ENVLOOP.NET · DEVELOPER & OPEN ECOSYSTEM PORTAL

Full-Stack Recursive Self-Improvement from Verifiable Environments.

EnvLoop versions environments, verifiers, benchmarks, data, models, harnesses, and updaters as a single closed-loop system S_t. Execute tasks in isolated sandboxes, turn failures into verifiable training data, post-train open-weight models, and promote checkpoints only when they pass an independent gate.

$ python -m pip install --upgrade envloop-cli
el · envloop-cli · S_t → S_{t+1} PyPI v0.x
# 1. Run baseline evaluation in isolated sandboxes (E_t, V_t)
$ el job submit --dataset envloop/pev@1.0.4 --wait
✓ job_01j9x8... completed | 980 states | verifier: harbor-offline

# 2. Post-train candidate checkpoint on verified data (D_t → M_t)
$ el job submit --type train --spec train-pev27.json
✓ LoRA SFT (Qwen3.8-27B rank-16) | macro: 0.762 → 0.915 | Brier: 0.125

# 3. Equip your coding agent with the EnvLoop Skill
$ npx skills@latest add https://docs.envloop.ai --skill envloop
S_t = (E_t, V_t, B_t, D_t, M_t, H_t, U_t)
R_t = Run(M_t, H_t; E_t, V_t)  →  C_{t+1} = U_t(S_t, R_t)
S_{t+1} = Gate(C_{t+1}, S_t; G_t)
0.915 +15.3% vs Base
Pev-27B Family-Macro Accuracy on Pev-Bench (720 new users)
3,885 Harbor Tasks
Harbor Tasks Across 7 Personal-Agent Decision Families
83.5% Strict Pass
Claude Opus 5.5 Strict Pass on cu-bench (20 Linux Apps)
900 Verified Levels
AI-Generated Levels with Constructive Witnesses in Recursive-Play

01 / FULL-STACK SELF-EVOLUTION ARCHITECTURE

Version the Entire System. Gate Every Promotion Independently.

When a model learns new skills, it needs harder tasks; when new tasks expose bottlenecks, tools and verifiers must evolve too. Click any component of the 7-tuple S_t below to inspect its technical contract and CLI workflow.

01 · System State
S_t = (E_t, V_t, B_t, D_t, M_t, H_t, U_t)
7-tuple co-versioned per round t
02 · Rollout & Trace
R_t = Run(M_t, H_t; E_t, V_t)
Execute model + harness in sandbox
03 · Candidate Proposal
C_{t+1} = U_t(S_t, R_t)
Updater attributes failures & trains
04 · Independent Gate
S_{t+1} = Gate(C_{t+1}, S_t; G_t)
Promote only if frozen gate G_t passes

02 / ENVHUB & ASSET NAMESPACE SERVICE (ANS)

Immutable Datasets, Sandboxed Runners & Reproducible Recipes.

EnvHub manages content-addressed Datasets, Task Environments, Runners, and Recipes with strict separation between public protocol clients (`envloop-envhub`) and execution orchestration (`EnvPlatform`).

EnvHub · envloop-envhub

Assets & ANS Protocol

Dataset / Task / Runner manifests, URI resolution, SHA-256 digest verification, and immutable publishing.

envloop-recipe

Algorithms & Training Libs

Reusable multi-file LoRA SFT, label-token calibration, and runtime libraries pinned by Git commit.

EnvPlatform

Scheduling & Governance

Sandboxed Job / Trial execution, RBAC, compute budgets, log streaming, and scale-to-zero endpoints.

envloop-cli · el

Developer Entrypoint

Unified `el` / `envloop` CLI and Python `envloop.Client` SDK for discovering assets and running jobs.

GitHub: EnvLoop ↗ Hugging Face: EnvLoop ↗

03 / VERIFIABLE BENCHMARKS & LEADERBOARDS

Leak-Resistant Evaluation with Network-Free Verifiers.

Every benchmark on EnvLoop pairs realistic tasks with independent deterministic verifiers—never relying on self-judging prompts.

04 / DEVELOPER QUICKSTART

From `pip install envloop-cli` to Your First Verified Job in 60 Seconds.

Run evaluations on public Environment Datasets, package your own task with `runner.toml`, or equip your coding agent with the EnvLoop Skill.