* feat(agent): queue and merge messages during active turns
Replace the hard rejection ("Turn in progress") when messages arrive
during an active turn with a bounded queue (max 10) that auto-drains
after the turn completes.
Queued messages are merged with newlines into a single turn so the LLM
receives full context from rapid consecutive inputs instead of producing
fragmented responses from partial context.
Key changes:
- Thread.pending_messages (VecDeque) with queue_message/drain_pending_messages
- Drain loop in agent_loop.rs merges all queued messages per iteration
- interrupt() and /clear both clear the pending queue
- MAX_PENDING_MESSAGES constant with cap enforced inside queue_message()
- Drain loop continues on soft errors, stops on NeedApproval/Interrupted
- Drain loop logs respond() failures instead of silently swallowing them
Fixes #259 — debounces rapid inbound messages during processing
Fixes #826 — drain loop is bounded by MAX_PENDING_MESSAGES cap
Co-Authored-By: Claude Opus 4.6 (1M context) <[email protected]>
* fix: address PR review — drain loop busy-loop guard and stale state re-check
- Add Ok(SubmissionResult::Ok) to drain loop break conditions to prevent
a tight busy-loop if process_user_input returns a queued-ack (e.g. from
a corrupted/hydrated session stuck in Processing state)
- Re-check thread.state under the mutable lock in the Processing arm to
guard against the turn completing between the snapshot read and the
queue operation
Co-Authored-By: Claude Opus 4.6 (1M context) <[email protected]>
* fix: clear attachments on drain-loop queued message processing
Queued messages are text-only (queued as strings during Processing
state). The drain loop was reusing the original IncomingMessage
reference which carried the first message's attachments, causing
augment_with_attachments to incorrectly re-apply them to unrelated
queued text. Clone the message with cleared attachments for drain-loop
turns.
Co-Authored-By: Claude Opus 4.6 (1M context) <[email protected]>
* fix: address PR review round 2 — stale state fallthrough and thread-not-found guard
- Processing arm: when re-checked state is no longer Processing, fall
through to normal processing instead of dropping user input
- Processing arm: return error when thread not found instead of false
"queued" ack
- Document intermediate drain-loop responses as best-effort for one-shot
channels (HttpChannel)
- Add regression tests for both edge cases
Co-Authored-By: Claude Opus 4.6 (1M context) <[email protected]>
* fix: address PR review feedback for message queue drain loop
[skip-regression-check] — test modifications present but hook has
SIGPIPE/pipefail false negative when awk exits early on match
- Replace wildcard match in drain loop with explicit `while let
Ok(Response)` guard — stops on Error variant too, preventing
confusing interleaved output after soft errors (review issue #1)
- Reject queueing messages with attachments during Processing state
instead of silently dropping them (review issue #2)
- Document response routing limitation: all drain-loop responses
route via original message identity (review issue #3)
- Document why SubmissionResult::Ok is correct for queued ack and
how it interacts with drain loop break condition (review issue #4)
- Rewrite two dead regression tests to assert actual behavior:
thread-gone returns error, state-changed does not queue (review #5)
- Document MAX_PENDING_MESSAGES=10 as acceptable for personal
assistant use case (review issue #6)
- Fix misleading one-shot channel comment — HttpChannel consumes
sender on first call, subsequent calls are dropped (review issue #8)
- Simplify drain loop intermediate response since while-let guard
guarantees Response variant
Co-Authored-By: Claude Opus 4.6 (1M context) <[email protected]>
* fix: add missing extension_manager field in webhook EngineContext
The fire_webhook method's EngineContext initializer was missing the
extension_manager field added in staging, causing CI compilation failure.
[skip-regression-check]
Co-Authored-By: Claude Opus 4.6 (1M context) <[email protected]>
* fix: gate TestRig::session_manager() behind libsql feature flag
The field is #[cfg(feature = "libsql")] so the accessor must match.
All callers are already inside #[cfg(feature = "libsql")] blocks.
[skip-regression-check]
Co-Authored-By: Claude Opus 4.6 (1M context) <[email protected]>
* fix: re-queue drained messages on drain loop failure
If process_user_input fails after drain_pending_messages() removed
all queued content, that user input was permanently lost. Now the
merged content is re-queued at the front of pending_messages on any
non-Response result so it will be processed on the next successful
turn.
Adds Thread::requeue_drained() helper and unit test.
Co-Authored-By: Claude Opus 4.6 (1M context) <[email protected]>
* fix: remove unreachable!() from drain loop, add lock-drop comments
- Extract content binding in `while let` pattern instead of using a
separate match with unreachable!() — satisfies the no-panic-in-
production convention (zmanian review item #1)
- Add comment clarifying session lock is dropped at Processing arm
boundary before fall-through (zmanian review item #5)
- Document bounded cap overshoot on requeue_drained (review item #2)
[skip-regression-check]
Co-Authored-By: Claude Opus 4.6 (1M context) <[email protected]>
* fix(security): validate queued messages and touch updated_at on queue ops
- Run safety validation, policy checks, and secret scanning on
messages before queueing during Processing state. Previously,
content with leaked secrets could be stored in pending_messages
and serialized without hitting the inbound scanner.
- Touch updated_at in queue_message(), drain_pending_messages(),
and requeue_drained() so thread timestamps reflect queue activity.
[skip-regression-check] — safety validation requires full Agent;
updated_at is a data-level fix on existing tested methods
Co-Authored-By: Claude Opus 4.6 (1M context) <[email protected]>
---------
Co-authored-by: Claude Opus 4.6 (1M context) <[email protected]>
IronClaw
Your secure personal AI assistant, always on your side
English | 简体中文 | Русский | 日本語
Philosophy • Features • Installation • Configuration • Security • Architecture
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/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.shbeforecargo buildso 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:
- Apache License, Version 2.0 (LICENSE-APACHE)
- MIT License (LICENSE-MIT)
at your option.
