* feat(llm): add smart model routing based on request complexity Automatically selects optimal model tier (flash/standard/pro/frontier) for each request based on 13-dimension complexity scoring: - Reasoning words, multi-step signals, code indicators - Domain-specific terms, creativity, precision - Safety sensitivity, tool likelihood, question complexity - Token estimate, context dependency, sentence complexity Features: - Pattern overrides for fast-path routing (greetings → flash, security audits → frontier) - Configurable tier-to-model mappings (defaults to -latest aliases) - Thinking mode per tier (pro: low, frontier: medium) - User-configurable pattern overrides - Zero-config for default benefits, full control for power users Expected cost savings: 50-70% vs always-using-frontier baseline. Refs: smart-routing-spec.md * fix(routing): address Gemini Code Assist review feedback - Add tracing warnings for invalid tier/regex in user overrides (router.rs) - Use unreachable!() for tier hint match since regex enforces valid tiers (scorer.rs) - Refactor weighted total to array iteration for maintainability (scorer.rs) - Add TODO for making domain keywords configurable (scorer.rs) Refs: PR #208 * feat(routing): make domain keywords configurable - Add ScorerConfig with optional domain_keywords field - Add DEFAULT_DOMAIN_KEYWORDS constant (exported for reference) - Add domain_keywords to RouterConfig for top-level configuration - Build domain regex at runtime from config, fallback to defaults - Add score_complexity_with_config() function - Add test for custom domain keywords Users can now provide project-specific keywords: RouterConfig { domain_keywords: Some(vec!["mycompany".into(), "myproduct".into()]), ..Default::default() } Addresses Gemini Code Assist review feedback on PR #208. Tests: 20/20 passing * docs: add domain_keywords to routing config example * feat: integrate 13-dimension complexity scorer into smart routing (takeover #208) Folds the 13-dimension complexity scorer and pattern overrides from PR #208 into the existing SmartRoutingProvider, replacing the simpler keyword-based classifier. Adds 4-tier system (Flash/Standard/Pro/Frontier), configurable scorer weights, domain keywords, regex pattern overrides, tier hints, and multi-dimensional boost. Removes separate routing/ directory and lazy_static dependency in favor of std::sync::LazyLock. Includes 44 tests covering all scoring dimensions, tier boundaries, pattern overrides, and provider routing. Co-Authored-By: onlyamicrowave <[email protected]> Co-Authored-By: Claude Opus 4.6 <[email protected]> * fix: address review feedback on smart routing PR (#529) - Cache compiled domain regex in SmartRoutingProvider (built once at construction, not per-request) and add score_complexity_with_regex() API - Check explicit tier hints before pattern overrides so user intent wins (e.g. "[tier:flash] security audit" routes as Flash, not Frontier) - Trim input before matching/scoring so trailing whitespace doesn't break anchored override regexes or skew token-length scoring - Fix token estimate comment (>=520 chars = 100, not >500) - Update spec: check implementation plan boxes, fix file paths, add note that llm.routing YAML schema is target design (current config uses env vars) - Add regression tests for tier hint precedence and trimmed greeting matching Co-Authored-By: Claude Opus 4.6 <[email protected]> * fix: restore Cargo.lock from main to fix html_to_markdown test The lockfile was fully regenerated during the PR #208 merge conflict resolution, which bumped html-to-markdown-rs from 2.25.1 to 2.27.2. The new version produces different output that breaks the golden-file snapshot test. Restore the original lockfile from main — lazy_static was never in main's lockfile, so no further changes needed. Co-Authored-By: Claude Opus 4.6 <[email protected]> * fix: address second round of review feedback (#529) - Tighten quick-lookup override regex with end anchor to prevent matching complex questions like "What time complexity is merge sort?" - Handle empty domain keywords list by falling back to defaults instead of producing a broken regex that matches empty strings everywhere - Clarify spec architecture diagram: current impl uses 2-provider split (cheap/primary), per-tier model mapping is target design - Add regression tests for both fixes Co-Authored-By: Claude Opus 4.6 <[email protected]> --------- Co-authored-by: Microwave <[email protected]> Co-authored-by: Joe <[email protected]> Co-authored-by: onlyamicrowave <[email protected]> Co-authored-by: Claude Opus 4.6 <[email protected]>
IronClaw
Your secure personal AI assistant, always on your side
Philosophy • Features • Installation • Configuration • Security • Architecture
Philosophy
IronClaw is built on a simple principle: your AI assistant should work for you, not against you.
