
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.
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
在 SourceWeft 中
- 開啟 儀表板中的 Adaptive Recall,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
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):
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
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 informationuser_knowledge- information about people and their preferences
Lookup types (static reference, no lifecycle):
callable_scripts- tool and script referenceswork_project- project tracking, tasks, deadlinescross_reference- pointers to external information and resourceslearned_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版本歷史
1- v1.0.1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