5c2ba44f12 feat(llm): declarative provider registry (#618)
* feat(llm): declarative provider registry, replace hardcoded provider configs

Replace the hardcoded LlmBackend enum and per-provider config structs with
a declarative JSON registry. Adding a new OpenAI-compatible provider now
requires zero Rust code changes -- just add an entry to providers.json.

- Add providers.json with 14 providers (openai, anthropic, ollama,
  openai_compatible, tinfoil, openrouter, groq, nvidia, venice, together,
  fireworks, deepseek, cerebras, sambanova)
- Add src/llm/registry.rs with ProviderProtocol, SetupHint,
  ProviderDefinition, and ProviderRegistry types
- Rewrite src/config/llm.rs: remove LlmBackend enum and 5 per-provider
  config structs, replace with generic RegistryProviderConfig
- Simplify src/llm/mod.rs: remove 5 create_*_provider functions, dispatch
  on ProviderProtocol (3 code paths for all providers)
- Dynamic setup wizard: menu built from registry.selectable(), generic
  credential collection dispatched by SetupHint kind
- Dynamic secret injection: inject_llm_keys_from_secrets() discovers
  secret-to-env mappings from registry instead of hardcoded list
- Users can extend with ~/.ironclaw/providers.json (no recompile)
- Subsumes open provider PRs: Groq #570, NVIDIA NIM #576, Venice.ai #451
  (Gemini #476 excluded -- not OpenAI-compatible)

[skip-regression-check]

Co-Authored-By: Claude Opus 4.6 <[email protected]>

* feat(llm): self-sufficient provider auth, onboard --provider-only, extract SessionConfig

- NearAiChatProvider handles its own session auth lazily in
  resolve_bearer_token() instead of requiring main.rs to pre-check.
  Triggers OAuth/API-key login on first request when no token exists.

- Add `ironclaw onboard --provider-only` to reconfigure just the LLM
  provider and model selection without re-running the full wizard.

- Extract auth_base_url and session_path from NearAiConfig into
  LlmConfig::session (SessionConfig). Callers now use
  config.llm.session directly instead of reaching into nearai fields.

[skip-regression-check]

Co-Authored-By: Claude Opus 4.6 <[email protected]>

* fix(llm): address PR review comments on provider registry

- Use registry.selectable() instead of registry.all() for secret
  injection to avoid duplicates from user provider overrides.

- Fix selectable() dedup bug: check setup hint on the final (overridden)
  definition, not the first occurrence. User overrides that add a setup
  hint are now included correctly.

- Only store openai_compatible_base_url for providers that actually use
  LLM_BASE_URL, preventing base URL pollution for groq/nvidia/etc.

- Normalize provider_id to canonical registry def.id instead of using
  the raw user-supplied alias string.

- Add comment explaining why .completions_api() is used over the
  default Responses API path.

[skip-regression-check]

Co-Authored-By: Claude Opus 4.6 <[email protected]>

* fix(docker): copy providers.json into build context

The declarative provider registry uses `include_str!("../../providers.json")`
at compile time, so the file must be present in the Docker builder stage.

Co-Authored-By: Claude Opus 4.6 <[email protected]>

* fix(llm): address second-round PR review comments (#618)

- Make --channels-only and --provider-only mutually exclusive via clap
  conflicts_with (Copilot: cli/mod.rs)
- Add 5s timeout to fetch_openai_compatible_models(), matching the other
  three model-fetch helpers (Copilot: wizard.rs)
- Apply models_filter from setup hints when listing models, so Groq's
  "chat" filter actually excludes non-chat models (Copilot: wizard.rs)
- Normalize LlmConfig.backend to the canonical provider ID instead of
  the raw user-supplied alias string (Copilot: llm.rs)
- Add models_filter() accessor to SetupHint with regression test

Co-Authored-By: Claude Opus 4.6 <[email protected]>

* fix(test): relax flaky parallel speedup timing threshold

The test_parallel_speedup test asserted <500ms but CI runners can be
slow enough to exceed that while still proving parallelism. Bumped to
800ms which still validates parallel execution (sequential would be
~600ms minimum) while tolerating CI jitter.

[skip-regression-check]

Co-Authored-By: Claude Opus 4.6 <[email protected]>

* fix(llm): handle api_key_login path in resolve_bearer_token, warn on missing keys

- resolve_bearer_token() now checks NEARAI_API_KEY env var after
  ensure_authenticated(), handling the case where the user entered an
  API key via the interactive login flow (which sets the env var but
  not a session token)
- Add tracing::warn when creating an OpenAI-compatible provider without
  an API key, making 401 errors easier to diagnose
- Add regression test for resolve_bearer_token auth paths

Co-Authored-By: Claude Opus 4.6 <[email protected]>

* style: fix formatting in nearai_chat test

[skip-regression-check]

Co-Authored-By: Claude Opus 4.6 <[email protected]>

* fix(llm): correct bearer token priority, handle setup-less providers (#618)

- resolve_bearer_token(): session token now takes priority over
  NEARAI_API_KEY env var, preventing unexpected auth mode switches.
  The env var fallback only triggers after ensure_authenticated() when
  no session token was stored (api_key_login path).
- run_provider_setup(): providers with setup: None no longer error,
  allowing env-var-only providers to be kept during re-onboarding.
- Split bearer token test into 3 focused tests: config api_key path,
  session token path, and session-beats-env-var precedence test.
- Add test for wizard handling of providers without setup hints.

Co-Authored-By: Claude Opus 4.6 <[email protected]>

* test(llm): comprehensive tests for provider registry, config, and auth

Add 13 new tests covering the critical paths in the provider system:

Bearer token auth priority (nearai_chat.rs):
- config api_key wins over session token and env var
- session token wins over env var (prevents mid-run auth mode switches)
- config api_key path works in isolation
- session token path works in isolation

Config resolution (config/llm.rs):
- backend alias normalization (open_ai → openai)
- unknown backend falls back to openai_compatible
- nearai aliases (nearai, near_ai, near) all resolve correctly
- base URL resolution priority (env > settings > registry default)

Registry dedup (registry.rs):
- user override adds setup hint → appears in selectable()
- user override removes setup hint → excluded from selectable()
- selectable() preserves insertion order during dedup
- all built-in ApiKey providers have api_key_env set

Wizard (wizard.rs):
- setup: None providers don't error during re-onboarding

Co-Authored-By: Claude Opus 4.6 <[email protected]>

---------

Co-authored-by: Claude Opus 4.6 <[email protected]>
2026-03-07 02:18:57 +00:00
2026-03-05 17:12:49 -08:00
2026-02-04 22:09:52 -08:00
2026-02-11 08:31:25 +00:00
2026-02-09 03:00:35 +00:00
2026-03-06 11:23:28 -08:00
2026-03-06 11:23:28 -08:00
2026-03-06 11:23:28 -08:00
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

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 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.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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