424a0366a9 feat: enable Anthropic prompt caching via automatic cache_control injection (#660)
* feat(llm): add Anthropic prompt caching and cache token tracking

- Inject cache_control via additional_params for Claude models in rig_adapter
- Add cache_read_input_tokens and cache_creation_input_tokens to
  CompletionResponse and ToolCompletionResponse
- Extract cached_input_tokens from rig-core unified Usage
- Add is_anthropic_model() detection helper with provider prefix support
- Log prompt cache hits at debug level (consistent with response_cache)
- Add 7 unit tests for cache injection and model detection
- Update all mock providers and test fixtures with new fields

* feat(cost): apply 90% cache discount to prompt-cached tokens in CostGuard

- Add cache_read_input_tokens to TokenUsage so cache counts flow from
  CompletionResponse through the reasoning layer to the dispatcher
- Update CostGuard::record_llm_call() to accept cache_read_input_tokens:
  cached tokens are billed at 10% of the normal input rate
- Thread cache_read_input_tokens from dispatcher into CostGuard
- Add test_cache_discount_reduces_cost verifying exact savings match
  90% of input cost for fully-cached requests
- Update all existing test callers with zero-cache parameter

* refactor(cache): scope cache_control to Anthropic backend and validate model support

- Replace model-name-based is_anthropic_model() with explicit
  enable_prompt_cache flag on RigAdapter, set only for the direct
  Anthropic backend via with_prompt_cache(true)
- Add supports_prompt_cache() to validate model names per Anthropic
  docs: only Claude 3+ models support caching; claude-2 and
  claude-instant are excluded to prevent 400 errors
- Warn when caching is enabled but model does not support it
- Replace is_anthropic_model tests with flag-based and model
  validation tests

* fix(cache): validate model at construction and propagate cache metrics through proxy

- Move supports_prompt_cache() check into with_prompt_cache() so
  unsupported models are detected once at construction, not per request
- Add cache_read_input_tokens and cache_creation_input_tokens to
  ProxyCompletionResponse and ProxyToolCompletionResponse with
  serde(default) for backward compatibility
- Pass cache metrics through orchestrator proxy instead of zeroing
- Use claude-opus-4-6 in cache discount test to match Anthropic
  semantics

* feat(llm): add configurable cache retention with write surcharge

- Add CacheRetention enum (none/short/long) to AnthropicDirectConfig
- Parse ANTHROPIC_CACHE_RETENTION env var (default: short)
- Inject TTL-aware cache_control (short=5m ephemeral, long=1h)
- Extract cache_creation_input_tokens from raw Anthropic response
- Add cache_write_multiplier() to LlmProvider trait (1.25x short, 2.0x long)
- Pipe dynamic write multiplier through dispatcher to CostGuard
- Add TokenUsage.cache_creation_input_tokens field
- Add tests for Long TTL injection, 5m and 1h write surcharges
- Document ANTHROPIC_CACHE_RETENTION in .env.example

* docs: fix stale cache_retention field comment

* fix: resolve CI failures after upstream merge

- Add missing cost_per_token arg to cache test callsites
- Apply cargo fmt to long lines in tests and tracing macros

* fix: address Copilot review feedback

- Use saturating_add for cache token sum to prevent u32 overflow
- Tighten supports_prompt_cache to explicitly match claude-3+/claude-4+
  and named families (claude-sonnet/claude-opus/claude-haiku)

* fix: adapt prompt caching to registry architecture and add missing cache fields

- Resolve merge conflicts: adapt CacheRetention and cache injection to
  the declarative provider registry (RegistryProviderConfig replaces
  AnthropicDirectConfig)
- Parse ANTHROPIC_CACHE_RETENTION env var in create_anthropic_from_registry()
- Use Anthropic automatic caching via top-level cache_control in
  additional_params (rig-core #[serde(flatten)] places it at request root)
- Add cache_read/creation_input_tokens fields to all mock LlmProviders
  added on main after PR #291 branched (response_cache, dispatcher,
  provider_chaos, trace_llm)
- Suppress clippy::too_many_arguments on record_llm_call and
  build_rig_request
- Add regression tests for cache injection (short/long/none) and
  cache_write_multiplier values

Co-Authored-By: Canvinus <[email protected]>

* fix: delegate cache_write_multiplier through provider wrappers and make cache_read_discount configurable

The 6 decorator providers (Retry, CircuitBreaker, Failover, SmartRouting,
CachedProvider, RecordingLlm) did not delegate cache_write_multiplier()
to their inner provider, causing it to always return 1.0 instead of the
actual 1.25x/2.0x from RigAdapter. This fix adds delegation for both
cache_write_multiplier() and the new cache_read_discount() method.

Also makes the cache read discount per-provider instead of hardcoding
Anthropic's 90% discount (÷10). OpenAI uses 50% (÷2), so the discount
is now returned by each provider via the LlmProvider trait.

Addresses review feedback on PR #660.

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

* style: cargo fmt

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

* test: add CacheRetention FromStr/Display unit tests

Tests cover primary values, aliases (off/disabled/5m/ephemeral/1h),
case-insensitivity, invalid input error, and Display round-trip.

Addresses Copilot review feedback on PR #660.

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

---------

Co-authored-by: Andrey <[email protected]>
Co-authored-by: Andrey Gruzdev <[email protected]>
Co-authored-by: Claude Opus 4.6 <[email protected]>
2026-03-07 09:10:05 +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
Description
IronClaw is OpenClaw inspired implementation in Rust focused on privacy and security
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