────────┤               ├─────────────────────────┤
│  Pipeline Orchestration │  Python       │  Pipeline Orchestration │  Python
│  16 Stage Execution     │               │  16 Stage Execution     │
│  EventBus / Session     │               │  EventBus / Session     │
├─────────────────────────┤               ├─────────────────────────┤
│  PipelineState (40+fld) │  Python       │  PipelineState (40+fld) │  Rust (PyO3)
│  TokenUsage / Metrics   │  dataclass    │  TokenUsage / Metrics   │  Rust struct
│  PipelineConfig         │               │  PipelineConfig         │
│  PipelineResult         │               │  PipelineResult         │
│  PipelineEvent          │               │  PipelineEvent          │
│  Error Hierarchy        │               │  Error Hierarchy        │  Rust enum
├─────────────────────────┤               ├─────────────────────────┤
│          N/A            │               │  Full Rust Core Engine  │  148 .rs files
│                         │               │  (16 stages, 60+ strat.)│  Pure Rust
│                         │               │  reqwest HTTP client    │
│                         │               │  tokio async runtime    │
└─────────────────────────┘               └─────────────────────────┘
   176 Python files                          144 Rust + 34 Python files
   deps: anthropic, pydantic, mcp           deps: Rust (compiled), anthropic

What's Actually in Rust

The data layer is implemented in Rust and exposed to Python via PyO3:

Type	Python (executor)	Rust (harness)
PipelineState	@dataclass (40+ fields)	Rust struct → #[pyclass]
TokenUsage	@dataclass + __add__	Rust struct + Add trait
PipelineConfig	@dataclass	Rust struct with apply_to_state()
PipelineResult	@dataclass + from_state()	Rust struct + class methods
PipelineEvent	@dataclass	Rust struct
ErrorCategory	str, Enum	Rust enum + is_recoverable()
StageDescription	@dataclass	Rust struct
Exceptions (6 types)	Python Exception subclasses	Rust error hierarchy → PyO3 exceptions

Additionally, geny-harness contains a complete Rust implementation of the entire pipeline engine (148 .rs files), including all 16 stages and 60+ strategy implementations. This Rust core is designed for:

• Future native execution — bypassing Python entirely for maximum throughput
• Embedding in Rust applications — use the pipeline engine directly from Rust/C/C++
• WebAssembly compilation — run the pipeline in browsers or edge environments

What's Still in Python (Same as executor)

The pipeline orchestration layer remains in Python for both libraries:

• Pipeline class (3-phase execution engine)
• EventBus (pub/sub with pattern matching)
• Session / SessionManager
• PipelineBuilder / PipelinePresets
• All 16 stage implementations (using anthropic SDK for API calls)

This means the actual pipeline execution flow is identical. The Anthropic API calls, streaming, tool execution, and agent loops work exactly the same way.

Performance Characteristics

Operation	executor (Python)	harness (Rust+PyO3)	Notes
State creation	~195ms/100K	~96ms/100K	Rust 2x faster — 40+ field struct init
Field access	~46ms/500K	~393ms/500K	Python faster — PyO3 boundary cost
Token arithmetic	~189ms/500K	~234ms/500K	Similar — PyO3 overhead offsets Rust speed
Result.from_state	~88ms/100K	~90ms/100K	Identical

Key insight: For the current Python-orchestrated usage pattern, there is no significant performance difference. The real performance advantage of geny-harness will emerge when:

1. The Rust pipeline engine is used natively (without Python)
2. Multiple pipelines run concurrently via tokio
3. State serialization/deserialization is done in Rust (JSON, MessagePack)

When to Use Which

Scenario	Recommendation
Standard Python project	geny-executor — simpler, pure Python, easier to debug
Want Rust core for future native use	geny-harness — invest in Rust ecosystem now
Embedding in Rust application	geny-harness — use geny-harness-core crate directly
Need to modify stage internals	geny-executor — all Python, easy to fork/modify
Production with high concurrency	geny-harness — Rust core ready for tokio-based scaling
Learning/prototyping	geny-executor — more straightforward

Quick Start

# Identical to geny-executor — just change the import
from geny_harness import PipelinePresets

pipeline = PipelinePresets.agent(
    api_key="sk-ant-...",
    model="claude-sonnet-4-20250514",
    system_prompt="You are a helpful assistant.",
)

result = await pipeline.run("Hello!")
print(result.text)

Drop-in Replacement

# Before (geny-executor)
from geny_executor import Pipeline, PipelineConfig, PipelinePresets
from geny_executor.session.manager import SessionManager

# After (geny-harness) — just change the package name
from geny_harness import Pipeline, PipelineConfig, PipelinePresets
from geny_harness.session.manager import SessionManager

Project Structure

geny-harness/
├── crates/
│   ├── geny-harness-core/    # Pure Rust library (148 .rs files)
│   │   └── src/
│   │       ├── core/          # Pipeline, State, Config, Errors, Builder
│   │       ├── events/        # EventBus, PipelineEvent
│   │       ├── session/       # Session, Manager, Freshness
│   │       ├── tools/         # Tool, Registry, MCP
│   │       └── stages/        # 16 stages × (interface + types + artifact)
│   └── geny-harness-py/      # PyO3 bindings (cdylib)
├── python/
│   └── geny_harness/          # Python package
│       ├── core/              # Pipeline, Builder, Presets, Stage
│       ├── events/            # EventBus
│       ├── session/           # Session, Manager
│       ├── stages/            # 16 stage implementations
│       └── tools/             # Tool, Registry
└── tests/

Supported Platforms

• Python: 3.10 — 3.14
• OS: Linux (x86_64), Windows (x64), macOS (arm64)
• Rust: 2021 edition (for building from source)

License

MIT