oris-runtime 0.23.0

An agentic workflow runtime and programmable AI execution system in Rust: stateful graphs, agents, tools, and multi-step execution.
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Oris

Oris is an AI Self-Evolution Framework for closed-loop software improvement.

Latest Version docs.rs

Why Oris

Most systems can execute tasks, but cannot systematically improve how they solve recurring problems.

Oris focuses on closed-loop improvement for AI software:

  • Capture real signals from failures and regressions.
  • Generate and validate candidate mutations safely.
  • Promote proven solutions into reusable assets.
  • Reuse successful solutions with confidence-aware replay.

Capability Boundary

In Scope (Primary) Supporting Layer Not Primary in This README
Self-evolution loop and framework primitives Durable execution and orchestration runtime Exhaustive runtime API reference
Gene/Capsule lifecycle and confidence reuse Storage/checkpoint backends and deployment integration Detailed access policy, endpoint, and metrics contracts
Evolution-oriented scenario workflows Production operations and integration surface General-purpose workflow engine positioning

Self-Evolution Loop

Oris implements an 8-stage self-evolution loop:

  1. Detect — collect actionable signals from compiler/test/runtime outcomes.
  2. Select — choose the best candidate gene or strategy.
  3. Mutate — generate candidate changes from prior successful patterns.
  4. Execute — run mutations in a controlled sandbox.
  5. Validate — verify correctness and safety gates.
  6. Evaluate — compare improvement versus regression.
  7. Solidify — promote successful mutations into durable assets.
  8. Reuse — replay proven assets with confidence tracking.

EvoMap Alignment

Oris maps EvoMap concepts to concrete framework behavior:

EvoMap Concept Oris Mapping
Worker Pool EvolutionPipeline stages
Task Queue Signal intake and selection flow
Bounty System Issue intake and prioritization
A2A Protocol oris-evolution-network experimental protocol

See EvoMap alignment details.

What You Can Build

  • Self-improving AI agents that learn from failed runs.
  • Supervised or autonomous dev loops for recurring issues.
  • Evolution-aware replay pipelines with confidence lifecycle.
  • Cross-agent knowledge exchange over an evolution network surface.

Quick Start

Install the core crate and enable the framework surface:

cargo add oris-runtime
cargo add oris-runtime --features full-evolution-experimental
export OPENAI_API_KEY="your-key"

Run the canonical evolution scenario:

cargo run -p evo_oris_repo

Run the first-run script with observable artifacts:

bash scripts/evo_first_run.sh

Expected outputs:

  • target/evo_first_run/summary.json
  • target/evo_first_run/run.log

Components & Maturity

Maturity below reflects the current checked-in framework surface.

Component Crate Maturity Gate
Evolution Core crates/oris-evolution Implemented baseline with extended lifecycle primitives evolution-experimental
Sandbox crates/oris-sandbox Implemented baseline evolution-experimental
EvoKernel crates/oris-evokernel Implemented baseline with governor-aware capture evolution-experimental
Intake crates/oris-intake Implemented baseline for issue intake/prioritization intake-experimental
Evolution Network crates/oris-evolution-network Experimental protocol scaffold evolution-network-experimental
Full Framework Facade crates/oris-runtime re-exports Aggregate framework surface full-evolution-experimental

Runtime Integration (Brief)

The runtime layer is a supporting integration surface for hosting and operating the framework (execution server, workers, durable jobs). This README does not act as a runtime handbook; use the docs below when you need runtime-level details.

Learn More

Community / License