shibaclaw 0.5.3

A lightweight personal AI assistant framework

ShibaClaw 🐕

The AI agent that just works — securely, privately, without babysitting.

22 Providers · 11 Chat Channels · Built-in WebUI · Security-First Core · MCP Ready

Built on three pillars: Simplicity · Security · Privacy

---

📢 Latest Release: v0.5.3 — Click to see what's new

This major release brings a complete overhaul to background tasks:

• Unified Automation Engine & UI — Refactored "Cron" and "Heartbeat" systems into a single "Automations" engine with a dedicated, premium control center in the WebUI.
• Realtime Background Telemetry — The UI global status indicator now pulses gold ("Executing...") whenever any automated job runs silently in the background.
• Native TASK.md Syncing — The WebUI now reads and writes TASK.md directly via the filesystem, ensuring perfect synchronization between the UI and the agent's directives.
• Boot Storm Prevention — Recurring jobs that were missed while the gateway was offline are now silently "fast-forwarded" to their next occurrence upon boot, preventing instant execution storms.
• Modern Workspace Summary — A sleek new glassmorphic widget at the bottom of the sidebar displays Active Channels, the Configured Provider, and strict Workspace Restriction status.

See the Changelog for full release history.

---

✨ Everything in One Agent

🛡️ Security-First CVE audit, prompt-injection wrap, SSRF guard — on by default	🧠 Smart Memory 3-level system with proactive learning & auto-compaction	🌐 22 Providers Native SDKs, no LiteLLM proxy OpenAI · Anthropic · Gemini · DeepSeek...
📱 Web & Mobile Expose the WebUI on your LAN and use the same agent from your phone	🖥️ Desktop App Native Windows launcher with tray, perfect combo with the WebUI	🔌 MCP Ready Connect any MCP server, tools auto-registered

---

Why ShibaClaw? Simply Works. 🐕

Tired of agents that need more babysitting than your actual work?
ShibaClaw is engineered around one principle: it just works — securely, reliably, and without constant maintenance.

Most AI agent frameworks treat security as an afterthought, leave you wrestling with provider compatibility, or force you to babysit configurations. ShibaClaw flips the script: security isn't bolted on, it's the foundation.

What makes ShibaClaw different:

• Security layers built into the core — CVE auditing at install time, prompt-injection wrapping on every tool result, SSRF/DNS-rebinding protection
• Native provider support — 22 providers via their official SDKs, no proxy layer to debug
• One-command setup — Docker or pip, follow the wizard, you're chatting in about a minute
• Runs everywhere — Terminal, WebUI, Discord, Telegram, WhatsApp, Windows desktop app, and more

---

🛡️ Security, Built In

Defenses that are normally scattered across app glue or external proxies — in ShibaClaw they ship in the core, on by default.

Core Security Layers

Layer	What it does
🔍 Install-time audit	Audits pip and npm before execution — blocks critical/high CVEs before they land
🛡️ Prompt-injection wrapping	Wraps every tool result in a randomized <tool_output_...> boundary and sanitizes closing tags
🔒 Shell hardening	20+ deny patterns, escape normalization (\x.., \u....), internal URL detection
🌐 Network guard	SSRF filtering, redirect revalidation, DNS-rebinding-safe resolution
📁 Workspace sandbox	File tools and file browser locked to the configured workspace
🔑 Access control	Bearer token auth, constant-time checks, channel allowlists, optional rate limiting
⚡ Distributed engine	UI (≈128 MB) decoupled from agent brain (≈256 MB+) — minimal footprint per process

🛡️ Prompt-Injection Wrapping (Tool Sandboxing)

Instead of simply feeding raw tool outputs back to the LLM, ShibaClaw wraps every tool result in a dynamically generated XML-like boundary with a randomized nonce (e.g., <tool_output_a1b2c3d4>).

💡 Standalone Defense: This core security mechanism (Randomized Tool Output Wrapping) has been decoupled and packaged as a standalone, zero-dependency Python library called Muzzle. You can use Muzzle to protect any agent framework (LangChain, LlamaIndex, CrewAI, AutoGen, or custom LLM loops) using this identical technique.

