Edge-Cloud Collaborative AI Agent
EdgeClaw: Keep sensitive data off the cloud, let cheap models handle 80% of requests
【中文 | English】
--- **What's New** 🔥 - **[2026.03.13]** 🎉 EdgeClaw adds Cost-Aware Collaboration: automatically determines task complexity and matches the most economical cloud model - **[2026.02.12]** 🎉 EdgeClaw is officially open-sourced — an Edge-Cloud Collaborative AI Agent --- ## 💡 About EdgeClaw EdgeClaw is an **Edge-Cloud Collaborative AI Agent** jointly developed by [THUNLP (Tsinghua University)](https://nlp.csai.tsinghua.edu.cn), [Renmin University of China](http://ai.ruc.edu.cn/), [AI9Stars](https://github.com/AI9Stars), [ModelBest](https://modelbest.cn/en), and [OpenBMB](https://www.openbmb.cn/home), built on top of [OpenClaw](https://github.com/openclaw/openclaw). In current AI Agent architectures, the edge side has long been overlooked — all data and tasks are funneled to the cloud, leading to privacy leaks and wasted compute. EdgeClaw reactivates the value of edge computing by constructing a customizable three-tier security system (S1 Passthrough / S2 Desensitization / S3 Local). Through a dual-engine on the edge (rule-based detection ~0ms + local LLM semantic detection ~1–2s), it classifies the sensitivity and complexity of every request in real time, then routes each request through a unified composable pipeline to the most privacy-safe and cost-effective processing path. With intelligent edge-cloud forwarding, developers can achieve seamless privacy protection — "public data to cloud, sensitive data desensitized, private data stays local" — without modifying any business logic.
| **🤝 Edge-Cloud Division of Labor** The edge perceives data attributes (sensitivity, complexity); the cloud handles reasoning and generation. The edge covers the cloud's blind spots (sensitive data never leaves the device), while the cloud compensates for the edge's limitations (complex tasks are offloaded to the cloud). | **🔒 Three-Tier Security Collaboration** Safe data (S1) — sent directly to the cloud; Sensitive data (S2) — desensitized on-device before forwarding to the cloud; Private data (S3) — processed entirely on-device, with the cloud only maintaining context continuity. |
| **💰 Cost-Aware Collaboration** A local LLM semantically judges task complexity, routing simple tasks to cheap models and reserving expensive models for complex tasks only. In typical workflows, 60–80% of requests are forwarded to low-cost models, drastically cutting cloud token expenses. | **🚀 Plug-and-Play, Zero Code Changes** EdgeClaw automatically intercepts and routes via its Hook mechanism — no modifications to any business logic required. It serves as a seamless drop-in replacement for OpenClaw. |
∀ x, Detect(x) = S₃ ⟹ x ∉ Cloud(x)
**Theorem 2 (Desensitization Completeness)**: For any S2-level data _x_, the cloud-visible form contains none of the original privacy entity values:∀ x, Detect(x) = S₂ ⟹ ∀ (ti, vi) ∈ Extract(x), vi ∉ Cloud(x)
--- ## 💰 Feature Two: Cost-Aware Collaboration ### Why Cost-Aware Collaboration? In a typical AI coding assistant workflow, most requests involve browsing files, reading code, and simple Q&A — using the most expensive model for these tasks is pure waste. Cost-Aware Collaboration uses a local small model as an LLM-as-Judge, classifying requests by complexity and routing them to cloud models at different price tiers. | Complexity | Task Examples | Default Target Model | | ------------- | -------------------------------------------------------------- | -------------------- | | **SIMPLE** | Queries, translation, formatting, greetings | `gpt-4o-mini` | | **MEDIUM** | Code generation, single-file editing, email drafting | `gpt-4o` | | **COMPLEX** | System design, multi-file refactoring, cross-document analysis | `claude-sonnet-4.6` | | **REASONING** | Mathematical proofs, formal logic, experiment design | `o4-mini` | ### Why LLM-as-Judge Instead of Keyword Rules? | Approach | Pros | Cons | | ---------------- | ---------------------------------------- | --------------------------------------------------- | | Keyword Rules | Fast | No semantic understanding, high false-positive rate | | **LLM-as-Judge** | **Semantic understanding, multilingual** | One additional local model call (~1–2s) | The Judge runs on a local small model (e.g., MiniCPM-4.1 / Qwen3.5), with latency of approximately 1–2 seconds. ### Smart Caching Prompt hash caching (SHA-256, TTL 5 minutes) — identical requests are not re-judged, further