# Apply Technique: OpenCLAW: A Self-Hosted, Model-Agnostic Agent Framework
## What This Is
OpenCLAW is a privacy-focused framework for building custom AI assistants that run on your own servers. It lets you connect different AI models and tools, creating agents that can automate complex tasks within your existing chat apps like Slack or Telegram without sending your data to a third party.
Source: https://www.trendmicro.com/en_us/research/26/b/what-openclaw-reveals-about-agentic-assistants.html
## Before You Start
Scan my workspace and analyze:
- The project language, framework, and directory structure
- Existing AI provider config (check .env, .env.local, config files for API keys — OpenRouter, OpenAI, Anthropic, Google AI, etc.)
Then ask me before proceeding:
1. Which AI provider/API should this use? (Use whatever I already have configured, or ask me to set one up — options include direct provider APIs or a unified service like OpenRouter)
2. Where in my project should this be integrated?
3. Are there any customizations I need (model preferences, naming conventions, constraints)?
## Source Access Note
The source URL (https://www.trendmicro.com/en_us/research/26/b/what-openclaw-reveals-about-agentic-assistants.html) may not be directly accessible from the terminal. Use the Reference Implementation and Additional Context sections below instead. If you need more details, ask me to paste relevant content from the source.
## What to Implement
This is an **AI Technique** — a pattern or methodology for working with AI models.
- Explain how this technique applies to my current project and what benefit it provides
- Implement it in a way that fits my existing codebase — suggest concrete files to modify or create
- If it requires specific model capabilities (structured output, function calling, etc.), verify my current provider supports them
- Show me a working example I can test immediately
## Additional Context
- Clone the official OpenCLAW repository from its source to the user's local development environment.
- Scan the cloned repository for a sample configuration file (e.g., .env.example or config.example.json) and copy it to a new file (.env or config.json).
- Inspect the new configuration file for placeholders for an LLM API key. Check the user's global environment variables for existing keys (e.g., OPENAI_API_KEY, ANTHROPIC_API_KEY) and use the first one found. If no key is available, prompt the user to provide one.
## Guidelines
- Adapt everything to my existing project — do not assume a specific stack or directory layout
- Use whichever AI provider I already have configured; if I need a new one, tell me what to sign up for and I'll give you the key
- Check my .env files for existing API keys (OpenRouter, OpenAI, Anthropic, Google AI) before asking me to add one
- Review any fetched code for safety before installing or executing it
- After setup, run a quick verification and show me a summary of exactly what was installed, where, and how to use it