* feat(setup): add Anthropic OAuth and Codex OAuth onboarding flows Add OAuth token authentication as an alternative to API keys during onboarding for both Anthropic (via `claude login`) and OpenAI/Codex (via `~/.codex/auth.json`). Key changes: - New `AnthropicOAuthProvider` using `Authorization: Bearer` header (rig-core hardcodes `x-api-key` which rejects OAuth tokens) - Wizard auth method selector: "Direct API Key" vs "OAuth Token" for both Anthropic and OpenAI providers - Codex token extraction from `$CODEX_HOME/auth.json` / `~/.codex/auth.json` - Claude Code sandbox sub-step in Docker setup (checks for credentials) - Secret injection mappings for `ANTHROPIC_OAUTH_TOKEN` and `CODEX_OAUTH_TOKEN` - `CODEX_OAUTH_TOKEN` falls back to `OPENAI_API_KEY` (same Bearer auth) Supersedes #143 which had a broken auth flow (OAuth token sent as x-api-key → 401). Credit to @bigguybobby for the original approach. Co-Authored-By: Claude Opus 4.6 <[email protected]> * fix: persist OAuth tokens in bootstrap .env and re-extract at startup OAuth tokens stored only in the secrets DB were invisible to Config::from_env() which runs before the DB connects (chicken-and-egg). Two fixes: 1. write_bootstrap_env() now persists ANTHROPIC_OAUTH_TOKEN and CODEX_OAUTH_TOKEN to ~/.ironclaw/.env (same pattern as NEARAI_API_KEY) 2. main.rs re-extracts a fresh token from the OS credential store (macOS Keychain / ~/.claude/.credentials.json) before config resolution, handling token expiry (8-12h) gracefully Co-Authored-By: Claude Opus 4.6 <[email protected]> * fix: persist all LLM credentials in bootstrap .env, not just NEAR AI All providers had the same chicken-and-egg issue: API keys stored in the secrets DB were invisible to Config::from_env() which runs before DB connects. Only NEARAI_API_KEY was written to bootstrap .env. Now write_bootstrap_env() persists all credential env vars: NEARAI_API_KEY, ANTHROPIC_API_KEY, ANTHROPIC_OAUTH_TOKEN, OPENAI_API_KEY, CODEX_OAUTH_TOKEN, LLM_API_KEY, TINFOIL_API_KEY. Also: setup_api_key_provider() now sets the env var during the wizard session so write_bootstrap_env() can pick it up. Co-Authored-By: Claude Opus 4.6 <[email protected]> * fix: address security review findings for OAuth onboarding - Extract "oauth-placeholder" to named OAUTH_PLACEHOLDER constant shared across config and wizard to prevent silent drift - Document plaintext credential tradeoff in write_bootstrap_env (API keys stored with 0o600 permissions, recommend full-disk encryption) - Add blocking "Press Enter" wait in Anthropic OAuth retry flow so user has time to run `claude login` in another terminal - Add escape hatch from manual OAuth paste back to API key flow (empty input switches to setup_api_key_provider) - Fix Retry-After header: parse u64 seconds into Duration before passing to LlmError::RateLimited - Make config::llm module pub(crate) for constant visibility - Use .bearer_auth() instead of manual format!("Bearer {}") - Remove response body from debug log (may contain PII) - Update Anthropic API version to 2024-10-22 Co-Authored-By: Claude Opus 4.6 <[email protected]> * security: remove plaintext credentials from bootstrap .env Credentials (API keys, OAuth tokens) were being written in plaintext to ~/.ironclaw/.env to work around a chicken-and-egg problem: Config::from_env() runs before the encrypted secrets DB is connected. Instead of storing secrets on disk, LlmConfig::resolve() now defers gracefully when credentials are missing — it returns None for the provider config instead of hard-erroring with MissingRequired. After the DB connects, AppBuilder::build_all() loads secrets from encrypted storage via inject_llm_keys_from_secrets() and re-resolves the config. For Anthropic OAuth tokens (which expire in 8-12h), the secret injection step also tries the OS credential store (macOS Keychain / Linux credentials.json) for a fresh token, overriding the potentially stale copy in the DB. Changes: - LlmConfig::resolve(): OpenAI, Anthropic, OpenAI-compatible, and Tinfoil all return None instead of MissingRequired when credentials are absent - write_bootstrap_env(): no longer writes any credential env vars - inject_llm_keys_from_secrets(): refreshes Anthropic OAuth from OS credential store before overlay is finalized - main.rs: removed OAuth re-extraction hack (no longer needed) Co-Authored-By: Claude Opus 4.6 <[email protected]> * fix: load OS credential store tokens even without secrets DB The OAuth token extraction from macOS Keychain / Linux credentials files was only running inside inject_llm_keys_from_secrets(), which requires the encrypted secrets DB. When no master key is configured, init_secrets() returned early — skipping both DB secret loading AND OS credential store extraction, leaving the Anthropic OAuth token unavailable. Split into two paths: - inject_llm_keys_from_secrets(): loads from encrypted DB + OS stores - inject_os_credentials(): loads from OS stores only (no DB needed) init_secrets() now calls inject_os_credentials() and re-resolves config even in the no-master-key early-return path, so `claude login` tokens are always available regardless of secrets DB state. Co-Authored-By: Claude Opus 4.6 <[email protected]> * fix: add anthropic-beta