$ loading_
Manage LLM context, compact conversations, and retain durable semantic memory.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Infinite Context Keeper" yet — see the docs or source repo.
Analyze the current conversation's context usage, identify the most token-heavy messages, and generate a compact summary that preserves key decisions, action items, and constraints for continued use.
Returns a context usage estimate, pruning suggestions, and a concise summary ready for continued conversation.
Store the project's technology choices, architecture conventions, known issues, and key conclusions as searchable semantic memory, organized by topic for future sessions.
Provides saved memory entries, topic categorization, and a durable memory structure for later retrieval and reuse.
Retrieve historical discussions related to 'user permission system refactor' from local semantic memory, summarize the background, decisions made, unresolved issues, and generate a context pack to continue the work.
Returns a summary of relevant history, key memory matches, and a context pack that can be injected into a new session.
Give LLMs persistent semantic memory and vector search for better context continuity.
Provide persistent virtual memory, retrieval, and prompt assembly for AI context.
Let AI assistants read and edit ContextKeeper context items with natural language.
Build a persistent, searchable memory layer for MCP-compatible LLM clients.
Manage remote AI memory banks for persistent context and project knowledge.
Give AI agents portable memory with temporal and semantic retrieval scoring.