Build Your Own OpenClaw
A step-by-step guide to building a lightweight AI agent, from a chat loop to multi-agent production systems.
- Source repo
- czl9707/build-your-own-openclaw
- Stars
- ★ 1.9k
- Last updated
- 3mo ago
- License
- MIT
- Primary language
- Python
- FA score
- 21/100 · Major gaps
At a glance
- How it runs
- Works with
- Universal · cross-platform
- Cost
- Free software; you pay for model usage
- Setup effort
- Medium · a few setup steps
- You'll need
- Typical use
- Developers wanting to learn AI agent development incrementally, each step building upon the previous.
- Not a fit if
- Developers working outside Python or wanting a production-ready system
- Users wanting a ready-to-run product rather than a step-by-step tutorial
- Source review
- 21/100 · Major gaps 8 safety controls not found
What does this agent do, and when should you use it?
This repository provides a step-by-step tutorial to build your own AI agent, from a simple chat loop to a lightweight version of OpenClaw. The tutorial consists of 18 progressive steps, each with a README.md explaining key components and design decisions, and a runnable codebase. The steps are organized into four phases: Phase 1 (Steps 0-6) builds a capable single agent with chat, tools, skills, persistence, slash commands, compaction, and web tools. Phase 2 (Steps 7-10) refactors to an event-driven architecture for scalability and multi-platform support, including config hot-reload, channels, and WebSocket. Phase 3 (Steps 11-15) adds multi-agent routing, cron heartbeat, multi-layer prompts, post-message-back, and agent dispatch. Phase 4 (Steps 16-17) covers concurrency control and memory. The reference implementation is the pickle-bot project.
The repository contains a set of tutorials for learning how to build AI agents. Each step includes a README.md and a runnable codebase. The steps progress as follows: Step 0 sets up a basic chat loop; Step 1 adds tools; Step 2 introduces skills using SKILL.md; Step 3 implements conversation persistence; Step 4 adds slash commands for session control; Step 5 implements context compaction; Step 6 adds web tools; Step 7 refactors to event-driven architecture; Step 8 enables config hot-reload; Step 9 adds communication channels; Step 10 adds WebSocket support; Step 11 introduces multi-agent routing; Step 12 adds cron heartbeat for scheduled tasks; Step 13 implements multi-layer prompts; Step 14 allows the agent to post messages back to the user; Step 15 adds agent dispatch for collaboration; Step 16 adds concurrency control; and Step 17 implements memory. The tutorial requires configuring API keys via a config file.
- Developers wanting to learn AI agent development incrementally, each step building upon the previous.
- Developers needing to quickly prototype an agent with tools, skills, and memory.
- Developers looking to scale a single-agent system to event-driven, multi-platform architecture.
- Developers aiming to implement multi-agent collaboration and scheduled tasks.
- Developers preparing for production deployment needing concurrency control and long-term memory.
How do you install or deploy this agent?
First, clone the repository: git clone https://github.com/czl9707/build-your-own-openclaw.git. Then, copy the example config: cp default_workspace/config.example.yaml default_workspace/config.user.yaml. Edit config.user.yaml to add your API keys, referring to LiteLLM's provider documentation for supported providers.
How do you use this agent?
Ensure Python is installed and API keys are configured. Then navigate to any step directory (e.g., 00-chat-loop), read the README.md for design details, and run the corresponding Python script. You can follow the tutorial in order or jump to specific steps. For a complete example, see the reference implementation pickle-bot.
What are this agent's strengths and limitations?
- Clear progressive structure with 18 steps, suitable for learning.
- Each step has runnable code, enabling hands-on practice.
- Uses LiteLLM to abstract multiple LLM providers, avoiding vendor lock-in.
- Comprehensive coverage of key agent capabilities: tools, skills, memory, event-driven design, multi-agent, and more.
- Requires API key configuration and depends on external LLM services, which may incur costs.
- Each step is implemented in a separate session; integrating all steps into a production system may require additional work.
- Some advanced topics such as concurrency control and memory may be minimally implemented, requiring further extension.
- The tutorial is Python-centric, not suitable for developers using other languages.
How does this agent compare with similar options?
The tutorial explicitly references OpenClaw as its base, aiming to build a lightweight version of it.
Key facts side by side with the most closely related agents.
| Agent | Source review | Form / cost | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|---|
| Build Your Own OpenClaw This agent | 21 · Major gaps | CLIFree + model costs | ★ 1.9k | 3mo ago | Python | — |
| Learn Claude Code: Harness Engineering for Real Agents | 48 · Major gaps | CLIFree + model costs | ★ 79k | 20d ago | Python | Claude API |
| CaveAgent | 62 · Some gaps | Library / SDKFree + model costs | ★ 204 | 1mo ago | Python | OpenAI API · Claude API |
| Claude Code From Scratch | 60 · Some gaps | CLIFree + model costs | ★ 2.8k | 3mo ago | Python | OpenAI API · Claude API |
How does FollowAgents rate this agent?
Why each dimension lost points
Evidence shows the repository provides only tutorial and example code, with no description of security mechanisms. No least privilege, user confirmation, data flow transparency, sensitive data handling, dependency security, external effects control, rollback, or source attribution. Hence all trust criteria score 0.
Tutorial structure is clear and steps are logically consistent, but no error handling or failure messages are described. Dependency availability is not mentioned. Thus self-consistency scores 1, others 0.
Tutorial targets developers with clear scenarios, but capability boundaries are not clearly defined, trigger precision is not addressed, and environment fit only mentions API configuration. Thus audience and scenarios score 2, capability boundaries and environment fit score 1, trigger precision 0.
Information architecture is good with clear directory and step descriptions; install notes only cover API configuration; naming is stable but versioning strategy is unclear; examples and FAQ are limited; known limitations not mentioned; license is MIT; no changelog; maintenance responsibility unclear. Thus information architecture scores 2, install notes, naming stability, examples and FAQ, maintenance responsibility score 1, known limitations and versioning changelog 0, license 2.
Output usability is average, tutorial provides runnable code but output format not specified; marginal value is high, offering a complete path to build from scratch; cost-benefit not assessed. Thus output usability scores 1, marginal value 2, cost-benefit 1.
Claim traceability is average, steps have corresponding code; but lacks cross-source verification and separation of facts and inferences. Thus claim traceability scores 1, others 0.
- Not found in source: least-privilege scopingGrant only what the task needs: a dedicated account or read-only token, scoped to specific directories and repos.
- Not found in source: confirmation before actingTurn on (or add) a confirmation step before it acts, and try it in a sandbox or test environment before real data.
- Not found in source: data-flow disclosureWatch which external services it contacts (proxy or firewall logs) and keep sensitive data out until you know where it goes.
- Not found in source: sensitive-data handlingUse dedicated, low-privilege, revocable API keys — never production credentials — and keep secrets out of logs.
- Not found in source: dependency securityPin versions and run a dependency audit (npm audit, pip-audit) before installing; prefer running it in a container.
- Not found in source: disclosed external effectsEstablish which external systems it writes to, sends to or changes, and verify with test accounts or repos before production.
- Not found in source: rollback or recovery pathBack up first, or work on a git branch or snapshot, so its changes can be undone.
- Not found in source: verifiable attributionInstall from the official repo or registry and check the publisher and URL to avoid look-alike packages.
- This repository is tutorial-oriented and lacks any security mechanisms; users should assess risks before production use.
- Dependency security is not mentioned, potential undisclosed vulnerabilities.
- No error handling or failure messages provided, users may face difficulties troubleshooting.