Inception ICORE Server

Name: Inception ICORE Server
Author: nbursa

@nbursa

About Inception ICORE 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

Basic information

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "inception-core-server": {
      "command": "docker",
      "args": [
        "run",
        "-d",
        "--name",
        "chroma-local",
        "\\"
      ]
    }
  }
}

Tools

No tools detected

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

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

FAQ from Inception ICORE Server

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.

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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