FrostAgent is a Go-based AI agent orchestration framework that bridges LLM providers with real-time chat platforms. It wraps any OpenAI-compatible API endpoint behind a structured agentic engine that handles multi-step tool-call loops, maintains per-user session context, and exposes a OneBot v11 WebSocket adapter so you can connect existing QQ bots — or any compliant client — without writing integration glue. A built-in ConnectRPC management API and embedded SPA give you live visibility into bot status, logs, and settings from day one.Documentation Index
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What FrostAgent provides
Agentic Engine
A multi-step tool-call loop with configurable max iterations that drives conversations to completion before replying.
Tool Registry
A first-class tool system with built-in tools (send message, sub-agent dispatch, weather, game version) and a clean interface for custom tools.
Session Management
Per-user context windows with configurable message count and character limits to keep LLM costs predictable.
OneBot v11 Adapter
A WebSocket handler compatible with the OneBot v11 protocol for group and private message routing out of the box.
Vision Support
A dedicated visual model slot (
VISUAL_MODEL_NAME) for multimodal inputs, configurable independently of the primary chat model.Management API
A ConnectRPC service (bot status, settings, log streaming) backed by an embedded single-page application on the HTTP port.
Project status
FrostAgent is in early-stage development and is currently intended for personal research and experimentation. APIs and configuration options may change between releases. Community contributions, pull requests, and guidance are warmly welcomed.
How it works
FrostAgent sits between your chat platform and your LLM provider as an active middleware layer rather than a passive proxy:- Inbound — A OneBot v11 client connects to FrostAgent’s WebSocket endpoint (
/ws/frostagent). Incoming group and private messages are received and dispatched to the engine. - Engine loop — The agentic engine builds a context window from the session history and sends it to the upstream LLM. If the model returns a tool call, the engine executes the registered tool, appends the result to the conversation, and calls the LLM again — up to
MaxIterations(default: 5) times. - Outbound — Once the engine produces a final text response, it is sent back over the same WebSocket connection. For group messages, FrostAgent can optionally prepend an
@mention so the reply is directed at the right user.
Technology stack
| Component | Library / Version |
|---|---|
| Language | Go 1.25.3+ |
| RPC layer | connectrpc.com/connect v1.20.0 |
| WebSocket | github.com/gorilla/websocket v1.5.3 |
| Env config | github.com/joho/godotenv v1.5.1 |
| Serialization | google.golang.org/protobuf v1.36.11 |