Loop Core Engineering

The specification layer for systems that improve through feedback.

Machine-readable contracts for declaring loops, scoring them, and naming failures — so every tool in the ecosystem speaks the same language.

Validate a loop in 30 seconds · Read LSS 1.0 · Full stack map · Discipline docs

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What you get here

Deliverable	What it does for you
LSS 1.0 — Loop Specification Standard	Declare objectives, workers, evaluators, memory, safety, and termination in validated YAML
LES 1.0 — Loop Engineering Score	Compare loops on 8 dimensions: effectiveness, speed, cost, robustness, scalability, safety, adaptability, autonomy
Failure taxonomy	Shared fail.* codes — not tribal knowledge in Slack threads
ID registry	Stable slugs for patterns, env prefixes, and cross-repo references
Validators & LES calculator	CI-ready tooling every other repo pins — no copied schemas

This repo is the root of the dependency graph. LoopNet, LoopGym, and LoopBench import specs from here. One source. Semver. RFCs.

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The problem this solves

Teams building agentic AI hit the same wall: every project invents its own config format, its own metrics, its own vocabulary for "why did the loop fail?"

Without a shared spec	With Loop Core Engineering
Incomparable demos	LES-scored, reproducible runs
Schema copied into 5 repos	One canonical lss@1.0.0 pin
"It worked in the demo"	Bounded termination + evaluator contracts
Failure post-mortems don't transfer	Shared taxonomy across data, runtime, and bench

Think of it as HTTP for loops — a thin, versioned layer that everything else builds on.

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The ecosystem

flowchart TB
  CORE["<b>Loop Core Engineering</b><br/>LSS · LES · taxonomy · validators"]
  NET["LoopNet<br/>500 trajectories + failures"]
  GYM["LoopGym<br/>Sim · Live · Replay"]
  BENCH["LoopBench<br/>3 tasks · leaderboard"]

  CORE --> NET
  CORE --> GYM
  CORE --> BENCH
  NET --> GYM
  GYM --> BENCH

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Repo	Role	Install
Loop Core Engineering	Specs & governance	Clone + pip install -r requirements.txt
LoopNet	Dataset	Hugging Face or JSONL
LoopGym	Runtime	pip install loopgym
LoopBench	Benchmarks	pip install loopbench

Narrative depth — manifesto, patterns, case studies: Loop Engineering

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Try it now

git clone https://github.com/KanakMalpani/Loop-Core-Engineering.git
cd Loop-Core-Engineering
pip install -r requirements.txt

# Validate against LSS 1.0 (same check CI runs)
python tools/validate_lss.py examples/minimal-loop.yaml

# Structural LES estimate before you run anything expensive
python tools/les_calculator.py --spec examples/minimal-loop.yaml --display

A minimal loop spec looks like this:

loop_name: minimal-echo-loop
version: "1.0"
objective: "Demonstrate smallest valid LSS document"
workers:
  - role: echo
    model: mock
evaluators:
  - type: rubric
termination_conditions:
  - type: max_iterations
    value: 3

Three CI-validated examples ship with the repo — from smoke test to multi-agent debate.

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Specifications @1.0.0

Artifact	Pin	Document
LSS JSON Schema	lss@1.0.0	specs/lss-1.0.schema.json
LSS overview	—	specs/lss-1.0.md
LES formulas	les@1.0.0	specs/les-1.0.md
Pattern & env IDs	—	specs/loop-ids.md
Semver policy	—	CHANGELOG.md

LES scale: store and exchange in [0, 1]. Multiply by 100 only for display.

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Who this is for

You are…	Start here
Building an agent framework	Pin LSS — let users export portable loop specs
Running benchmarks	Validate submissions against lss-1.0.schema.json
Publishing research	Cite lss@1.0.0 + les@1.0.0 for reproducibility
Designing org workflows	Use failure taxonomy + LES dimensions as a shared scorecard

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Governance

Spec changes flow through RFCs → review → semver bump in CHANGELOG.md. See CONTRIBUTING.md · SYNC.md · SECURITY.md

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Citation

@misc{loop-core-engineering-2026,
  title={Loop Core Engineering: Canonical LSS and LES Specifications},
  author={Malpani, Kanak},
  year={2026},
  url={https://github.com/KanakMalpani/Loop-Core-Engineering}
}

_{MIT License · v0.1 · Status · Launch checklist}