Why this matters: attackers often try to prematurely close tags or inject fake system instructions inside tool outputs (like web page content). By using a randomized boundary generated per-iteration, the agent can reliably differentiate between actual system instructions and injected payloads. Furthermore, any attempt to inject the specific closing tag inside the content is automatically sanitized and escaped, ensuring the sandbox remains airtight and the original system prompt takes precedence.

🔍 Install-Time Package Autoscan

Before executing any pip, npm, or apt install command, ShibaClaw intercepts the action and parses the dependencies. It runs tools like pip-audit or npm audit --json to scan for known vulnerabilities against CVE databases before applying any changes.

Why this matters: it shifts security entirely to the left. Instead of blindly blocking package managers or relying on post-install scans, it evaluates the exact dependency tree before execution. If a package contains critical/high CVEs, or if suspicious flags (like --allow-unauthenticated for apt) are detected, the installation is blocked. This allows the AI to autonomously build software without turning the host into a liability.

Full disclosure policy and supported versions: SECURITY.md

---

⚡ Quick Start

Docker

curl -fsSL https://raw.githubusercontent.com/RikyZ90/ShibaClaw/main/docker-compose.yml -o docker-compose.yml
docker compose up -d     # pulls from Docker Hub
docker exec -it shibaclaw-gateway shibaclaw print-token

Open http://localhost:3000, paste the token, and follow the onboard wizard.

Expose shibaclaw-web on your LAN (e.g. via reverse proxy) and open the same URL from your phone to chat with your agent on mobile.

pip

pip install shibaclaw
shibaclaw web --with-gateway   # starts WebUI + agent engine on :3000

Open http://localhost:3000 and follow the onboard wizard.
Prefer the CLI? shibaclaw onboard runs the same guided setup from the terminal.

---

🖥️ Native Desktop App (Windows)

ShibaClaw features a fully integrated Windows Desktop Launcher built with pywebview. It offers a seamless local experience without the need to manage background terminal windows.

• System Tray Integration: Close the window to minimize ShibaClaw silently into the system tray. Right-click the Shiba icon to re-open the UI, access workspace logs, visit the website, or gracefully quit the engine.
• Auto-Login: When using the Desktop Launcher locally, WebUI authentication is bypassed by default for a smoother local-first experience.
• Embedded WebUI: No need to open your own browser; the WebUI runs inside a dedicated native window frame.
• Portable & Lightweight: Packaged as a single standalone folder using PyInstaller to run instantly without requiring Python on the host machine.

If you installed via pip:

shibaclaw desktop

Or download the pre-built Windows executable directly from the latest release:

⬇ Download ShibaClaw.exe (latest)
Full release notes → github.com/RikyZ90/ShibaClaw/releases/latest

---

🌐 WebUI

  

The WebUI is built-in — no separate frontend or Node.js required.

Expose it on your local network and open the same URL from your phone or tablet — no extra apps, just a browser.

• Chat — multi-session conversations with live streaming of tool calls, thinking blocks, elapsed time, and per-session model switching from the chat footer
• Cross-provider model search — one searchable picker merges models from all configured providers, shows provider labels, and switches the live runtime provider when you change the session model
• Agent Profiles — switch personas per session (Hacker, Builder, Planner, Reviewer) with dynamic avatars
• File browser — browse, view, and edit workspace files in-browser (sandboxed to workspace)
• Voice — speech-to-text via OpenAI-compatible audio APIs and browser-native TTS
• Settings — configure default session model, memory / consolidation model, providers, tools, MCP servers, channels, skills, and OAuth from a single panel
• Onboard wizard — guided first-time setup: pick a provider, enter API key or start OAuth, choose a model
• Context viewer — inspect the full system prompt and token usage breakdown
• Gateway monitor — health check and one-click restart
• OAuth flows — GitHub Copilot, OpenAI Codex, and OpenRouter can all be configured from the settings modal; OpenRouter stores the returned API key directly into provider settings
• Hardened rendering — chat Markdown escapes raw HTML, file names render through safe DOM nodes, and expired auth returns cleanly to login without reconnect loops
• Auto-update — checks GitHub releases every 12h, notifies in the UI and on all active channels
• Notification Center (WIP) — bell icon with unread badge, real-time WebSocket push, per-notification deep-link to the related session; covers background automations, agent responses, and update alerts
• Responsive — works great on desktop and mobile; open the same agent UI from your couch, not only from your desk

⚡ Dynamic Model Selection

Change models per session — no more single global model, but a flexible choice for every conversation.

