
Engram
io.github.NickCirv由 GitHub 帳號 NickCirv 發布v4.5.0更新於 Oct 24, 2026
Ranked local code-graph context for AI coding agents. Intercepts reads + greps. Apache-2.0.
發布方自己的服務:Engram
概覽
Engram 為專案建立本機結構化程式碼圖,讓 AI 程式助理依需求取回相關程式碼脈絡,而不必讀完整個檔案。
- 可以做什麼
- Engram 會將原始碼實體、Git 關係與選定的專案記憶索引到專案本機的 SQLite 圖中,查詢時回傳符合的實體與估算的 token 數量。其命令列提供探索結構(query、path、gods、callers、callees、impact、stats)、維護脈絡(init、watch、reindex、learn、mistakes)、產生助理文件,以及透過 MCP 或本機面板提供脈絡等指令。它也提供可攔截讀取與 grep 的鉤子整合,以及可警告或拒絕的失誤防護。
- 適用情境
- 當助理需要在大型程式庫中先定位相關程式碼與其關係、再開啟完整檔案,或需要把已記錄的失誤帶到後續工作階段時,適合安裝。建議先使用程式碼圖並檢查其輸出,再決定是否啟用鉤子或其他整合。
- 執行需求
- 以本機 Node.js 程序執行(套件名稱 engramx,需 Node >= 20),透過 stdio 通訊。索引會在專案內寫入 .engram/ 圖。選用環境變數包括 ENGRAM_ANTHROPIC_MEMORY_PATH、ENGRAM_MCP_CONFIG_PATH、ENGRAM_MISTAKE_GUARD、ENGRAM_NO_UPDATE_CHECK,以及本機 HTTP 伺服器所需的密鑰 ENGRAM_API_TOKEN。MCP 伺服器本身未宣告需要帳號或驗證。
安裝
在 SourceWeft 中
- 開啟 儀表板中的 Engram,將其新增到工作區。
- 為需要使用其工具的對話啟用該服務。
Desktop only,透過 STDIO。 STDIO 服務會啟動本機處理程序,因此需要 SourceWeft 桌面主機。
其他 MCP 客戶端
參照 儲存庫 中的啟動說明。
README
[Engram — Nicholas Ashkar repository collection]
Engram
A local structural code graph and context toolkit for AI coding agents. Engram indexes source entities, Git relationships and selected project memory, then exposes focused context through a CLI, agent hooks, MCP and an authenticated local dashboard.
Use it to find relevant code before reading full files, inspect relationships and carry recorded mistakes into a later session. Read the source returned by a query before making a behavioral claim: graph retrieval is an aid to investigation.
Quickstart
The inspected package is engramx 4.5.0 and requires Node >=20. This source-build example is source-inspected, not executed; it does not assume a currently published package or extension version.
Replace ../your-project with a codebase you can inspect. Indexing writes the project's .engram/ graph. The --no-hook flag matters: the default CLI init also installs a Sentinel hook. Start with the graph, examine the output, then decide which integration to enable. The build includes grammar bundling; inspect package scripts and dependencies before installation.
The query returns matching graph entities and an estimated token count, or a no-matching-nodes message. Counts depend on the indexed project; no benchmark output is fabricated here.
What is implemented
These entry points are declared in the pinned CLI. Consult each command's help before a write operation. Generators, hook installers, cache/database commands and plugin installation can modify local state.
Architecture
Source miners extract entities, references, Git co-change and selected mistake/session information. The graph store persists project-local SQLite using sql.js. Queries traverse the graph and render bounded context. The provider resolver combines built-in sources and optional plugins/MCP providers; integrations can add external processes and network access.
The architecture and integration guide maps these paths, documents HTTP authentication and gives a local MCP configuration. Existing design records, release notes, integration documents and the ECP draft remain linked in the documentation inventory. They retain historical context and are not all certified against this revision.
Privacy and operational boundaries
The core graph is local, but the entire system is not guaranteed to be network-free. The CLI has npm update checks, Context7 can call an external wrapper, and configured MCP/plugin providers can execute their own processes. ENGRAM_NO_UPDATE_CHECK=1 disables the update-check path; it does not disable every optional integration. Review configured providers and the agent that receives graph output.
The HTTP server binds to 127.0.0.1, validates Host/Origin and requires a bearer token or dashboard cookie for protected routes. It resolves a token from ENGRAM_API_TOKEN or a local token file. Keep that token private and retain the existing security policy.
Limits and measurement
Structural context size, estimated token reductions, session replay and actual provider billing answer different questions. A smaller packet does not by itself demonstrate equal answer quality or a lower bill. Reproduce the included benchmarks on your own representative workload and track correctness alongside cost. This documentation review did not run a build, test suite, benchmark or agent session.
Development
The repository contains unit and integration test sources for graph storage, providers, hooks, HTTP security and other modules. Their presence is evidence of test coverage intent; results at this revision remain unverified here. See the preserved contribution guide before changing behavior.
License and author
Apache-2.0 license. The ECP specification carries its own attribution and licensing in its source document; this rewrite does not alter either.
Nicholas Ashkar · Applied AI, systems and consulting.
User guides
User manual covers setup, skills, troubleshooting and removal. Sentinel explains current hook behavior and guard modes. Tool authors can use the integration guide.
來源:README.md,提交 df445d3
工具
0版本歷史
1- v4.5.0最新Sep 16, 2026
評分
已滿足適用檢查項的 50%
該評分衡量此收錄條目的完整程度,依據註冊中心條目、端點與公開來源計算得出。它不是對伺服器本身的評價,也不表示伺服器安全或目前可用:可連通性是帶日期的檢查結果,評分也不包含安裝量或使用者評價。
- 近 30 天內可連通25 分
請保持端點能夠回應 MCP 握手;SourceWeft 每 30 天探測一次。
- 已宣告認證方式10 分
請在註冊中心條目中宣告憑證請求標頭,或保持端點能夠回應探測,以便判定其認證方式。
- 已列出工具15 分
請讓伺服器在無憑證時回應 tools/list,或發布可在沙箱中無憑證啟動的軟體包。
- 描述必需15 分
- 圖示10 分
- 文件15 分
- 授權條款10 分
其他 Engram 整合

Engram
tools.engram官方發布者
Engram 為 AI 助理提供跨工具、工作階段、程式碼儲存庫與團隊成員共享的持久記憶層。

Engram
由 GitHub 帳號 TushaeBXN 發布
面向AI的本機私密長期記憶:逐字儲存、可搜尋、可更正。無需LLM或API金鑰。AI 翻譯

Engram: AI memory you can edit in Obsidian
由 GitHub 帳號 engram-app 發布
託管式 MCP 記憶伺服器,讓 Claude、ChatGPT 等 MCP 用戶端共用可讀寫的筆記,並可在 Obsidian 或網頁應用程式中編輯。

Engram
由 GitHub 帳號 devinmlowe 發布
Engram 是面向編碼代理的本地優先認知記憶伺服器:擷取對話、提取結構化知識,並提供混合檢索、知識圖譜與記憶管理。

Engram
由 GitHub 帳號 tstockham96 發布
Engram 為 AI 助理提供本機 SQLite 記憶庫,支援語意檢索、知識圖譜與由 LLM 驅動的記憶整併。