Orchard

Orchard is an open foundation for agentic modeling research. We build one shared substrate — Orchard Env — and then use it to explore agentic modeling recipes across domains: software engineering, browser navigation, computer use, and personal-assistant workflows.

The foundation exists to make the recipes possible. Because the environment layer is a stable service rather than a piece of a training stack, every recipe reuses the same substrate for trajectory distillation, on-policy RL rollouts, and evaluation — so datasets, training recipes, and evaluation protocols stay portable across harnesses, domains, and projects instead of being rebuilt for each new study.

Layer	What it is
Recipes	The research. Open SFT + RL recipes explored on top of the foundation — Orchard-SWE, Orchard-GUI, and Orchard-Claw — plus follow-on work such as OpenWebRL and OpenForge RL.
Orchard Env	The foundation. A Kubernetes-native sandbox service + Python SDK that spins up thousands of isolated containers on demand and drives multi-turn agent ↔ sandbox interaction (exec, file I/O, git patches) over HTTP.
Trainer	RL training stack — a vendored slime fork with Orchard rollout code.

📄 Paper: Orchard: An Open-Source Agentic Modeling Framework (Peng et al., arXiv:2605.15040)
🤗 Dataset: microsoft/Orchard — swe (107K SWE trajectories) and gui (3,070 multimodal browser-navigation rollouts) subsets

News

[2026-07] 🎉 We are excited to release OpenForge RL, which extends Orchard to train agents inside their real deployment harnesses — ZeroClaw, OpenClaw, Codex — instead of the simplified reimplementations open training stacks usually require, removing the train–deploy mismatch. A lightweight proxy records the harness's own inference calls and reconstructs them into samples for any RL codebase, while Orchard Env launches each rollout as a remote container, so any harness pairs with any environment.
[2026-06] 🎉 We are excited to release OpenWebRL, which extends Orchard-GUI into a full online multi-turn RL study on live websites — covering supervised initialization, multimodal context management, trajectory-level success judging, and multi-turn policy optimization.
[2026-05] 📄 The Orchard paper is on arXiv, together with the microsoft/Orchard trajectory datasets and Orchard Env.

Recipes

Three studies from the Orchard paper — different domains, harnesses, and reward mechanisms, one environment service underneath.

Recipe	Backbone	Training data	Key techniques	Headline result
Orchard-SWE	Qwen3.5-35B-A3B	107K distilled trajectories	Credit-assignment SFT · Balanced Adaptive Rollout · on-policy distillation · rubric-based process reward · value-model reranking	73.0% SWE-bench Verified
Orchard-GUI	Qwen3-VL-4B-Thinking	0.4k SFT + 2.2k RL tasks	Distillation, then online RL on live websites	68.4% avg — 74.1 / 67.0 / 64.0
Orchard-Claw	Qwen3-30B-A3B-Thinking	0.2k synthetic tasks	Opus-synthesized tasks · training across two harnesses	59.6% pass@3, 73.9% under ZeroClaw

The common thread is generalization, not just peak score. Orchard-SWE keeps 51.0 on SWE-bench Multilingual (vs 28.7 for OpenSWE-32B) and still works under a harness never seen in training — 45.0 on SWE-bench Verified and 20.1 on Terminal-Bench 2.0 with Kimi-CLI, where OpenSWE-32B collapses to 3.6 and 0.0.

Orchard Env — the foundation

A thin, Kubernetes-native environment service exposing generic primitives — sandbox lifecycle, command execution, file I/O, network policy, and a REST API — with no assumptions about the harness, trainer, inference backend, or task domain sitting above it.

REST API for sandbox lifecycle (create / exec / files / patch / delete)
Sync and async Python SDK with auto-cleanup, retries, and context-manager ergonomics
In-pod agent for low-latency exec over Pod IP
Any base image — the agent is injected by an init container bundling its own self-contained Python interpreter
Any harness — codex, claude, pi, opencode, and hermes preinstalled on PATH in every sandbox
Multi-replica orchestrator with Redis-backed state and distributed locks
Network isolation via Calico NetworkPolicy (deny-egress by default), per-sandbox CPU / memory / timeout limits, TTL cleanup, and API-key auth

Quick start

pip install -e "orchard_env[dev]"

export SANDBOX_BASE_URL="http://your-orchestrator-host"
export SANDBOX_API_KEY="your-api-key"

from orchard_env import SandboxClient

with SandboxClient() as client:
    with client.create_sandbox("python:3.11-slim") as sandbox:
        result = sandbox.exec("echo 'Hello, Orchard!'")
        print(result.stdout)

Roadmap

Stateful sandboxes — pause, resume, and branching

The environment is currently linear: a sandbox is created, driven for N turns, and destroyed. Snapshotting makes that measurable directly instead. We are adding:

Pause / resume — checkpoint a sandbox's full state (filesystem, processes, environment) at any turn and restore it later.
Branching — fork k independent continuations from the same snapshot at turn t.
Prefix sharing — because a branched prefix is executed once instead of once per rollout, tree-structured search and per-turn advantage estimation get substantially cheaper.

Citation

@article{peng2026orchard,
  title={Orchard: An Open-Source Agentic Modeling Framework},
  author={Peng, Baolin and Yao, Wenlin and Wu, Qianhui and Cheng, Hao and
          Yu, Xiao and Yang, Rui and Ge, Tao and Sordoni, Alessandro and
          Yuan, Xingdi and Shen, Yelong and He, Pengcheng and Zhang, Tong and
          Yu, Zhou and Gao, Jianfeng},
  journal={arXiv preprint arXiv:2605.15040},
  year={2026},
  url={https://arxiv.org/abs/2605.15040}
}

Orchard (GitHub Repo)