* feat: add Codex auth.json token reuse for LLM authentication When LLM_USE_CODEX_AUTH=true, IronClaw reads the Codex CLI's auth.json (default ~/.codex/auth.json) and extracts the API key or OAuth access token. This lets IronClaw piggyback on a Codex login without implementing its own OAuth flow. New env vars: - LLM_USE_CODEX_AUTH: enable Codex auth fallback (default: false) - CODEX_AUTH_PATH: override path to auth.json * fix: handle ChatGPT auth mode correctly Switch base_url to chatgpt.com/backend-api/codex when auth.json contains ChatGPT OAuth tokens. The access_token is a JWT that only works against the private ChatGPT backend, not the public OpenAI API. Refactored codex_auth.rs to return CodexCredentials (token + is_chatgpt_mode) instead of just a string key. * fix: Codex auth takes highest priority over secrets store When LLM_USE_CODEX_AUTH=true, Codex credentials are now loaded before checking env vars or the secrets store overlay. Previously the secrets store key (injected during onboarding) would shadow the Codex token. * feat: Responses API provider for ChatGPT backend - New CodexChatGptProvider speaks the Responses API protocol - Auto-detects model from /models endpoint (gpt-4o -> gpt-5.2-codex) - Adds store=false (required by ChatGPT backend) - Error handling with timeout for HTTP 400 responses - Message format translation: Chat Completions -> Responses API - SSE response parsing for text, tool calls, and usage stats - 7 unit tests for message conversion and SSE parsing * fix: SSE parser uses item_id instead of call_id for tool call deltas The Responses API sends function_call_arguments.delta events with item_id (e.g. fc_...) not call_id (e.g. call_...). The parser now keys pending tool calls by item_id from output_item.added and tracks call_id separately for result matching. * fix: strip empty string values from tool call arguments gpt-5.2-codex fills optional tool parameters with empty strings (e.g. timestamp: ""), which IronClaw's tool validation rejects. Strip them before passing to tool execution. * fix: prevent apiKey mode fallback to ChatGPT token When auth_mode is explicitly 'apiKey' but the key is missing/empty, do not fall through to check for a ChatGPT access_token. This prevents returning credentials with is_chatgpt_mode: true and routing to the wrong LLM provider. * refactor: reuse single reqwest::Client across model discovery and LLM calls Create Client once in with_auto_model, pass &Client to fetch_default_model, and move it into the provider struct. Eliminates the redundant Client::new() that wasted a connection pool. * fix: bump client_version to 1.0.0 to unlock gpt-5.3-codex and gpt-5.4 The /models endpoint gates newer models behind client_version. Version 0.1.0 only returns up to gpt-5.2-codex, while 1.0.0+ also returns gpt-5.3-codex and gpt-5.4. * feat: user-configured LLM_MODEL takes priority over auto-detection Fetch the full model list from /models endpoint. If LLM_MODEL is set, validate it against the supported list and warn with available models if not found. If LLM_MODEL is not set, auto-detect the highest-priority model. Also bumps client_version to 1.0.0 to unlock gpt-5.3/5.4. * fix: add 10s timeout to model discovery HTTP request Prevents startup from blocking indefinitely if chatgpt.com is slow or unreachable. Uses reqwest per-request timeout. * docs: add private API warning for ChatGPT backend endpoint The chatgpt.com/backend-api/codex endpoint is private and undocumented. Add warning in module docs and a runtime log on first use to inform users of potential ToS implications. * feat: implement OAuth 401 token refresh for Codex ChatGPT provider On HTTP 401, if a refresh_token is available, the provider now automatically refreshes the access token via auth.openai.com/oauth/token (same protocol as Codex CLI) and retries the request once. Refreshed tokens are persisted back to auth.json. Changes: - codex_auth: read refresh_token, add refresh_access_token() and persist_refreshed_tokens() - codex_chatgpt: RwLock for api_key, 401 detection + retry in send_request, send_http_request helper - config/llm: thread refresh_token/auth_path through RegistryProviderConfig - llm/mod: pass refresh params to with_auto_model * refactor: lazy model detection via OnceCell, remove block_in_place Model is no longer resolved during provider construction. Instead, resolve_model() uses tokio::sync::OnceCell to lazily fetch from /models on the first LLM call. This eliminates the block_in_place + block_on workaround in create_codex_chatgpt_from_registry. - with_auto_model (async) -> with_lazy_model (sync constructor) - resolve_model() added with OnceCell-based lazy init - build_request_body takes model as parameter - model_name() returns resolved or configured_model as fallback * feat: support multimodal content (images) in Codex ChatGPT provider message_to_input_items now checks content_parts for user messages. ContentPart::Text maps to input_text and ContentPart::ImageUrl maps to input_image, matching the Responses API format used by Codex CLI. Falls back to plain text when content_parts is empty. Also updates client_version to 0.111.0 for /models endpoint. Adds test: test_message_conversion_user_with_image * refactor: move codex_auth module from src/ to src/llm/ codex_auth is only used by the LLM layer (codex_chatgpt provider and config/llm). Moving it under src/llm/ reflects its actual scope. - Remove pub mod codex_auth from lib.rs - Add pub mod codex_auth to llm/mod.rs - Update imports: super::codex_auth, crate::llm::codex_auth * Fix codex provider style issues * Use SecretString throughout codex auth refresh flow * Use SecretString for codex access tokens * Reuse provider client for codex token refresh * Stream Codex SSE responses incrementally * Fix Windows clippy and SQLite test linkage * Trigger checks after regression skip label * Tighten codex auth module handling
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 supports many LLM providers out of the box. Built-in providers include Anthropic, OpenAI, Google Gemini, MiniMax, Mistral, and Ollama (local). OpenAI-compatible services like OpenRouter (300+ models), Together AI, Fireworks AI, and self-hosted servers (vLLM, LiteLLM) are also supported.
Select your provider in the wizard, or set environment variables directly:
# Example: MiniMax (built-in, 204K context)
LLM_BACKEND=minimax
MINIMAX_API_KEY=...
# Example: OpenAI-compatible endpoint
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.
