ClamBot: AI Agent Runs LLM-Generated Code in WASM Sandbox for Security

What ClamBot Does
ClamBot is an AI agent framework that addresses security concerns with existing agent frameworks by running all LLM-generated code in a WebAssembly sandbox instead of using exec() or subprocess calls. The creator built it after trying frameworks that execute arbitrary code directly on the host machine, citing examples like LangChain having a CVE for this approach, AutoGen shelling out with subprocess, and SWE-Agent running bash commands from the model.
Technical Implementation
ClamBot is built on top of amla-sandbox, a WASM sandbox that uses QuickJS in Wasmtime. The LLM writes JavaScript code that runs in a memory-isolated sandbox with zero network access. Every tool call (HTTP, filesystem, cron) must pass through an approval gate back in Python. No Docker or VM is required - it runs as one binary.
Key Features
- Sandbox Security: All code runs in WASM - cannot touch host memory or network
- Approval Gate: SHA-256 fingerprinted approval gate on every tool call with pre-approve patterns (e.g., "allow web_fetch for api.coinbase.com")
- Clam Reuse: Successful scripts get saved as "clams" and can be reused, reducing API costs for repeated requests
- Multi-Provider Support: OpenRouter, Anthropic, OpenAI, Gemini, DeepSeek, Groq, Ollama
- Telegram Integration: Telegram bot with inline approval buttons
- Additional Features: Persistent memory, cron scheduling, SSRF protection that blocks private IPs, secrets never appear in logs/tool args/traces
Example Workflow
User asks: "what are the top movers on binance?" The sandbox executes JavaScript → makes http_request to Binance API → goes through approval gate → returns result. The bot responds with the top 10 movers on Binance by 24h change.
Getting Started
bash git clone https://github.com/clamguy/clambot.git
cd clambot
uv run clambot onboard
uv run clambot agentStack and Scale
The project is built with Python + QuickJS/Wasmtime, contains approximately 10K lines of code, and was inspired by OpenClaw and nanobot. The creator built it because they wanted "an AI agent I could actually trust on my server."
📖 Read the full source: r/openclaw
👀 See Also

MetaBot: Open-Source Bridge Connects Claude Code to Telegram, Feishu, and WeChat
MetaBot is an open-source TypeScript bridge that connects the Claude Code Agent SDK to messaging platforms like Telegram, Feishu, and WeChat. It provides persistent memory, scheduled tasks, multi-agent collaboration, and real-time streaming of tool calls.

Connecting OpenClaw to Qwen2.5 Coder: Feasibility and Considerations
Explore the possibility of connecting OpenClaw to a local Qwen2.5 Coder model with 7 billion parameters to address rate limits with API Gemini 3.

Claude Code Verification Bottleneck and Browser Automation Plugin Solution
A developer reports that verification remains the slowest part of using Claude Code, requiring manual testing of features. They found a browser automation plugin that lets the agent verify real product flows before marking tasks complete.

Antigravity 2.0 Tops OpenSCAD Architectural 3D Benchmark – ModelRift Tests 6 LLMs on the Pantheon
ModelRift benchmarked 6 LLMs on building the Pantheon in OpenSCAD. Antigravity scored 4.5/5 in architectural quality, beating baseline Codex 5.5. Cursor 3.5 was fastest but weakest.