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자체 호스팅, Docker 기반, 오픈소스 AI 메모리 시스템입니다. Model Context Protocol(MCP)을 통해 프로젝트 범위의 메모리 저장소를 AI 코딩 어시스턴트에 노출하므로, 어시스턴트는 값비싼 탐색 작업을 다시 수행하지 않고도 이전 세션에서 학습한 내용을 기억할 수 있습니다.

상태

v1 진행 중입니다. 사양은 docs/spec.md, 의도는 docs/intent.md, 작업 분류는 tasks/todo.md에 있습니다.

Related MCP server: AIVectorMemory

빠른 시작

cp .env.example .env          # fill in API_KEY and GEMINI_API_KEY
docker compose up -d          # server + Qdrant
docker compose logs -f int    # tail server

서버에 OpenCode를 연결하려면 opencode.json에 원격 MCP 항목을 추가하세요:

{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "int": {
      "type": "remote",
      "url": "http://localhost:8000/mcp",
      "headers": { "API_KEY": "{env:API_KEY}" }
    }
  }
}

전체 설정, CLI 사용법, 일반적인 문제는 docs/deployment.md를 참조하세요.

도구

도구 이름은 접두사 없이 (add, search, ...) 사용됩니다. MCP 서버는 int로 등록되므로, MCP 클라이언트는 이를 int_add, int_search, int_list, int_delete로 노출합니다.

도구

입력

출력

add

project, type, content

memory_id

delete

memory_id

deleted: bool

search

project, query, limit=5

순위가 매겨진 SearchResult[]

list

project

메타데이터 전용 Memory[]

기술 스택

Python 3.14, FastAPI + MCP (mcp Python SDK, Streamable HTTP), Qdrant (별도 컨테이너), gemini-embedding-001 (MRL을 통해 768차원으로 L2 정규화됨), Docker Compose, uv (의존성 관리용).

라이선스

Apache-2.0 — 자세한 내용은 LICENSE를 참조하세요.

F
license - not found
-
quality - not tested
B
maintenance

Maintenance

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