• Multi-Provider Search: Search through all models from all your configured providers (OpenRouter, GitHub Copilot, Anthropic, etc.) in a single dropdown.
• Session-Aware Routing: Each session remembers its chosen model. You can have a coding session with Claude 3.5 Sonnet and a research session with Gemma 4 simultaneously.
• Runtime Switching: Switch models instantly without restarting the agent; the gateway automatically resolves the correct endpoint based on the selected model.
• Dedicated Memory Model: Configure a separate model and provider specifically for memory consolidation and proactive learning, ensuring high-quality state extraction without affecting your chat budget.
• Default-First: New sessions automatically start with the default model set in settings, ensuring immediate consistency.

🤖 Agent Profiles

Switch the agent's personality on-the-fly without losing context. Each profile overrides the system prompt (SOUL.md) while keeping model, memory, and tools shared. Profiles are per-session — run a security audit in one tab and plan architecture in another.

Built-in profiles: Default · Builder · Planner · Reviewer · Hacker (elite security expert with 50+ tool recommendations, OWASP/MITRE/NIST methodologies, CVSS scoring, and a custom cyber-shiba avatar).

Create your own profiles interactively — the agent walks you through defining the persona and saves everything automatically.

---

🧠 Advanced 3-Level Memory System

ShibaClaw's memory isn't just a rolling chat buffer; it's a structured, proactive system designed for long-term operational continuity.

• USER.md (Identity & Preferences): Stores durable personal facts, communication styles, and language preferences. The agent reads this to know who you are.
• MEMORY.md (Operational State): The agent's working knowledge. It tracks environment details, recurring entities, and project state.
• HISTORY.md (Session Archive): An append-only, searchable ledger of past sessions with timestamped, tagged summaries.

Instead of bloating the system prompt with thousands of messages, ShibaClaw features a Proactive Learning loop. Every N messages, a background LLM process silently extracts new durable facts and updates USER.md and MEMORY.md, without interrupting the conversation. When MEMORY.md grows too large, an auto-compaction routine summarizes and deduplicates the context, prioritizing recent state while keeping token usage within strict budgets. When the agent needs older context, it can autonomously search HISTORY.md using TF-IDF and recency scoring. This separation of concerns ensures the agent stays hyper-aware of the current project without ever hitting token limits or losing focus.

---

🛠️ Features

Workflow & Reasoning

• Model-first session routing — each session stores its own selected model, and ShibaClaw resolves the correct provider backend from that model at runtime
• Focused background delegation — the spawn tool can offload a specific task and report back into the main session when done
• Advanced reasoning — supports extended thinking (Anthropic), reasoning effort (OpenAI o-series), and DeepSeek-R1 chains

Tools

Tool	What it does
exec	Shell commands with 20+ deny-pattern guards, encoding normalization, and CVE scanning
read_file / write_file / edit_file	Paginated reads, fuzzy find-and-replace, auto-created parent dirs
web_search	Brave, Tavily, SearXNG, Jina, or DuckDuckGo (fallback, no key needed)
web_fetch	HTTP fetch with SSRF protection, DNS rebinding defense, and redirect validation
memory_search	Ranked search over session history (TF-IDF + recency + importance scoring)
message	Cross-channel messaging with media attachments
automation	Manage or schedule background jobs (cron expressions, intervals, ISO dates, timezone-aware)
spawn	Optional background worker for a focused task; reports back to the main session when done
MCP	Connect any MCP server (stdio, SSE, or streamable HTTP) — tools auto-registered as mcp_<server>_<tool>

Channels

Telegram · Discord · Slack · WhatsApp · Matrix · Email · DingTalk · Feishu · QQ · WeCom · MoChat

All channels route through the same message bus. WhatsApp uses a Node.js bridge (Baileys) for QR-based linking.