In a world where AI systems are increasingly opaque about data handling and aligned with corporate interests, IronClaw takes a different approach:
- Your data stays yours - All information is stored locally, encrypted, and never leaves your control
- Transparency by design - Open source, auditable, no hidden telemetry or data harvesting
- Self-expanding capabilities - Build new tools on the fly without waiting for vendor updates
- Defense in depth - Multiple security layers protect against prompt injection and data exfiltration
IronClaw is the AI assistant you can actually trust with your personal and professional life.
Features
Security First
- WASM Sandbox - Untrusted tools run in isolated WebAssembly containers with capability-based permissions
- Credential Protection - Secrets are never exposed to tools; injected at the host boundary with leak detection
- Prompt Injection Defense - Pattern detection, content sanitization, and policy enforcement
- Endpoint Allowlisting - HTTP requests only to explicitly approved hosts and paths
Always Available
- Multi-channel - REPL, HTTP webhooks, WASM channels (Telegram, Slack), and web gateway
- Docker Sandbox - Isolated container execution with per-job tokens and orchestrator/worker pattern
- Web Gateway - Browser UI with real-time SSE/WebSocket streaming
- Routines - Cron schedules, event triggers, webhook handlers for background automation
- Heartbeat System - Proactive background execution for monitoring and maintenance tasks
- Parallel Jobs - Handle multiple requests concurrently with isolated contexts
- Self-repair - Automatic detection and recovery of stuck operations
Self-Expanding
- Dynamic Tool Building - Describe what you need, and IronClaw builds it as a WASM tool
- MCP Protocol - Connect to Model Context Protocol servers for additional capabilities
- Plugin Architecture - Drop in new WASM tools and channels without restarting
Persistent Memory
- Hybrid Search - Full-text + vector search using Reciprocal Rank Fusion
- Workspace Filesystem - Flexible path-based storage for notes, logs, and context
- Identity Files - Maintain consistent personality and preferences across sessions
Installation
Prerequisites
- Rust 1.85+
- PostgreSQL 15+ with pgvector extension
- NEAR AI account (authentication handled via setup wizard)
Download or Build
Visit Releases page to see the latest updates.
Install via Windows Installer (Windows)
Download the Windows Installer and run it.
Install via powershell script (Windows)
irm https://github.com/nearai/ironclaw/releases/latest/download/ironclaw-installer.ps1 | iex
Install via shell script (macOS, Linux, Windows/WSL)
curl --proto '=https' --tlsv1.2 -LsSf https://github.com/nearai/ironclaw/releases/latest/download/ironclaw-installer.sh | sh
Install via Homebrew (macOS/Linux)
brew install ironclaw
Compile the source code (Cargo on Windows, Linux, macOS)
Install it with cargo, just make sure you have Rust installed on your computer.
# Clone the repository
git clone https://github.com/nearai/ironclaw.git
cd ironclaw
# Build
cargo build --release
# Run tests
cargo test
For full release (after modifying channel sources), run ./scripts/build-all.sh to rebuild channels first.
Database Setup
# Create database
createdb ironclaw
# Enable pgvector
psql ironclaw -c "CREATE EXTENSION IF NOT EXISTS vector;"
Configuration
Run the setup wizard to configure IronClaw:
ironclaw onboard
The wizard handles database connection, NEAR AI authentication (via browser OAuth),
and secrets encryption (using your system keychain). Settings are persisted in the
connected database; bootstrap variables (e.g. DATABASE_URL, LLM_BACKEND) are
written to ~/.ironclaw/.env so they are available before the database connects.
Alternative LLM Providers
IronClaw defaults to NEAR AI but works with any OpenAI-compatible endpoint. Popular options include OpenRouter (300+ models), Together AI, Fireworks AI, Ollama (local), and self-hosted servers like vLLM or LiteLLM.
Select "OpenAI-compatible" in the wizard, or set environment variables directly:
LLM_BACKEND=openai_compatible
LLM_BASE_URL=https://openrouter.ai/api/v1
LLM_API_KEY=sk-or-...
LLM_MODEL=anthropic/claude-sonnet-4
See docs/LLM_PROVIDERS.md for a full provider guide.
Security
IronClaw implements defense in depth to protect your data and prevent misuse.
