# shareAI-lab/claw0

0 - 1 learn OpenClaw: sections to build an claw-AI agent from scratch

Repository: https://github.com/shareAI-lab/claw0
Canonical: https://ross.abutalabs.com/products/claw0
Language: Python
License Family: other
Last push: 2026-06-30T16:13:05+00:00

## Health v2 (maintenance only)
Score: 55/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 90, release rhythm 35, longevity 13
- inputs: {"age_days": 190, "days_push": 64, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3299, forks 382 (observed 2026-08-28T04:07:55.663845+00:00)

## What it is
claw0 is a 10-section progressive tutorial that teaches you to build a production-grade AI agent gateway from scratch in Python, with each section being a single runnable file. It covers the agent loop, tool use, sessions, messaging channels, routing, memory, scheduling, delivery queues, resilience, and concurrency, culminating in the ability to read the OpenClaw production codebase.

## Use cases
- learn how AI agent loops work from scratch
- build an AI agent gateway step by step
- understand tool calling and dispatch tables in LLM agents
- implement persistent sessions and context overflow handling for chatbots
- connect an AI agent to Telegram and Feishu channels
- learn retry, backoff, and auth rotation patterns for LLM apps
- prepare to read the OpenClaw production codebase

## When to choose
- you want a hands-on, code-first path from a basic agent loop to a production-grade gateway
- you learn best from small runnable examples that build incrementally
- you need to understand messaging channels, routing, and concurrency for agent systems
- you want multilingual (English/Chinese/Japanese) tutorial material

## When to avoid
- you need a ready-to-deploy agent framework rather than a learning resource
- you want a library with a stable API to depend on in production
- you need a project with an explicit open-source license for commercial use
- you are looking for a no-code or low-code agent builder

## Facets
- artifact type: learning-resource
- maturity: active
- function: agent-framework, chatbot, llm-inference, developer-tools, message-queue, scheduling
- domain: artificial-intelligence, large-language-models, developer-tools, tutorials, education
- platform: python, cli, cross-platform
- tags: ai-agent-gateway, openclaw, tutorial, progressive-learning, telegram-bot, feishu, agent-loop, tool-use, hands-on, ai-agents

## Member repositories
- shareAI-lab/claw0 (main) score 55

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:55.663845+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:22:40.942564+00:00, confidence not recorded.
  - readme: https://github.com/shareAI-lab/claw0 (fetched 2026-08-28T04:07:55.663845+00:00, sha 75e831c33289)
- Data as of 2026-08-30T08:39:29.467469+00:00.