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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.
Give LLMs persistent semantic memory and vector search for better context continuity.
Persistent knowledge-graph memory for MCP with semantic search and version tracking.
Give AI agents persistent memory and semantic retrieval across conversations.
Add session-scoped memory so assistants can store and retrieve conversation context.
Give AI assistants a persistent, searchable memory layer and context management.
Give AI assistants persistent memory, semantic search, and layered context management.