deerwork-ai
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deer-workflow
An open-source Dynamic Workflow runtime that combines deterministic TypeScript orchestration with replaceable Agent runtimes.
AI repository intelligence
FR-AI / ANALYSISWhy it is worth attention
It offers a ready-to-deploy multi-platform setup with persistent vector memory, sandboxed shell execution, browser automation, and a single gateway to diverse LLM providers, all containerized for consistency.
Who it is for
- Developers and AI enthusiasts seeking a personal Telegram bot with advanced capabilities
- Users who are comfortable with Docker and command-line tools
- Individuals wanting to experiment with multiple LLM providers through a unified endpoint
- Privacy-conscious users preferring local deployment of an AI agent
Use cases
- Automating web tasks like data scraping or meeting management via browser control
- Managing files and running shell commands in a sandboxed environment
- Using persistent memory for personal assistant tasks (e.g., recalling past conversations)
- Multi-model chat and reasoning with fallback across different AI providers
Strengths
- Multi-platform Docker deployment (macOS, Windows, Ubuntu) with clear per-OS instructions
- Persistent vector memory (LanceDB) for automatic context capture and recall
- Sandboxed shell execution for safe file and command operations
- Integration with multiple LLM providers (NVIDIA, Groq, DeepSeek, etc.) via a single 9Router gateway
Considerations
- Requires manual installation and configuration of 9Router on the host machine
- Linux users need extra steps (--add-host) to connect container to host network
- Not hardened for multi-user access; designed as a single-operator personal agent
README quick start
🦾 Mavent-claw
OpenClaw Agent Powered by 9Router — Multi-Platform Deployment Guide
📋 About
Mavent-claw is a personal deployment configuration for OpenClaw — an open-source, tool-enabled AI agent — connected through 9Router as a unified OpenAI-compatible gateway to multiple LLM providers (NVIDIA NIM, Groq, DeepSeek, MiniMax, and more).
The agent runs as a 24/7 Telegram-reachable automation agent, with browser control, filesystem access, persistent vector memory, and sandboxed shell execution — deployed via Docker for consistency across macOS, Windows, and Ubuntu.
✨ What's Configured
| Feature | Provider | Status |
|---|---|---|
| 💬 Chat / Reasoning | Custom combo via 9Router (multi-model fallback) | ✅ Active |
| 👁️ Vision (Image Input) | NVIDIA Nemotron 3 Omni via 9Router | ✅ Active |
| 🔍 Web Search | DuckDuckGo (experimental) | ✅ Active |
| 🧠 Persistent Memory | Memory LanceDB (vector-based, auto-capture + auto-recall) | ✅ Active |
| 🧮 Embeddings | nemotron-3-embed-1b via 9Router | ✅ Active |
| 🖥️ Sandboxed Shell | OpenShell Sandbox (mirror mode) | ✅ Active |
| 🌐 Browser Automation | Chrome-based control (incl. Google Meet integration) | ✅ Active |
| 📞 Voice Call | Mock provider (dev/testing only) | ⏸️ Inactive (mock) |
| 📱 Telegram | Paired via onboarding wizard | ✅ Active |
🛠️ Tech Stack
📦 Architecture
graph TB
User[Telegram User] -->|Message| Gateway[OpenClaw GatewayDocker Container :18789]
Gateway -->|OpenAI-compatible API| Router[9Routerlocalhost:20128]
Router -->|Route| M1[NVIDIA NIM Models]
Router -->|Route| M2[Groq Models]
Router -->|Route| M3[DeepSeek / MiniMax]
Router -->|Route| M4[NVIDIA Nemotron 3 OmniVision + Embeddings]
Gateway --> Memory[(Memory LanceDBVector Store)]
Gateway --> Shell[OpenShell Sandboxmirror mode]
Gateway --> Browser[Chrome AutomationWeb + Google Meet]
Gateway -->|Volume Mount| Workspace[(Local Workspace)]
style Gateway fill:#f97316,color:#fff
style Router fill:#6366f1,color:#fff
style Memory fill:#10b981,color:#fff
Key components:
- OpenClaw — the agent runtime, running inside a Docker container, exposing a local gateway on port
18789 - 9Router — runs natively on the host machine, a
Description
Personal deployment config for OpenClaw + 9Router — a 24/7 Telegram-reachable AI agent with browser automation, vector memory, and sandboxed execution, running via Docker on macOS, Windows, and Ubuntu.
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