Skills

8 built-in skills (GitHub, weather, summarize, tmux, automation, memory guide, skill-creator, ClawHub browser). Skills are Markdown files with YAML frontmatter and optional scripts — create your own or install from ClawHub. Pin frequently-used skills to load them on every conversation.

Automation

• Automations Engine — persistent, timezone-aware scheduled jobs and background interval routines managed via a unified UI modal and stored in automation.json. Supports every, cron, and at schedules. Missed jobs are automatically fast-forwarded on startup to prevent execution storms.
• TASK.md Integration — the engine uses TASK.md as the unified source of truth for background routines, skipping the LLM entirely when tasks are empty to save tokens and only processing active directives.

If you are upgrading from an older release, HEARTBEAT.md has been deprecated and removed. Your tasks and schedules should be migrated to TASK.md and the new Automations UI.

---

🔌 MCP Ecosystem

ShibaClaw is fully compatible with the Model Context Protocol (MCP), transforming the agent from a standalone tool into a plug-and-play AI hub.

Instead of relying solely on built-in skills, ShibaClaw can connect to any MCP-compliant server, instantly granting your agent access to a vast universe of external data sources and professional tools without modifying a single line of core code.

Why this matters:

• Instant Extensibility: Plug in community-made MCP servers for Google Drive, Slack, GitHub, PostgreSQL, and more.
• Standardized Tooling: Leverage a universal protocol for AI-to-tool communication, ensuring stability and interoperability.
• Decoupled Architecture: Keep your agent lean while scaling its capabilities through a distributed network of MCP servers.

Configure your MCP servers directly in the Settings panel to start expanding ShibaClaw's horizons.

---

🌐 Supported Providers

ShibaClaw uses native SDKs (no LiteLLM proxy) and resolves the active provider from the selected model or canonical provider-prefixed model ID. In the WebUI, all configured provider catalogs are merged into a single searchable list, while each session keeps its own chosen model.

API Key

Provider	Env Variable
OpenAI	OPENAI_API_KEY
Anthropic	ANTHROPIC_API_KEY
DeepSeek	DEEPSEEK_API_KEY
Google Gemini	GEMINI_API_KEY ¹
Groq	GROQ_API_KEY
Moonshot	MOONSHOT_API_KEY
MiniMax	MINIMAX_API_KEY
Zhipu AI	ZAI_API_KEY
DashScope	DASHSCOPE_API_KEY

¹ Setting GEMINI_API_KEY in the environment is sufficient — no stored key required. The Google OpenAI-compatible endpoint is pre-configured.

Gateway / Proxy

OpenRouter · AiHubMix · SiliconFlow · VolcEngine · BytePlus — auto-detected by key prefix or api_base.

Local

Ollama (http://localhost:11434) · LM Studio · llama.cpp · vLLM · any OpenAI-compatible endpoint(http://localhost:1234/v1)

Note for Docker users: If you run ShibaClaw via Docker Compose, localhost points inside the container itself. To connect to a local server running on your host machine (like LM Studio or Ollama on Windows/Mac), use: http://host.docker.internal:1234/v1 (or 11434 for Ollama). On native Linux, use http://172.17.0.1:port.

OAuth

Provider	Flow	Setup
OpenRouter	PKCE browser flow, stores returned API key in provider config	WebUI Settings
GitHub Copilot	Device flow, auto token refresh	shibaclaw provider login github-copilot or WebUI Settings
OpenAI Codex	PKCE browser flow	shibaclaw provider login openai-codex or WebUI Settings

For OpenRouter, the callback reuses the current WebUI URL and port by default, so http://localhost:3000 is not a dedicated OAuth-only port. If you expose the WebUI behind a reverse proxy or need a different public callback origin, set SHIBACLAW_OPENROUTER_CALLBACK_BASE_URL=https://your-public-webui-host before starting the server.

