Skip to main content

Loom Stack

loom-run — official Loom stack showcase

PyPI GitHub Loom stack docs Python 3.11+ License: MIT Tests

Official reference product for the Loom stack — not a generic agent framework, but the runnable demo that wires loom-runner + flow-xray into a local-first durable chat agent with multi-agent supervisor.

Keywords: durable agents · checkpoint resume · multi-agent orchestration · MCP tools · local-first · SQLite · inspectable runs · LangGraph alternative (runtime slice)

Showcase hub kroq86.github.io/loom-stack
This repo github.com/kroq86/loom-run
Runtime layer github.com/kroq86/loom-runner
Local traces github.com/kroq86/flow-xray
Ops product github.com/kroq86/loom-ops — runbooks, HITL, incident response
User message  →  coordinator (LLM + tools)  →  loom-runner checkpoint/resume  →  flow-xray --trace
                      ↑ supervise → subagent runs (v0.2)

What this repo is

Role Explanation
Showcase / reference product End-to-end proof that the Loom stack works: CLI, mock LLM (CI-safe), tools, supervisor, E2E tests
Not LangGraph, CrewAI, or a hosted agent platform
Built on loom-runner (durability), loom-tailcalls (loop shape), flow-xray (HTML traces)
Pattern from agents_architecture — coordinator loop only, not full backend

If you want libraries to compose → start with loom-runner.
If you want see it all working in 30 seconds → clone this repo.
If you want incident/deploy runbooks (not dev chat) → loom-ops.

More: Showcase guide · Architecture

Loom stack position

The stack is a pyramid, not five equal frameworks. Tail-call optimization is the primitive, runner is the durable runtime, xray is the microscope, and the apps prove the stack in real workflows.

Layer Project Job
Primitive loom-tailcalls Make async recursive/state-machine loops stack-safe
Runtime kernel loom-runner Make those loops durable, resumable, idempotent
Microscope flow-xray Show what actually happened in one offline HTML trace
Proof app loom-run ← this repo Chat agent reference implementation
Proof app loom-ops Ops/runbook agent reference implementation

loom-run is not loom-runner. loom-runner is the reusable runtime kernel; this repo is the runnable chat/dev showcase that proves the kernel, traces, MCP tools, and supervisor pattern work together.

Install

From PyPI:

pip install "loom-run[api,openai]"
loom-run chat "explain checkpoint policy" --run-id demo --db runs.sqlite --mock-llm

From source:

git clone https://github.com/kroq86/loom-run.git
cd loom-run
python3.13 -m venv .venv && source .venv/bin/activate
pip install -e ".[dev,api,openai]"

Ecosystem map: loom-stack ECOSYSTEM.md

Demo commands (copy-paste)

Single agent (mock LLM, no API key):

loom-run chat "explain checkpoint policy" --run-id demo --db runs.sqlite --mock-llm
loom-run resume --run-id demo --db runs.sqlite --max-steps 20
loom-run explain --run-id demo --db runs.sqlite
loom-run chat "explain checkpoint policy" --run-id demo2 --db runs.sqlite --mock-llm --trace trace.html

Multi-agent supervisor (v0.2):

loom-run supervise "explain checkpoint policy" --run-id team-demo --db runs.sqlite --mock-llm
loom-run explain --run-id team-demo:sub:researcher --db runs.sqlite

HTTP SSE (optional):

loom-run serve --db runs.sqlite --mock-llm
curl -N -X POST http://127.0.0.1:8765/chat \
  -H 'Content-Type: application/json' \
  -d '{"message":"explain checkpoint policy","run_id":"demo","max_steps":20}'

Tools

Tool Default With MCP
read_file local workspace read rule-based-verifier → read_repo_file
search_docs grep *.md in workspace mcp-docs-memory → docs_search
run_tests pytest -q in workspace rule-based-verifier → run_tests
delegate_subagent supervisor → child chat run —

MCP config: mcp.servers.example.json · env: docs/ENV.md

Extended ecosystem map

Layer Repo Role
Dev showcase (this repo) loom-run Reference chat agent + supervisor
Ops product loom-ops Runbook supervisor (planner / executor / verifier)
Durability loom-runner SQLite checkpoint, resume, idempotent tools
Transitions loom-tailcalls Stack-safe @tailrec driver
Traces flow-xray Offline HTML execution traces
Coordinator reference agents_architecture Production backend patterns
Verification MCP rule-based-verifier Tests, lint, repo read
Docs/memory MCP mcp-docs-memory Semantic docs search

Tests

python -m pytest -q              # 21 tests incl. subprocess E2E
python -m pytest -q tests/test_e2e.py

Docs

License

MIT · kroq86

Metadata

Release files for loom-run 0.2.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for loom-run 0.2.1
File Size Uploaded
loom_run-0.2.1.tar.gz 22.1 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for loom-run 0.2.1
File Interpreter ABI Platform
loom_run-0.2.1-py3-none-any.whl Python 3 none any Details

Total release size: 45.3 kB

Release files / loom_run-0.2.1.tar.gz

Download URL loom_run-0.2.1.tar.gz
Size 22.1 kB
Tags Source
SHA-256 checksum
How to use checksums
78512162105a076520171aaf8fa7ed7b74b887032b563b424d3caca7b3c0278b
BLAKE2b-256 checksum
How to use checksums
4b575c59760aea3f5dd4f4ae3e207578c77fce7a4280bb4be2774b75e9da4932
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.2.0 CPython/3.11.15

Release files / loom_run-0.2.1-py3-none-any.whl

Download URL loom_run-0.2.1-py3-none-any.whl
Size 23.2 kB
Tags Python 3
SHA-256 checksum
How to use checksums
0278e5803b383831622e8e5c1a420f543357263a997998e6be88ef717c5e8329
BLAKE2b-256 checksum
How to use checksums
3b6c6b91eaa44ad6ca99f2243cabf96e4585785dfa579f5a0012757472aa4a2a
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.2.0 CPython/3.11.15

Release history Release notifications | RSS feed

This release

0.2.1 This release

2 release files

0.2.0

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page