Illia Polosukhin 43d6fc16b0 feat(engine-v2): Phase 4 cost tracking + Phase 6 mission lifecycle acceptance (#2660)
* feat(engine-v2): Phase 4 cost tracking + Phase 6 mission lifecycle acceptance

Closes two gaps blocking v2 engine becoming the default:

**Phase 4 — token + cost accounting**

- Delete orphaned `crates/ironclaw_engine/src/executor/compaction.rs`
  (176 lines). The Python orchestrator (`default.py::compact_if_needed`)
  has owned compaction policy since #1557; the Rust module had no
  callers anywhere in the workspace.
- Wire `cost_usd` in `LlmBridgeAdapter` by calling
  `LlmProvider::calculate_cost()` at both the no-tools and with-tools
  response paths. The engine's `Thread::total_cost_usd` accumulator and
  `max_budget_usd` gates were already plumbed — only the adapter was
  hardcoding 0.0.
- Persist `total_cost_usd` through `ThreadArchiveSummary` round-trip in
  `store_adapter.rs`. Previously, rehydrating an archived thread
  silently dropped the cost to 0.0. `#[serde(default)]` keeps existing
  archive files deserializing cleanly.

**Phase 6 — mission lifecycle acceptance**

Three new integration tests in `bridge/effect_adapter.rs` driving
`execute_action()` end-to-end (per `.claude/rules/testing.md` "Test
Through the Caller"):

- `mission_full_lifecycle_via_execute_action` — create → list → complete
  → list, asserting the `Completed` status surfaces through
  `mission_list` after `mission_complete`.
- `mission_fire_returns_thread_id_for_manual_cadence_via_execute_action`
  — fresh manual mission fires successfully and returns a UUID thread_id
  rather than `not_fired`.
- `mission_list_returns_all_user_missions_via_execute_action` — all
  three created missions appear in `mission_list` output.

**Regression tests for cost wiring**

Three new tests in `bridge/llm_adapter.rs`:

- `complete_no_tools_populates_cost_usd_through_adapter`
- `complete_with_tools_populates_cost_usd_through_adapter`
- `complete_routes_subcalls_through_cheap_provider_for_cost` — pins that
  `depth > 0` is priced with the cheap provider, not the primary.

Coordinated with in-flight work: skipped paths owned by #2504 (auth
E2E), #2631 (paused-lease resume), #2570 (mission re-fire), #2549
(mission_get), #2452 (tool_calls persistence), #2621 (replay snapshot).

Verified: `cargo fmt`, `cargo clippy --all --benches --tests --examples
--all-features` (0 warnings), `cargo test -p ironclaw_engine` (409
passed), `cargo test -p ironclaw --lib` (5079 passed).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* feat(engine-v2): surface engine capability actions to LLM via available_actions

Fixes the gap called out in the PR body: `EffectBridgeAdapter::available_actions`
was only enumerating v1 `ToolRegistry` tools + latent OAuth actions, so
engine-native capabilities like `missions` never appeared in the LLM's
tools list even when a thread held an active lease for them. The LLM
was therefore unable to call `mission_create` / `mission_list` / etc.
via structured tool calls; the only ways to drive missions were CodeAct
Python calls (which relied on the same `known_actions` set and hit the
same gap) or `/routine` slash commands falling through to v1.

Wire `CapabilityRegistry` into the adapter and iterate active leases to
surface every leased, engine-registered capability action. Respects
lease grant scope — a lease granting only `mission_list` does not leak
`mission_create`. Skips the `"tools"` capability since that lease is
already reconciled from the v1 path.

Router wires the shared `Arc<CapabilityRegistry>` to both the adapter
and `ThreadManager` at setup.

Three new regression tests:
- `available_actions_surfaces_leased_mission_capability`
- `available_actions_respects_partial_lease_grant`
- `available_actions_omits_capability_without_lease`

Verified: `cargo fmt`, `cargo clippy --all --benches --tests --examples
--all-features` (0 warnings), `cargo test -p ironclaw_engine` (409
passed), `cargo test -p ironclaw --lib` (5082 passed).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* test(engine-v2): close review gaps — archive round-trip, v1/engine merge, defensive filters

Addresses gaps raised in PR #2660 review:

- **Consolidate `use ironclaw_engine::{...}`** into a single grouped
  import in `effect_adapter.rs` (was split across two statements).

