AIntegriX: The First Open-Source ACP Orchestrator for Multi-Agent Coordination
AIntegriX is an open-source server that coordinates multiple ACP agents via a single API, enabling parallel execution, pipelines, and intelligent routing.
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Key points
- AIntegriX acts as an ACP multiplexer, spawning agents as subprocesses and exposing them through a single MCP/REST endpoint.
- It supports orchestration modes: parallel, race, jury, and pipelines, along with auto-routing and webhook triggers.
- Built with Rust and licensed under Apache-2.0, it integrates with agents like Kiro, Copilot, Claude, and more.
Why it matters
This matters because aIntegriX acts as an ACP multiplexer, spawning agents as subprocesses and exposing them through a single MCP/REST endpoint.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
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AIntegriX is the first open-source ACP orchestrator — a server that coordinates multiple Agent Client Protocol agents through a single API.
What is ACP?
The Agent Client Protocol (ACP) is the open standard that connects code editors with AI coding agents — the same way LSP standardized language servers. Every major coding agent now speaks ACP: Kiro, GitHub Copilot, Claude Code, OpenCode, Codex CLI, and 30+ more.
The Problem
Each ACP agent runs independently. You can't:
Ask one agent to delegate work to another
Send the same prompt to multiple agents and compare
Chain agents in a pipeline (generate → review → fix)
Auto-trigger agents on Git events
The Solution
AIntegriX acts as an ACP multiplexer. It spawns agents as subprocesses, speaks JSON-RPC 2.0 over stdio with each one, and exposes them all through a single MCP/REST endpoint.
Your Agent (any MCP-compatible tool) │ ▼ MCP / REST ┌─────────────────────────────────┐ │ AIntegriX │ │ Route • Orchestrate • Stream │ │ (ACP Client) │ └──┬──────┬──────┬──────┬──────┬──┘ │ │ │ │ │ JSON-RPC 2.0 / stdio ▼ ▼ ▼ ▼ ▼ Kiro Copilot OpenCode Claude Codex (ACP) (ACP) (ACP) (ACP) (ACP)
One prompt. Any agent. Real results.
Why AIntegriX?
Problem Solution
Each agent has different strengths Smart routing picks the best agent for each task
Can't compare agent responses Orchestration sends to N agents in parallel
No way to chain agent work Pipelines feed output from one agent to the next
Agents can't read your latest code Auto-clone from your Git remote or direct local filesystem
Responses arrive all at once SSE streaming shows chunks in real-time
Manual code review requests Webhooks auto-trigger review on MR open
Workflow
┌─────────────────────────────────────────────────────────────┐ │ YOUR DEVELOPMENT FLOW │ ├─────────────────────────────────────────────────────────────┤ │ │ │ 1. Code locally │ │ │ │ │ 2. Ask AIntegriX: │ │ "Review src/ for security issues" │ │ │ │ │ 3. AIntegriX routes to Claude (security expert) │ │ │ │ │ 4. Claude reads your files, analyzes, responds │ │ │ │ │ 5. You get the review in your agent's context │ │ │ │ ─── OR ─── │ │ │ │ Pipeline: OpenCode generates → Claude reviews → Kiro fixes │ │ │ │ ─── OR ─── │ │ │ │ Race: Send to 3 agents, first response wins │ │ │ └─────────────────────────────────────────────────────────────┘
Quick Start
Local (recommended for development)
Install
git clone [email protected]:alfonsodg/aintegrix.git cd aintegrix cargo build --release ./configs/install.sh ~/.local/share/aintegrix
Add to your agent's MCP config
{ "mcpServers": { "aintegrix": { "command": "mcp-proxy", "args": ["-H", "Authorization", "Bearer aintegrix-local-key-2026", "--transport", "streamablehttp", "http://localhost:8050/mcp"], "env": {} } } }
Remote (for teams / CI)
{ "mcpServers": { "aintegrix": { "url": "https://your-server.example.com/mcp", "type": "http", "headers": {"Authorization": "Bearer "} } } }
Agents
Agent Model Best For
🧠 Kiro claude-opus-4.6 Rust, architecture, complex reasoning
🤖 Copilot gpt-5.3-codex Frontend, React, quick edits
⚡ OpenCode mimo-v2.5-pro Fast analysis, multi-file reads
🔍 Claude minimax-2.7 Security review, deep analysis
🛠️ Codex gpt-5.5-xhigh Python, refactoring, generation
Features
Core
5 ACP agents with real subprocess management
MCP server — any agent can use AIntegriX as a tool
REST API with Bearer auth
SSE streaming of agent responses in real-time
Orchestration
Parallel — send to N agents, collect all responses
Race — first response wins, cancel others
Jury — N agents respond, a judge picks the best
Pipelines — sequential multi-step workflows
Intelligence
Auto-routing — YAML rules pick the best agent by keywords/file patterns
Prompt rewriting — per-agent prefix/suffix
Git-aware sessions — inject branch, commits, diff as context
Context injection — auto-load steering files
Operations
Auto-clone repos — fresh checkout from your Git remote (remote mode)
Local filesystem — direct access to your code (local mode)
Webhook triggers — MR/PR → auto code review
Session fork — try same conversation with different agent
Cost tracking — usage per agent/session/model
Live status — agent idle/busy with session counts
API
Method Endpoint Description
POST /api/v1/sessions Create session
POST /api/v1/sessions/{id}/prompt Send prompt
POST /api/v1/sessions/{id}/fork Fork to another agent
POST /api/v1/orchestrate Multi-agent (parallel/race/jury)
POST /api/v1/pipelines Sequential agent chaining
POST /api/v1/stream Create + stream SSE
POST /api/v1/webhooks/git Receive webhook events
GET /api/v1/agents/status Live agent status
GET /api/v1/usage Cost tracking
POST /mcp MCP JSON-RPC endpoint
Documentation
Installation
Configuration
API Reference
Architecture
Development Standards
Diagrams
Tech Stack
Layer Technology
Language Rust (latest stable)
Runtime tokio
HTTP axum
Protocol ACP (JSON-RPC 2.0 over stdio)
Database SQLite (sqlx)
Config YAML
License
Apache-2.0
Built with 🦀 Rust • Powered by ACP
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AIntegriX is a centralized orchestrator using ACP
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AIntegriX v0.1.0
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May 26, 2026
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Rust 91.0%
Shell 4.9%
Python 3.6%
Dockerfile 0.5%