e81fb7e5cb refactor(setup): extract init logic from wizard into owning modules (#1210)
* refactor(setup): extract init logic from wizard into owning modules

Move database, LLM model discovery, and secrets initialization logic
out of the setup wizard and into their owning modules, following the
CLAUDE.md principle that module-specific initialization must live in
the owning module as a public factory function.

Database (src/db/mod.rs, src/config/database.rs):
- Add DatabaseConfig::from_postgres_url() and from_libsql_path()
- Add connect_without_migrations() for connectivity testing
- Add validate_postgres() returning structured PgDiagnostic results

LLM (src/llm/models.rs — new file):
- Extract 8 model-fetching functions from wizard.rs (~380 lines)
- fetch_anthropic_models, fetch_openai_models, fetch_ollama_models,
  fetch_openai_compatible_models, build_nearai_model_fetch_config,
  and OpenAI sorting/filtering helpers

Secrets (src/secrets/mod.rs):
- Add resolve_master_key() unifying env var + keychain resolution
- Add crypto_from_hex() convenience wrapper

Wizard restructuring (src/setup/wizard.rs):
- Replace cfg-gated db_pool/db_backend fields with generic
  db: Option<Arc<dyn Database>> + db_handles: Option<DatabaseHandles>
- Delete 6 backend-specific methods (reconnect_postgres/libsql,
  test_database_connection_postgres/libsql, run_migrations_postgres/
  libsql, create_postgres/libsql_secrets_store)
- Simplify persist_settings, try_load_existing_settings,
  persist_session_to_db, init_secrets_context to backend-agnostic
  implementations using the new module factories
- Eliminate all references to deadpool_postgres, PoolConfig,
  LibSqlBackend, Store::from_pool, refinery::embed_migrations

Net: -878 lines from wizard, +395 lines in owning modules, +378 new.

Co-Authored-By: Claude Opus 4.6 (1M context) <[email protected]>

* test(settings): add wizard re-run regression tests

Add 10 tests covering settings preservation during wizard re-runs:
- provider_only rerun preserves channels/embeddings/heartbeat
- channels_only rerun preserves provider/model/embeddings
- quick mode rerun preserves prior channels and heartbeat
- full rerun same provider preserves model through merge
- full rerun different provider clears model through merge
- incremental persist doesn't clobber prior steps
- switching DB backend allows fresh connection settings
- merge preserves true booleans when overlay has default false
- embeddings survive rerun that skips step 5

These cover the scenarios where re-running the wizard would
previously risk resetting models, providers, or channel settings.

Co-Authored-By: Claude Opus 4.6 (1M context) <[email protected]>

* refactor(setup): eliminate cfg(feature) gates from wizard methods

Replace compile-time #[cfg(feature)] dispatch in the wizard with
runtime dispatch via DatabaseBackend enum and cfg!() macro constants.

- Merge step_database_postgres + step_database_libsql into step_database
  using runtime backend selection
- Rewrite auto_setup_database without feature gates
- Remove cfg(feature = "postgres") from mask_password_in_url (pure fn)
- Remove cfg(feature = "postgres") from test_mask_password_in_url

Only one internal #[cfg(feature = "postgres")] remains: guarding the
call to db::validate_postgres() which is itself feature-gated.

Co-Authored-By: Claude Opus 4.6 (1M context) <[email protected]>

* refactor(db): fold PG validation into connect_without_migrations

Move PostgreSQL prerequisite validation (version >= 15, pgvector)
from the wizard into connect_without_migrations() in the db module.
The validation now returns DatabaseError directly with user-facing
messages, eliminating the PgDiagnostic enum and the last
#[cfg(feature)] gate from the wizard.

The wizard's test_database_connection() is now a 5-line method that
calls the db module factory and stores the result.

Co-Authored-By: Claude Opus 4.6 (1M context) <[email protected]>

* fix: address PR review comments [skip-regression-check]

- Use .as_ref().map() to avoid partial move of db_config.libsql_path
  (gemini-code-assist)
- Default to available backend when DATABASE_BACKEND is invalid, not
  unconditionally to Postgres which may not be compiled (Copilot)
- Match DatabaseBackend::Postgres explicitly instead of _ => wildcard
  in connect_with_handles, connect_without_migrations, and
  create_secrets_store to avoid silently routing LibSql configs through
  the Postgres path when libsql feature is disabled (Copilot)
- Upgrade Ollama connection failure log from info to warn with the
  base URL for better visibility in wizard UX (Copilot)
- Clarify crypto_from_hex doc: SecretsCrypto validates key length,
  not hex encoding (Copilot)

Co-Authored-By: Claude Opus 4.6 (1M context) <[email protected]>

* fix: address zmanian's PR review feedback [skip-regression-check]

- Update src/setup/README.md to reflect Arc<dyn Database> flow
- Remove stale "Test PostgreSQL connection" doc comment
- Replace unwrap_or(0) in validate_postgres with descriptive error
- Add NearAiConfig::for_model_discovery() constructor
- Narrow pub to pub(crate) for internal model helpers

Co-Authored-By: Claude Opus 4.6 (1M context) <[email protected]>

* fix: address Copilot review comments (quick-mode postgres gate, empty env vars) [skip-regression-check]

- Gate DATABASE_URL auto-detection on POSTGRES_AVAILABLE in quick mode
  so libsql-only builds don't attempt a postgres connection
- Match empty-env-var filtering in key source detection to align with
  resolve_master_key() behavior
- Filter empty strings to None in DatabaseConfig::from_libsql_path()
  for turso_url/turso_token

Co-Authored-By: Claude Opus 4.6 (1M context) <[email protected]>

---------

Co-authored-by: Claude Opus 4.6 (1M context) <[email protected]>
2026-03-16 04:58:17 +00:00
2026-02-04 22:09:52 -08:00
2026-02-11 08:31:25 +00:00
2026-02-09 03:00:35 +00:00
2026-02-22 19:08:43 +00:00
2026-02-22 19:08:43 +00:00
2026-02-21 15:14:57 -07:00

IronClaw

IronClaw

Your secure personal AI assistant, always on your side

License: MIT OR Apache-2.0 Telegram: @ironclawAI Reddit: r/ironclawAI

English | 简体中文 | Русский

PhilosophyFeaturesInstallationConfigurationSecurityArchitecture


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.sh before cargo build so 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:

at your option.

S
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IronClaw is OpenClaw inspired implementation in Rust focused on privacy and security
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