Adaptive Recall

io.github.AIAppsAPI由 GitHub 帳號 AIAppsAPI 發布v1.0.1更新於 Oct 24, 2026

Adaptive memory system with cognitive scoring, knowledge graph, and self-improving ML.

已驗證STDIO僅桌面知識與記憶AI 與機器學習

概覽

AI 產生的概覽

託管式長期記憶伺服器,讓助理儲存、檢索、更新與刪除記憶,並具備認知評分與知識圖譜。

可以做什麼
  • 儲存、更新與刪除記憶,自動產生向量並擷取實體
  • 以多策略檢索召回記憶,並依認知評分排序
  • 依實體名稱與遍歷深度探索知識圖譜
  • 查看系統狀態、記憶數量、信心度分布與知識缺口
  • 依類型取得已存記憶的格式化快照,並向開發者送出意見回饋
適用情境
適合需要跨工作階段保留長期記憶的情境,例如使用者偏好、事實、專案筆記或操作流程,並以相關性排序而非單純關鍵字檢索的方式取回。
執行需求
需要託管的 Adaptive Recall 帳號:註冊後取得伺服器網址與 API 金鑰。金鑰透過 ADAPTIVE_RECALL_API_KEY 環境變數或 Authorization 標頭中的 Bearer 權杖提供。需要能連線至該託管端點;免費額度之外可能需要付費方案。
安裝前請注意
所有記憶都會傳送並儲存在第三方託管服務,請勿存放密鑰或敏感個人資料。API 金鑰屬於憑證,應避免寫入共用設定。forget 會刪除記憶,update 會覆寫原有內容。免費額度之外的方案需付費。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 Adaptive Recall,將其新增到工作區。
  2. 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

Adaptive Recall

Adaptive memory system for AI applications. Patent pending.

adaptiverecall.com | Documentation | Sign Up Free

What It Does

Adaptive Recall is a hosted memory server that stores, retrieves, and manages long-term memory for AI applications. It connects via MCP or REST API.

  • Multi-strategy retrieval: four search strategies run in parallel (vector similarity, temporal recency, full-text keyword, knowledge graph traversal) and the system learns which to prioritize for each type of query
  • Cognitive scoring: results ranked using ACT-R activation modeling from cognitive science, factoring in recency, access frequency, entity connections, and validated confidence
  • Knowledge graph: entities and relationships extracted automatically from stored memories, used as a retrieval pathway alongside text similarity
  • Memory lifecycle: memories progress through stages, gain or lose confidence based on corroborating evidence, and fade naturally when unused
  • Self-improving: ML models train on your usage patterns, every parameter change must pass statistical validation against real query history before being adopted
  • Retrieval quality monitoring: the system verifies its own retrieval consistency and identifies knowledge gaps

Connect

Sign up at adaptiverecall.com to get your server URL and API key.

MCP Configuration

Add to your MCP client config (Claude Code, Codex, Cursor, or any MCP-compatible tool):

json
{  "mcpServers": {    "adaptive-recall": {      "type": "url",      "url": "https://YOUR_SERVER_URL/mcp",      "headers": {        "Authorization": "Bearer YOUR_API_KEY"      }    }  }}

For Claude Code, add this to .mcp.json in your project or ~/.claude/settings.json for global access. For Gemini CLI, add to ~/.gemini/settings.json using httpUrl instead of url. For Codex, add to your Codex MCP configuration.

REST API

Every action is also available as an HTTP endpoint at https://YOUR_SERVER_URL/v1/. All requests require a Bearer token in the Authorization header.

Actions

ActionDescription
storeSave a new memory. Generates embeddings and extracts entities automatically.
recallSearch memories using multi-strategy retrieval with cognitive scoring.
updateModify an existing memory. Re-embeds automatically if content changes.
forgetRemove a memory by ID or by finding the closest match to a query.
graphExplore the knowledge graph, traversing entity relationships by name and depth.
statusSystem health, memory counts, confidence distribution, and knowledge gap detection.
snapshotGet a formatted overview of stored memories, organized by type.
feedbackSend feedback directly to the Adaptive Recall developers.

Memory Types

When storing memories, assign a type that affects how the memory is managed:

Learning types (evolve over time, gain/lose confidence, have lifecycle stages):

  • general_knowledge - facts, observations, reference information
  • user_knowledge - information about people and their preferences

Lookup types (static reference, no lifecycle):

  • callable_scripts - tool and script references
  • work_project - project tracking, tasks, deadlines
  • cross_reference - pointers to external information and resources
  • learned_procedure - multi-step workflows and procedures

Pricing

Free, Starter, Pro, and Business plans available. See adaptiverecall.com for details.

Links

來源:README.md,提交 60e898c

工具

0
SourceWeft 尚未在沙箱中啟動過該軟體包,因此其工具未知。

版本歷史

1
  1. v1.0.1最新Sep 16, 2026

評分

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