Is Inception ICORE Server ready for production?
No, it is a work in progress under active development; interfaces, APIs, and internal structure may change frequently.
Inception ICORE Server MCP Server
@nbursa
基本信息
关于 Inception ICORE Server MCP Server
Model–Context–Protocol (MCP) Server A modular, extensible Rust-based server providing short-term, long-term, and latent memory services, a chat endpoint backed by a BaseAgent + Sentience DSL, and seamless integration with ChromaDB and LLM services.
Inception ICORE Server MCP Server 配置
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"inception-core-server": {
"command": "docker",
"args": [
"run",
"-d",
"--name",
"chroma-local",
"\\"
]
}
}
}Inception ICORE Server MCP Server 工具
工具列表暂未提供
当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。
工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。
概览
What is Inception ICORE Server?
A modular, extensible Rust-based server providing short-term, long-term, and latent memory services, a chat endpoint backed by a BaseAgent + Sentience DSL, and seamless integration with ChromaDB and LLM services. It is designed as a flexible microservice for building AI systems with layered memory and agent logic.
How to use Inception ICORE Server?
Clone the repository, build with cargo build, set environment variables (CHROMADB_URL, CHROMA_COLLECTION_ID, LLM_URL, ICORE_ENV), then run with docker-compose -f docker-compose.dev.yml up -d --build or run natively. Test the server with curl -i http://localhost:8080/api/ping expecting a "pong" response.
Key features of Inception ICORE Server
- Modular memory layers (short-term, long-term, latent via ChromaDB)
- BaseAgent with remember/recall/context logic
- Sentience DSL for scripting custom response logic
- LLM fallback integration (HTTP-based)
- Docker Compose orchestration with ChromaDB and LLM
- RESTful JSON API (Axum, Tokio async)
Use cases of Inception ICORE Server
- Build chatbots with persistent and ephemeral memory
- Prototype AI agents using Sentience DSL scripting
- Manage and query vector embeddings for similarity search
- Enable fallback to local LLM for text generation
- Create modular AI systems with separate memory layers
常见问题
What are the runtime dependencies?
Rust toolchain, Docker (for ChromaDB and LLM), a ChromaDB service, and an HTTP-based LLM server (e.g., llama.cpp).
Where is data stored?
Short-term memory is in-memory (volatile), long-term in SQLite file (`memory.db`), latent memory in ChromaDB.
What transport protocol does the API use?
HTTP with JSON request/response, built on the Axum framework.
Are there any known limitations in the current prototype?
Latent memory embeddings are dummy zero-vectors; real LLM encoder integration is not yet implemented.
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