- **Apply `is_v1_only_tool` / `is_v1_auth_tool` filters** to the engine
  capability path in `available_actions`. Defensive guardrail: a future
  engine capability that registers an action under a v1-denylisted name
  (`create_job`, `tool_auth`, ...) must not bypass the v2-isolation
  filters by virtue of coming through a different capability registry.

- **`ThreadArchiveSummary` serialization round-trip tests** in
  `store_adapter.rs`:
    - `archive_summary_preserves_total_cost_usd_through_round_trip` —
      pins the regression the PR fixed (cost silently zeroed on
      rehydration).
    - `archive_summary_handles_legacy_json_without_total_cost_usd_field`
      — pins `#[serde(default)]` back-compat for archive files written
      before this PR.

- **`available_actions` combined advertising tests** in
  `effect_adapter.rs`:
    - `available_actions_merges_v1_tools_with_engine_capabilities` —
      v1 tool + mission capability both surface on one call.
    - `available_actions_filters_v1_denylisted_names_from_engine_capabilities`
      — pins the new defensive filter.

- **`cost_usd_from` subscription-billed-provider test** in
  `llm_adapter.rs`:
    - `complete_with_subscription_billed_provider_yields_zero_cost` —
      zero `cost_per_token` round-trips to exactly `0.0`, no NaN/Inf.

Verified: `cargo fmt`, `cargo clippy --all --benches --tests --examples
--all-features` (0 warnings), `cargo test -p ironclaw_engine` (409
passed), `cargo test -p ironclaw --lib` (5087 passed, +5 new).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* fix(engine-v2): price cache tokens correctly in LlmBridgeAdapter

Addresses PR #2660 review (gemini-code-assist + Copilot, L23/L115/L189):
`cost_usd_from` only priced `input_tokens + output_tokens`, ignoring
`cache_read_input_tokens` and `cache_creation_input_tokens`. For
providers with prompt caching (Anthropic, OpenAI), this undercounted
input cost and silently neutered the `max_budget_usd` gate.

Extend the helper to mirror the canonical formula in
`src/agent/cost_guard.rs::CostGuard::record_llm_call`:

  uncached_input = input_tokens - (cache_read + cache_write)
  cache_read_cost  = input_rate * cache_read  / cache_read_discount()
  cache_write_cost = input_rate * cache_write * cache_write_multiplier()
  cost = input_rate * uncached_input
       + cache_read_cost
       + cache_write_cost
       + output_rate * output_tokens

All `LlmProvider` implementations already supply `cache_read_discount()`
(default 1, Anthropic 10, OpenAI 2) and `cache_write_multiplier()`
(default 1, Anthropic 1.25 for 5m / 2.0 for 1h) through the decorator
chain, so no trait surgery is required.

Regression test: `complete_prices_cache_tokens_with_discount_and_multiplier`
uses Anthropic Sonnet 5m-TTL rates, exercises a 10k-input / 2k-read /
1k-write / 500-output response, and pins the correct total ($0.03285)
against the old naive $0.0375 that would have undercounted ~14%.

Verified: `cargo fmt`, `cargo clippy --all --benches --tests --examples
--all-features` (0 warnings), `cargo test -p ironclaw_engine` (435
passed), `cargo test -p ironclaw --lib` (5136 passed).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-19 21:25:12 +09:00

IronClaw

IronClaw

Your secure personal AI assistant, always on your side

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

English | 简体中文 | Русский | 日本語 | 한국어

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, GitHub Copilot, 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/capabilities/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
  • Channels: See docs/channels/overview.mdx for setup of Telegram, Discord, and other channels.
  • 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.

Description
IronClaw is OpenClaw inspired implementation in Rust focused on privacy and security IronClaw 基于一个简单的原则:你的 AI 助手应该为你服务,而不是与你为敌。
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