reducing latency overhead. ### How Much Do You Save? In a typical coding assistant workflow, Cost-Aware Collaboration can route **60–80% of requests** to cheaper models. --- ## 🚀 Composable Router Pipeline Security collaboration and cost-aware collaboration run in the **same pipeline**, working together via weights and a two-phase short-circuit strategy: ``` User Message │ ▼ RouterPipeline.run() │ ├── Phase 1: Fast routers (weight ≥ 50) run in parallel │ └── security router → three-tier sensitivity detection │ ├── Short-circuit: If Phase 1 detects sensitive data → skip Phase 2 │ └── Phase 2: Slow routers (weight < 50) run on demand └── cost-aware router → LLM Judge task complexity classification ``` **Design Philosophy**: Security first — the security router runs first with high weight. If sensitive data is found, it short-circuits immediately without wasting time on complexity judgment. Cost-aware collaboration kicks in only after the security check passes (S1). ### End-to-End Pipeline Formalization ``` ⎧ θ_cloud(m) if a = passthrough m ─[c_msg]→ Detect(m) → l ─[c_route]→ R(l) → a → ⎨ θ_cloud(De(m)) if a = desensitize ⎩ θ_local(m) if a = redirect ─[c_persist]→ W(m, l) ─[c_end]→ Sync ``` ### 10 Hooks Covering the Complete Lifecycle | Hook | Trigger Point | Core Responsibility | | ---------------------- | -------------------------- | ------------------------------------------- | | `before_model_resolve` | Before model selection | Run pipeline → routing decision | | `before_prompt_build` | Before prompt construction | Inject Guard Prompt / S2 markers | | `before_tool_call` | Before tool invocation | File access guard + sub-agent guard | | `after_tool_call` | After tool invocation | Tool result detection | | `tool_result_persist` | Result persistence | Dual-track session write | | `before_message_write` | Before message write | S3 → placeholder, S2 → desensitized version | | `session_end` | Session ends | Memory synchronization | | `message_sending` | Outbound message | Detect and desensitize/cancel | | `before_agent_start` | Before sub-agent starts | Task content guard | | `message_received` | Message received | Observability logging | --- ## 📦 Installation We provide two installation methods: from source (recommended) and local LLM environment setup. ### Method 1: Install from Source ```bash git clone https://github.com/openbmb/edgeclaw.git cd edgeclaw pnpm install pnpm build pnpm ui:build pnpm openclaw onboard --install-daemon ``` ### Method 2: Local LLM Environment EdgeClaw requires a local inference backend for privacy detection and the Guard Agent. We recommend Ollama: ```bash # macOS brew install ollama # Linux curl -fsSL https://ollama.ai/install.sh | sh # Pull the recommended model ollama pull openbmb/minicpm4.1 # Start the service ollama serve ``` All OpenAI-compatible APIs are also supported, including vLLM, LMStudio, SGLang, TGI, etc. See `config.example.json` for backend configuration examples. ### Verify Installation ```bash pnpm openclaw gateway run ``` If you see the GuardClaw plugin loading logs, the installation was successful. --- ## 🚀 Quick Start ### 1. Enable the GuardClaw Plugin Add the following to `openclaw.json`: ```json { "plugins": { "entries": { "GuardClaw": { "enabled": true, "config": { "privacy": { "enabled": true, "localModel": { "enabled": true, "provider": "ollama", "model": "openbmb/minicpm4.1", "endpoint": "http://localhost:11434" }, "guardAgent": { "id": "guard", "workspace": "~/.openclaw/workspace-guard", "model": "ollama/openbmb/minicpm4.1" } } } } } } } ``` ### 2. Configure the Guard Agent Register the Guard Agent in the `agents` section of `openclaw.json`: ```json { "agents": { "list": [ { "id": "main", "workspace": "~/.openclaw/workspace-main", "subagents": { "allowAgents": ["guard"] } }, { "id": "guard", "workspace": "~/.openclaw/workspace-guard", "model": "ollama/openbmb/minicpm4.1" } ] } } ``` ### 3. Launch ```bash pnpm openclaw gateway run ``` EdgeClaw automatically intercepts and routes — no modifications to any business logic required. ### 4. (Optional) Enable Cost-Aware Collaboration Enable it in `privacy.routers`: ```json { "privacy": { "routers": { "token-saver": { "enabled": true, "weight": 30, "options": { "tiers": { "SIMPLE": { "provider": "openai", "model": "gpt-4o-mini" }, "MEDIUM": { "provider": "openai", "model": "gpt-4o" }, "COMPLEX": { "provider": "anthropic", "model": "claude-sonnet-4.6" }, "REASONING": { "provider": "openai", "model": "o4-mini" } } } } } } } ``` --- ## 🎬 Demo