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@@ -4,7 +4,7 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co
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## Project Overview
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Sico is an open-source AI agent platform with three main services that communicate via gRPC and a bidirectional "reverse RPC" pattern. The backend handles HTTP APIs and persistence, the core handles AI/LLM orchestration, and the frontend provides a React dashboard.
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Sico is an open-source AI agent platform with three main services that communicate via gRPC and a bidirectional "reverse RPC" pattern. The backend handles HTTP APIs and persistence, the core handles AI/LLM orchestration, and the frontend provides a React dashboard (frontend codebase is not opensourced).
-**`proto-types/src/`** — generated TS types from protobuf (do not edit manually; emitted by `bash gen.sh` in `proto/`).
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- Uses Vite, Tailwind CSS, styled-components, React Router, and `@` path alias mapped to `src/`.
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### Sandbox System
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The sandbox subsystem manages isolated environments (Android emulators, AIO containers) for agent tool execution. Sandboxes are pooled and leased to agent instances. The `sandbox/emulator/` directory contains the Android emulator sandbox implementation (Python).
|**Backend**| Go | Gin | HTTP API gateway, JWT/Casbin auth for operator-facing APIs, sandbox-client HMAC auth, data persistence, reverse gRPC server |
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|**Core**| Python | asyncio | Agent orchestration, LLM invocation, tool execution, Experience Learning; no direct MySQL access, with runtime state in workspace files and memory/vector stores |
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|**Frontend**|-|- | Operator-facing web client for human users (not open source)|
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|**Frontend**|TypeScript|React / Vite | Operator-facing web client for human users; source code is not currently published in this repository and is distributed separately as a packaged archive|
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Sico uses two services because AI orchestration and platform responsibilities evolve at different speeds and benefit from different runtimes. The Backend service is implemented in Go for high-throughput HTTP handling. The Core service is implemented in Python to support rapid iteration across the AI/LLM ecosystem. This separation decouples deployment cycles and allows each service to optimize for its primary workload.
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