shibaclaw 0.1.6

A lightweight personal AI assistant framework

ShibaClaw 🐕

Security-first AI agent with built-in WebUI, native provider support, and hardened tools.

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📢 Welcome to ShibaClaw v0.1.6! This major update overhauls the WebUI architecture with a native WebSocket handler and modular API routers. See the Changelog for details.

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ShibaClaw is a security-first AI agent for your terminal and browser. Security isn't glue code — it's the foundation: CVE auditing at install time, prompt-injection wrapping on every tool result, SSRF/DNS-rebinding protection, shell hardening, workspace sandboxing, and bearer-token auth are all built into the core.

22 providers · 11 chat channels · built-in WebUI · 3-level proactive memory · cron · heartbeat · skills · MCP

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Quick Start

Docker

git clone https://github.com/RikyZ90/ShibaClaw.git && cd ShibaClaw
docker compose up -d --build
docker exec -it shibaclaw-gateway shibaclaw print-token

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

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.

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Security, Built In

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

🛡️ 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>). 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.

Security Layers Overview

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

Full disclosure policy and supported versions: SECURITY.md

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WebUI

  

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

• Chat — multi-session conversations with live streaming of tool calls, thinking blocks, and elapsed time
• 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 agent, provider, 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
• Auto-update — checks GitHub releases every 12h, notifies in the UI and on all active channels
• Responsive — works on desktop and mobile

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.

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Features

🧠 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.

Why this matters: 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.

Workflow & Reasoning

• 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
cron	Schedule one-time or recurring 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, cron reference, 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

• Cron service — persistent, timezone-aware scheduled jobs stored in jobs.json. Supports every, cron, and at schedules. Overdue jobs fire on startup.
• Heartbeat — periodic wake-up reads HEARTBEAT.md, uses its frontmatter for session/profile/targets, keeps enable/interval in global settings, skips the LLM entirely when Active Tasks is empty, and only asks the model to decide when real active work exists.

If you are upgrading from an older release, it is recommended to reset your workspace HEARTBEAT.md once so you get the new frontmatter-based base template. Existing files still work, but they will not gain the new editable settings block automatically.

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Supported Providers

ShibaClaw uses native SDKs (no LiteLLM proxy) and auto-detects the right provider from the model name.

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) · vLLM · any OpenAI-compatible endpoint.

OAuth

Provider	Flow	Setup
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

💡 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.

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🔌 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.

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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), cron service	3000

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

Single-process mode

shibaclaw web runs agent + WebUI + cron 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 + cron 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)

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[0.1.6] - 2026-04-25

Added

• API Modularization — Refactored WebUI backend into focused routers for onboarding, settings, and sessions.
• Native WebSocket Transport — Replaced Socket.IO with a lightweight, native WebSocket handler for real-time communication.

[0.1.5] - 2026-04-24

[0.1.4] - 2026-04-24

Fixed

• AttributeError: 'list' object has no attribute 'strip' in memory consolidation for multi-part message content. (Thanks @itskun! — #18)
• shibaclaw channels status now shows channels with missing optional dependencies instead of silently hiding them.

Added

• SHIBACLAW_DEBUG=true env var to enable debug logging without --verbose.

→ Full changelog

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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

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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.

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🌟 Support ShibaClaw

If you find this project useful or if it helps you build a more secure and powerful AI workflow, please consider giving it a Star!

Your support helps ShibaClaw grow, reach more developers, and stay updated with the latest AI advancements. Thank you for being part of the journey! ❤️

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Inspired by NanoBot by HKUDS — MIT License.

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