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Inception ICORE Server MCP Server

@nbursa

代码仓库

基本信息

分类

AI 与智能体

许可证

View license

传输方式

stdio

作者

nbursa

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.

#docker#rust#experimental#memory#dsl#sqlite#inference#autonomous-agents#ai-agents#vector-search#agent-framework#axum#llm#chromadb

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

1

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.

2

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).

3

Where is data stored?

Short-term memory is in-memory (volatile), long-term in SQLite file (`memory.db`), latent memory in ChromaDB.

4

What transport protocol does the API use?

HTTP with JSON request/response, built on the Axum framework.

5

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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