WASM Sandbox
All untrusted tools run in isolated WebAssembly containers:
- Capability-based permissions - Explicit opt-in for HTTP, secrets, tool invocation
- Endpoint allowlisting - HTTP requests only to approved hosts/paths
- Credential injection - Secrets injected at host boundary, never exposed to WASM code
- Leak detection - Scans requests and responses for secret exfiltration attempts
- Rate limiting - Per-tool request limits to prevent abuse
- Resource limits - Memory, CPU, and execution time constraints
WASM ──► Allowlist ──► Leak Scan ──► Credential ──► Execute ──► Leak Scan ──► WASM
Validator (request) Injector Request (response)
Prompt Injection Defense
External content passes through multiple security layers:
- Pattern-based detection of injection attempts
- Content sanitization and escaping
- Policy rules with severity levels (Block/Warn/Review/Sanitize)
- Tool output wrapping for safe LLM context injection
Data Protection
- All data stored locally in your PostgreSQL database
- Secrets encrypted with AES-256-GCM
- No telemetry, analytics, or data sharing
- Full audit log of all tool executions
Architecture
┌────────────────────────────────────────────────────────────────┐
│ Channels │
│ ┌──────┐ ┌──────┐ ┌─────────────┐ ┌─────────────┐ │
│ │ REPL │ │ HTTP │ │WASM Channels│ │ Web Gateway │ │
│ └──┬───┘ └──┬───┘ └──────┬──────┘ │ (SSE + WS) │ │
│ │ │ │ └──────┬──────┘ │
│ └─────────┴──────────────┴────────────────┘ │
│ │ │
│ ┌─────────▼─────────┐ │
│ │ Agent Loop │ Intent routing │
│ └────┬──────────┬───┘ │
│ │ │ │
│ ┌──────────▼────┐ ┌──▼───────────────┐ │
│ │ Scheduler │ │ Routines Engine │ │
│ │(parallel jobs)│ │(cron, event, wh) │ │
│ └──────┬────────┘ └────────┬─────────┘ │
│ │ │ │
│ ┌─────────────┼────────────────────┘ │
│ │ │ │
│ ┌───▼─────┐ ┌────▼────────────────┐ │
│ │ Local │ │ Orchestrator │ │
│ │Workers │ │ ┌───────────────┐ │ │
│ │(in-proc)│ │ │ Docker Sandbox│ │ │
│ └───┬─────┘ │ │ Containers │ │ │
│ │ │ │ ┌───────────┐ │ │ │
│ │ │ │ │Worker / CC│ │ │ │
│ │ │ │ └───────────┘ │ │ │
│ │ │ └───────────────┘ │ │
│ │ └─────────┬───────────┘ │
│ └──────────────────┤ │
│ │ │
│ ┌───────────▼──────────┐ │
│ │ Tool Registry │ │
│ │ Built-in, MCP, WASM │ │
│ └──────────────────────┘ │
└────────────────────────────────────────────────────────────────┘
Core Components
| Component | Purpose |
|---|---|
| Agent Loop | Main message handling and job coordination |
| Router | Classifies user intent (command, query, task) |
| Scheduler | Manages parallel job execution with priorities |
| Worker | Executes jobs with LLM reasoning and tool calls |
| Orchestrator | Container lifecycle, LLM proxying, per-job auth |
| Web Gateway | Browser UI with chat, memory, jobs, logs, extensions, routines |
| Routines Engine | Scheduled (cron) and reactive (event, webhook) background tasks |
| Workspace | Persistent memory with hybrid search |
| Safety Layer | Prompt injection defense and content sanitization |
Usage
# First-time setup (configures database, auth, etc.)
ironclaw onboard
# Start interactive REPL
cargo run
# With debug logging
RUST_LOG=ironclaw=debug cargo run
Development
# Format code
cargo fmt
# Lint
cargo clippy --all --benches --tests --examples --all-features
# Run tests
createdb ironclaw_test
cargo test
# Run specific test
cargo test test_name
- Telegram channel: See docs/TELEGRAM_SETUP.md for setup and DM pairing.
- Changing channel sources: Run
./channels-src/telegram/build.shbeforecargo buildso the updated WASM is bundled.
OpenClaw Heritage
IronClaw is a Rust reimplementation inspired by OpenClaw. See FEATURE_PARITY.md for the complete tracking matrix.
Key differences:
- Rust vs TypeScript - Native performance, memory safety, single binary
- WASM sandbox vs Docker - Lightweight, capability-based security
- PostgreSQL vs SQLite - Production-ready persistence
- Security-first design - Multiple defense layers, credential protection
License
Licensed under either of:
- Apache License, Version 2.0 (LICENSE-APACHE)
- MIT License (LICENSE-MIT)
at your option.
