Loom

Turn what you know into testable AI steps. Chain them into workflows. Measure whether they work. Scale when ready.

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What Loom Does

Most AI tools give you one big prompt and one model. That works until it doesn't — the prompt gets unwieldy, you can't test parts independently, and asking the same model to review its own work doesn't catch real problems.

Loom splits AI work into focused steps. Each step has a clear job, a typed contract (so you know what goes in and what comes out), and can use a different model. You test steps individually, chain them into pipelines, and measure whether changes help or hurt.

  Document ──► Extract ──► Classify ──► Summarize ──► Report
                 │            │            │
                 │            │            └─ Claude Opus (complex reasoning)
                 │            └─ Ollama local (fast, free)
                 └─ Ollama local (fast, free)

Steps can run in parallel, use different AI models, and be tested with the built-in Workshop web UI — all without deploying any infrastructure.

Quick Start

pip install loom-ai[workshop]    # install from PyPI
loom setup                       # configure (auto-detects Ollama)
loom workshop                    # open the web UI at localhost:8080

Open your browser, pick a worker (summarizer, classifier, extractor, qa, reviewer, or translator), paste some text, and see results. No data files needed — just text you already have.

Want to analyze Telegram channels? See the RAG quickstart.

Who This Is For

Anyone hitting the limits of single-prompt AI. Start with the six shipped workers in Workshop — no coding needed.

Researchers and analysts — process documents, extract data, build analytical pipelines with knowledge silos and blind audit review.

AI engineers — multi-step LLM workflows with typed contracts, tool-use, and pipeline orchestration.

Platform teams — Kubernetes deployment with rate limiting, model tier management, and OpenTelemetry tracing.

Key Features

Feature	What It Does
6 Ready-Made Workers	Summarizer, classifier, extractor, translator, QA, reviewer — use immediately
Workshop	Web UI for testing, evaluating, and comparing step outputs
Built-in Evaluation	Test suites, scoring, golden dataset baselines, regression detection
Config-Driven	Define workers in YAML — no Python code needed for LLM steps
Knowledge Silos	Per-worker access control; blind audit workers can't see what they're reviewing
Pipeline Orchestration	Chain steps with automatic dependency detection and parallelism
Three Model Tiers	Local (Ollama), Standard (Claude Sonnet), Frontier (Claude Opus)
RAG Pipeline	Telegram channel ingestion, chunking, vector search
MCP Gateway	Expose any workflow as an MCP server

Documentation

Start here:

Guide	Description
Concepts	How Loom works — the mental model in plain language
Getting Started	Install, configure, and get your first result
Why Loom?	How Loom compares to other frameworks — and when not to use it
Workshop Tour	What each Workshop screen does and when to use it
Workers Reference	6 shipped workers with I/O schemas and examples
CLI Reference	All commands with every flag and default

Go deeper:

Guide	Description
Building Workflows	Custom steps, pipelines, tools, knowledge
RAG Pipeline	Social media stream analysis end-to-end
Architecture	System design, message flow, NATS subjects
Deployment	Local, Docker, and Kubernetes