firat.sertgoz 862ac13a87 feat(memory): configurable insights interval, session summary hook, reasoning-augmented recall (#2336)
* feat(memory): configurable insights interval, session summary hook, reasoning-augmented recall

Three memory enrichment features:

1. Configurable conversation insights interval via MISSION_INSIGHTS_INTERVAL
   env var (default: 5, min: 1) with MissionsConfig + MissionSettings wiring
2. SessionSummaryHook that writes LLM-generated conversation summaries to
   workspace daily logs on session end (fail-open, 30s timeout)
3. Optional reasoning parameter on memory_search that synthesizes raw chunks
   via cheap LLM before returning, controlled by SEARCH_REASONING_ENABLED

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix(memory): address PR #2336 review feedback and CI failures

Critical fixes:
- Use DB-first config system for MissionsConfig instead of raw
  std::env::var in router.rs (issue #1)
- SessionSummaryHook now uses thread_ids from HookEvent::SessionEnd
  to summarize the correct conversation instead of guessing via
  recency; falls back to most-recent for backward compatibility (#2)
- Add per-user rate limiter (10/min, 60/hr) and 15s timeout on
  reasoning LLM calls in MemorySearchTool to prevent unbounded
  usage (#3)

Test coverage:
- Caller-level tests for reasoning-augmented recall (LLM wiring,
  disabled config, and failure fallback paths) (#4)
- SessionSummaryHook LLM failure path test confirming fail-open
  behavior (#5)
- reasoning_enabled config field tests (default, env, DB override) (#6)
- MissionSettings and SearchSettings round-trip assertions in
  comprehensive_db_map_round_trip (#11)

Convention fixes:
- Remove double env-var parsing in MissionsConfig::resolve (#7)
- Use ChatMessage::system()/user() constructors in
  SessionSummaryHook (#8)
- Add TODO comments for inline prompt strings (#9)
- Add timeout on reasoning LLM call (#10)

CI fixes:
- Remove 4 stale wasmtime advisory entries from deny.toml
- Add RUSTSEC-2026-0097 (rand 0.8.5) to advisory ignore list

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(memory): address henrypark133 + ilblackdragon review — safety, concurrency, prompts (#2336)

- Move inline prompt templates to prompts/*.md per project convention
  (session_summary.md, memory_reasoning_synthesis.md) — resolves TODOs
- Add Arc<Semaphore> to SessionSummaryHook to cap concurrent LLM calls
  on mass session expiry (follows OutboundWebhookHook pattern)
- Sanitize LLM-generated summaries via ironclaw_safety::Sanitizer before
  writing to workspace (mitigates stored prompt injection vector)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(memory): CI compile fix + reasoning sanitizer parity + harden test

- Add live_state / live_state_started_at fields to ConversationSummary
  literals in three session-summary test sites; staging added these
  fields after the branch was created and clippy/test builds were
  failing on missing-field errors.
- Replace silent unwrap_or_default on MissionsConfig::resolve in
  bridge::router::init_engine with an explicit warn-and-default match,
  so a misconfigured MISSION_INSIGHTS_INTERVAL surfaces in logs instead
  of being absorbed into the default.
- Run the reasoning-synthesis output through ironclaw_safety::Sanitizer
  before persisting it to the tool result, matching the parity already
  applied in SessionSummaryHook. Memory chunks fed into synthesis can
  carry attacker-controlled text and the synthesis flows back into
  future LLM contexts via memory_search results.
- Strengthen reasoning_enabled_fires_llm_and_returns_synthesis: add a
  preflight assertion that FTS returns the seeded doc, then
  unconditionally assert the LLM was called once and that synthesis
  matches the mocked response. Removes the prior `if llm.calls() > 0`
  guard that made the synthesis assertions vacuous when search returned
  empty.

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

---------

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: Illia Polosukhin <ilblackdragon@gmail.com>
2026-04-20 15:52:15 +09:00
2026-02-04 22:09:52 -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 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

Engine v2 is opt-in right now. If you want to run the new engine instead of the legacy agent loop, start IronClaw with ENGINE_V2=true. See Engine v2 architecture for more details.

# First-time setup (configures database, auth, etc.)
ironclaw onboard

# Start interactive REPL
cargo run

# Start interactive REPL with engine v2
ENGINE_V2=true cargo run

# Engine v2 with debug logging
ENGINE_V2=true 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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