💡 Pro Tip: Cost-Effective & Premium Models

ShibaClaw performs exceptionally well even without expensive API usage:

• Free/Open Models: We highly recommend using OpenRouter to access powerful free models like nvidia/nemotron-3-super-120b-a12b:free or gemma-4-31b-it:free.
• Unlimited Premium: If you use the GitHub Copilot OAuth integration, you gain access to premium models like raptor (oswe-vscode-prime) at zero additional cost, effectively giving you unlimited requests.

---

📊 How ShibaClaw Compares (Security-first)

This table is a rough, security-focused snapshot, based only on what is explicitly documented in public repos/docs as of May 2026.
❓ means “not clearly documented / not checked”, not “does not exist”.

Security Feature	ShibaClaw	OpenClaw	Hermes Agent	Nanobot	ZeroClaw
Install-time CVE auditing (pip, npm, apt)	✅	❌	❌	❌	❌
Prompt-injection wrapping on every tool result	✅	❌	❌	❌	❌
SSRF + DNS-rebinding protection built-in	✅	❌	❌	❌	❌

ShibaClaw focuses on shipping these defenses in the core engine, on by default, so you do not have to glue together external scanners and proxies just to run an agent safely.

---

🏗️ Architecture

Docker Compose

Service	Role	Default Port
shibaclaw-gateway	Core agent loop, message bus, channel integrations	19999 (HTTP) · 19998 (WS)
shibaclaw-web	WebUI (Starlette + native WebSocket), automations service	3000

Both share the ~/.shibaclaw/ volume (config, workspace, memory, automation jobs, media cache).

Single-process mode

shibaclaw web runs agent + WebUI + automations in a single process — no gateway container needed.

Stack

Layer	Technology
Server	Uvicorn → Starlette (ASGI)
Real-time	Native WebSocket (/ws on WebUI, port 19998 on gateway)
Frontend	Vanilla JS · Marked.js · Highlight.js
Sessions	JSONL append-only per session (cache-friendly for LLM prompt prefixes)

Resource usage

Component	Idle	Peak (install/compile)
Gateway	~120 MB	~350 MB
WebUI	~120 MB	~350 MB

Docker Compose sets a 512 MB limit / 256 MB reservation per container. Tool output is streamed with bounded buffers, so long-running commands (apt, npm install) can't blow up memory.

---

🔧 CLI Reference

shibaclaw web               # Start WebUI (agent + automations in-process)
shibaclaw gateway           # Start gateway only (for Docker split)
shibaclaw onboard           # CLI-based first-time setup wizard
shibaclaw agent -m "Hello"  # One-shot message via terminal
shibaclaw agent             # Interactive REPL with history
shibaclaw status            # Provider, workspace, OAuth health check
shibaclaw print-token       # Show WebUI auth token
shibaclaw channels status   # List enabled channels
shibaclaw provider login <p># OAuth login (github-copilot, openai-codex)
shibaclaw desktop           # Launch Windows desktop app

---

🐛 Troubleshooting

Problem	Try
General status check	shibaclaw status
Container logs	docker logs shibaclaw-gateway / docker logs shibaclaw-web
WebUI won't connect	Check token with shibaclaw print-token, verify port binding
Provider errors	shibaclaw status shows API key and OAuth state
Security policy	SECURITY.md

---

🤝 Contributing

See CONTRIBUTING.md — PRs welcome.

Channels are extensible via Python entry points (shibaclaw.integrations). Skill creation is documented in docs/CHANNEL_PLUGIN_GUIDE.md and the built-in skill-creator skill.

Gateway integrators: see docs/GATEWAY_PROTOCOL.md for the WebSocket contract on port 19998.

---

🌟 Join the ShibaClaw Pack

ShibaClaw is built by one developer, maintained by the community, and growing fast.
If it saved you time, secured your workflow, or just made you smile — leave a star ⭐

"The AI agent that just works. No babysitting required." 🐕

⭐ Star the repo  ·  🐛 Open an issue  ·  🔧 Send a PR  ·  💬 Join the Discord  ·  📦 Browse ClawHub

---

Inspired by NanoBot by HKUDS — MIT License.