LiteLLM open-sources agentchat SDK for multi-platform agent deployment
The messaging-adapter layer is the least glamorous part of agent deployment and usually gets rebuilt per project; a shared open-source version lowers the cost of shipping agents to where users already chat. Developers running agents in production can plug it in instead of maintaining five sets of webhook and OAuth plumbing. It works alongside LiteLLM rather than replacing it, covering channels instead of model routing.
LiteLLM (BerriAI) has open-sourced agentchat, a Python SDK that connects AI agents built with any agent framework to Slack, Teams, iMessage, WhatsApp, Discord and email. The SDK handles OAuth, webhook signature verification, socket connections, deduplication and session isolation so developers skip repetitive integration work. Its architecture pipes platform events through channel adapters into a core layer that deduplicates, locks sessions, stores history and dispatches to application handlers. According to the team, an agent can be connected to a channel in 2 lines of code. The code is available at github.com/BerriAI/agentchat.
LiteLLM 把 Agent 传输层 SDK 开源了 agentchat 这个 Python SDK 让你可以用任意框架写的 AI Agent 统一接入 Slack、Teams、iMessage、WhatsApp、Discord 五个聊天平台。避免自己处理一遍 OAuth、webhook 签名、Socket 连接、去重、会话隔离这种接入工作。 架构：管道 + 协议 + 值对象 平台事件 → Channel 适配器（解码/路由）→ AgentChat 核心（去重→加会话锁→入历史→分发） → 应用 handler（授权、调用你的 agent）→ 回复原路送达 开源地址： github.com/BerriAI/agentc… Ishaan @ishaan_jaff We're open sourcing agentchat, it's a unified SDK that allows you to connect your agent to Slack, Teams, iMessage, email, and WhatsApp in 2 lines of code and works with any agent SDK We noticed we kept on rebuilding the stack to connect our agents to different channels and ended up building the same shared layer. That's where agentchat came in. This allows you as a developer to focus on your core agent's brain and let the SDK manage your connections with different channels. 🔗 View Quoted Tweet 💬 3 🔄 1 ❤️ 4 👀 410 📊 3 ⚡
