Power your agent
การติดตั้งจากผู้เผยแพร่ ไม่ใช่เอนด์พอยต์ของ PlugMCP
URL แบบรีโมตหรือคำสั่งบนเครื่องของคุณ? การติดตั้งสองแบบต่างกันอย่างไร
รันบน PlugMCP
A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.
เปิดจากผู้เผยแพร่
culi (cà phê culi — the single dense peaberry bean) is the memory and context layer between your coding agents and your projects. It stores rules, lessons, styles, patterns, skills, and agent definitions as small Markdown cards, then routes a token-budgeted selection to Claude Code or Codex.
Self-improving context & memory for Claude Code and OpenAI Codex. One canonical knowledge store injected only when relevant — hooks push budgeted context, an MCP server serves depth on demand, and it learns lessons from your sessions. Agent memory + context engineering as a single static Go binary.
It is local-first, git-backed, and inspectable. Hooks and retrieval make no model calls; optional background learning proposes new knowledge for review. No Node, Python, or resident daemon is required.
claude:<id> and
codex:<id> provenance.~/.culi/knowledge/; SQLite holds rebuildable card-search tables
plus preserved local activity state.go install github.com/hung12ct/culi/cmd/culi@latest
culi init # auto-detect Claude Code and Codex
culi serve # http://localhost:7378
Open a new coding-agent session after initialization. Codex users should run /hooks once and
trust the four generated Culi hooks. Verify the wiring with:
culi doctor --harness=codex
Ollama is optional. Without it, retrieval uses SQLite FTS/BM25. For semantic retrieval, install
Ollama and run ollama pull nomic-embed-text.
For harness-specific setup, adding your first cards, and importing existing instructions, see the getting started guide.
The local console makes Culi's behavior visible and reversible: review proposed lessons, search and edit cards, filter delivery activity by agent/repository/date, compare hot cards across Claude and Codex, and safely tune the learning backend.
| Home — health, next actions, and savings | Review — evidence-first lesson decisions |
|---|---|
 |  |
prompt ──► cheap gate ──► scope + keyword + optional semantic retrieval
│
rank + budgeted pack
│
Claude Code or Codex
│
session transcript ──► background mining ──► review ──► knowledge cards
search_context, expand_card, and save_lesson.The default auto provider tries configured APIs first, then signed-in Claude and Codex terminals.
Ollama is opt-in so Culi never silently chooses an unprepared local generation model. Model calls
never occur in prompt hooks.
See learning and cost for Codex backfill, authentication, caps, model defaults, and manual learning commands.
config.yaml reference and safe console settings.Run culi help for the complete command list.
go install (the Go toolchain can download the pinned version automatically).nomic-embed-text is optional.Actively developed and dogfooded daily. Built on gopheragent. Issues and ideas are welcome.
MIT © 2026 hung12ct
Self-improving context & memory for Claude Code and OpenAI Codex. One canonical knowledge store injected only when relevant — hooks push budgeted context, an MCP server serves depth on demand, and it
ภาพรวม
Self-improving context & memory for Claude Code and OpenAI Codex. One canonical knowledge store injected only when relevant — hooks push budgeted context, an MCP server serves depth on demand, and it learns lessons from your sessions. Agent memory + context engineering as a single static Go binary.
เครื่องมือ
ยังไม่มีรายการเครื่องมือ เซิร์ฟเวอร์แบบรีโมตจะได้รายการหลังการตรวจสอบสถานะครั้งแรก
FAQ
คัดลอกคอนฟิก stdio สำหรับ Cursor, Claude Desktop, Claude Code หรือ VS Code ได้ในหน้านี้ คอนฟิกนี้ใช้การติดตั้งของผู้เผยแพร่ ไม่ใช่พร็อกซีของ PlugMCP
ระเบียนนี้ไม่ได้ระบุตัวแปรสภาพแวดล้อมที่จำเป็น
Culi ทำงานแบบโลคัลผ่าน stdio
ตรวจสอบล่าสุดเมื่อ 2026-10-28 Self-improving context & memory for Claude Code and OpenAI Codex. One canonical knowledge store injected only when relevant — hooks push budgeted context, an MCP server serves depth on demand, and it learns lessons from your sessions. Agent memory + context engineering as a single static Go binary.