header required for OAuth authentication Anthropic's api.anthropic.com requires the `anthropic-beta: oauth-2025-04-20` header to accept OAuth Bearer tokens. Without it, the API returns 401 "OAuth authentication is currently not supported." Also reverts API version to 2023-06-01 since the OAuth beta flag does not support the 2024-10-22 version (returns 400 "not a valid version"). This was the same bug that caused PR #143's 401 errors — the beta header was missing entirely. Co-Authored-By: Claude Opus 4.6 <[email protected]> * fix: Anthropic and OpenAI model resolution respects selected_model The Anthropic and OpenAI config resolution ignored settings.selected_model entirely, only checking the provider-specific env var (ANTHROPIC_MODEL, OPENAI_MODEL) and falling back to a hardcoded default. This meant the model chosen during onboarding wizard was silently overridden. Now follows the same pattern as NearAI and OpenAI-compatible: env var > settings.selected_model > hardcoded default. Also deduplicated the Anthropic config construction (two identical branches for API key vs OAuth now share model/base_url resolution). Co-Authored-By: Claude Opus 4.6 <[email protected]> * test: add provider resolution tests for all LLM backends Covers deferred resolution (no credentials → None instead of error), credential presence, model selection fallback chain, and OAuth token routing for Anthropic, OpenAI, Tinfoil, Ollama, and NearAI. Co-Authored-By: Claude Opus 4.6 <[email protected]> * fix: handle nested tokens.access_token format in Codex auth.json Codex CLI stores OAuth tokens in a nested format under tokens.access_token (ChatGPT OAuth flow), not at the top level. Also adds ENV_MUTEX to Codex token tests for thread safety. Co-Authored-By: Claude Opus 4.6 <[email protected]> * refactor: remove Codex OAuth onboarding (incompatible with OpenAI API) Codex CLI OAuth tokens use a different endpoint (chatgpt.com/backend-api/codex) and the Responses API wire format, not api.openai.com with Chat Completions. The tokens lack the model.request scope needed for the platform API, so they can't be used as drop-in OPENAI_API_KEY replacements. Removes: extract_codex_oauth_token(), wizard Codex OAuth flow, CODEX_OAUTH_TOKEN env var support, and related tests. OpenAI onboarding now uses direct API key only. Co-Authored-By: Claude Opus 4.6 <[email protected]> * style: fix formatting for CI (cargo fmt) Co-Authored-By: Claude Opus 4.6 <[email protected]> * fix: address Gemini review feedback - Use ? operator for ANTHROPIC_MODEL/BASE_URL env resolution instead of .ok().flatten() to propagate ConfigErrors consistently - Skip Tool messages without tool_call_id with a warning instead of using unwrap_or_default() which would send empty string to Anthropic - Extract credential check into closure to reduce duplication in Claude Code sandbox setup Co-Authored-By: Claude Opus 4.6 <[email protected]> * refactor(review): address PR review feedback for OAuth onboarding - Gate ANTHROPIC_OAUTH_TOKEN resolution to Anthropic provider only (was needlessly checked for all registry providers) - Add 3 regression tests for OAuth config resolution: - oauth_token sets placeholder api_key - real api_key takes priority over oauth - non-Anthropic providers don't pick up oauth_token - Validate OAuth token prefix (sk-ant-oat) in wizard to catch accidentally pasted API keys - Improve error body read handling in AnthropicOAuthProvider (was silently swallowing read errors with unwrap_or_default) - Remove extra blank line in write_bootstrap_env - Remove stale blank line in RegistryProviderConfig doc comment [skip-regression-check] Co-Authored-By: Claude Opus 4.6 <[email protected]> * fix: address PR #384 review comments Blocker: - Replace OnceLock<HashMap> with LazyLock<Mutex<HashMap>> for INJECTED_VARS so both inject_os_credentials() and inject_llm_keys_from_secrets() merge data instead of the second caller silently dropping its entries. High: - Add 401 retry with OS credential store re-extraction in AnthropicOAuthProvider, recovering from expired OAuth tokens (~8-12h) without manual intervention. - Fix comment in app.rs: ~/.codex/auth.json → ~/.claude/.credentials.json. Medium: - Remove unsafe { std::env::set_var } from wizard; use thread-safe inject_single_var() overlay instead (safe on multi-threaded Tokio). - Add post-init validation in AppBuilder: fail early with clear error when LLM_BACKEND is set but no credentials were resolved after secret injection. - Add sk-ant-oat prefix validation in parse_oauth_access_token(). - Only route to AnthropicOAuthProvider when api_key is missing or equals OAUTH_PLACEHOLDER (API key takes priority over OAuth token). - Teach fetch_anthropic_models() to use Bearer auth when only OAuth token is available (model listing no longer fails for OAuth-only users). Low: - Use optional_env() in wizard credential checks to read from injected overlay, not just raw env vars. [skip-regression-check] Co-Authored-By: Claude Opus 4.6 <[email protected]> * style: cargo fmt Co-Authored-By: Claude Opus 4.6 <[email protected]> --------- Co-authored-by: Claude Opus 4.6 <[email protected]> Co-authored-by: [email protected] <[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.
