$ loading_
Build a persistent, searchable memory layer for MCP-compatible LLM clients.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "adl-context-collector" yet — see the docs or source repo.
Ingest these meeting notes, project decisions, and customer preferences into adl-context-collector and create long-term semantic memory. Later, when I ask “What has Client A been focused on recently?”, return the most relevant facts, timeline, and source summaries first.
A continuously updated knowledge base that returns relevant facts, temporal context, and source-backed summaries through semantic search.
Store the following structured facts with temporal tracking: the product owner changed from Li Ming to Wang Lu effective 2024-06-01; the budget changed from 500,000 to 800,000 effective 2024-07-15. When answering later questions, distinguish current values from historical ones.
Time-aware fact records that clearly surface both the current state and historical changes in later queries.
Ingest this product requirements doc, technical design, and retrospective report into adl-context-collector. In future sessions, whenever I ask “Why was this feature delayed?” or “What was the original goal?”, retrieve from the ingested documents first and summarize the answer.
Documents are ingested and available for cross-session retrieval, enabling fast, accurate answers grounded in prior materials.
Persistent knowledge-graph memory for MCP with semantic search and version tracking.
Give AI agents persistent memory and semantic retrieval across conversations.
Give AI assistants persistent memory, entity storage, and semantic search across sessions.
Give AI assistants a persistent, searchable memory layer and context management.
Provide shared cross-session memory storage, retrieval, and governance for MCP AI tools.
Give AI agents persistent long-term memory with hybrid semantic and keyword search.