AgentConnect

Product Introduction

1. Definition: AgentConnect is an open-source, self-hostable platform and control plane for orchestrating AI agents within team collaboration environments. Technically, it is a multi-agent orchestration system that integrates with the Agent Client Protocol (ACP) and Model Context Protocol (MCP) ecosystems.
2. Core Value Proposition: It exists to enable teams to deploy, manage, and collaborate with specialized AI agents directly within their existing workflows on platforms like Slack, Discord, Telegram, and GitHub. Its primary value is providing enterprise-grade control, visibility, and interoperability for AI agents, solving the fragmentation and security concerns of using disparate, single-purpose AI bots.

Main Features

1. Multi-Channel, Multi-Agent Orchestration: AgentConnect allows teams to deploy multiple AI agents (like Claude Code, Codex, or custom ACP runtimes) and connect them to various communication channels simultaneously. How it works: The platform's control plane manages agent identities, routing logic, and permissions. Inbound triggers from channels (like a Slack mention or a GitHub PR) are routed to the appropriate agent daemon running on your infrastructure. Agents can then call upon each other's skills, creating a collaborative workflow.
2. Granular Agent Configuration & Permissions: Each agent can be assigned a specific role, model runtime, memory system, tools, and access permissions. How it works: Administrators define agents in the console, specifying their purpose (e.g., support-bot), linking them to an ACP-compatible runtime (e.g., Claude Code), scoping their access to specific tools (e.g., Linear, Sentry), knowledge bases, and channels. This ensures agents operate within strict security and operational boundaries.
3. Unified Operational Console & Visibility: The platform provides a single dashboard for monitoring all agent activity across channels. How it works: The console displays real-time metrics on agent status, active sessions, scheduled jobs (daemons), and tool usage. Teams can see the entire context of work—from a triggered conversation in Slack to the subsequent actions taken by multiple agents—without switching between apps, enabling oversight and audit trails.
4. Open Ecosystem Integration: Built on open standards like ACP and MCP, it supports a vast tooling ecosystem. How it works: Agents can utilize any MCP server to connect to data sources (Notion, BigQuery) or tools (Figma, GitHub). Skills like code-review or web-search can be shared or restricted between agents. This avoids vendor lock-in and allows teams to integrate their existing stack.

Problems Solved

1. Pain Point: Fragmented and insecure AI agent deployment. Teams often build one-off bots or use multiple, disconnected AI services, leading to inconsistent behavior, no central management, and credentials scattered across insecure environments.
2. Target Audience: Engineering & DevOps Teams needing automated code review, deployment checks, and incident response. Product & Support Teams requiring AI-powered customer support triage and internal Q&A. Cross-functional teams seeking to automate repetitive workflows across communication and development tools.
3. Use Cases: Automated Code Review & PR Triage: An agent like review-bot is triggered on every GitHub PR push, while advance-review-bot performs deep security audits on request. 24/7 Incident Response: A support-bot handles initial user queries in Discord, escalates complex issues to a human channel, and an ops-bot automatically scales infrastructure in response to PagerDuty alerts. Scheduled Operational Reporting: A cron-triggered agent compiles and posts a daily standup digest or weekly metrics report to a team channel.

Unique Advantages

1. Differentiation vs. Claude Tag/Enterprise: Unlike Claude Tag (a single, proprietary agent on Anthropic's cloud), AgentConnect is model-agnostic, supports multiple agents concurrently, is self-hostable, and provides team-level controls. Unlike building custom bots, it offers a complete, pre-built orchestration layer with permissions, memory, and tool management.
2. Key Innovation: Its architecture separates the control plane (configuration, routing, UI) from the agent daemons (where the AI runtime and your code/credentials execute). This ensures sensitive data never leaves your infrastructure, while still providing centralized management. The use of ACP as the core runtime protocol future-proofs it against model provider changes.

Frequently Asked Questions (FAQ)

1. What is the difference between AgentConnect and building my own AI bot? Building a bot handles basic messaging. AgentConnect provides the essential team collaboration layer: multi-agent coordination, shared memory and knowledge bases, granular tool permissions, scheduled jobs, and a unified console for visibility across all channels, which teams would otherwise have to rebuild from scratch.
2. Where does my code and conversation data go when using AgentConnect? All sensitive data—code repositories, conversation transcripts, and tool credentials—resides on your self-hosted agent daemons. Only configuration metadata is stored in the control plane. Model API calls go directly from your infrastructure to your provider (e.g., Anthropic, OpenAI); AgentConnect does not proxy or store this data.
3. Which AI models and agents are compatible with AgentConnect? AgentConnect is compatible with any runtime that implements the open Agent Client Protocol (ACP), including Claude Code, Codex, and Gemini CLI. You can run different models for different agents (e.g., Claude for support, Codex for code review) and switch or add new ACP runtimes without platform changes.
4. Is AgentConnect open source or a hosted SaaS? It is both. The core AgentConnect platform is open-source (Apache 2.0) and can be self-hosted on your own infrastructure. AgentConnect Cloud is a hosted management console (currently in waitlist) for teams that prefer a managed control plane while their agents run in their own environment.
5. How does AgentConnect handle agent memory and knowledge? It supports multiple memory backends. Agents can use native session memory, managed memory via the platform, or integrate with external systems like Mem0 OSS. Knowledge bases (like internal docs) can be made read-only to agents at the start of a session, with suggestions for new knowledge to be reviewed and accepted by the team.