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Give AI agents persistent memory and temporal knowledge across sessions.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Hebbrix MCP Server" yet — see the docs or source repo.
Store the following project context in long-term memory and keep using it in future sessions: Our product serves education customers, key metrics are next-day retention and paid conversion, and the current priority feature is homework grading.
The agent saves the project context and can use it for future answers and reasoning across sessions.
Add these events to the temporal knowledge graph: New pricing page launched in March; paid conversion increased 12% in April; a new onboarding A/B test started in May. Then analyze possible causal relationships.
The agent organizes events over time and provides analysis clues based on temporal relationships.
Remember the research hypotheses, evidence, and open questions we discussed earlier. When I return next time, first recap the current state and then continue the reasoning.
The agent can restore context in a new session, summarize prior conclusions, and continue the work.
Developers can use it when connecting MCP tools to AI agents to preserve user preferences, project context, and prior conclusions. This helps the agent stay consistent across repeated sessions and reduces repeated explanations.
Research or product teams can use the temporal knowledge graph to organize events, conclusions, and their order in time. This is useful for multi-stage reviews and ongoing reasoning.
It provides long-term memory and a temporal knowledge graph for AI agents, allowing them to retain information and continue reasoning across sessions.
It is suitable for AI agents that need continuity across sessions, such as long-running project assistants, research assistants, or workflows that track historical events.
The provided material does not include installation steps, runtime requirements, or key requirements. For exact prerequisites, see the source repository.
Give AI agents persistent knowledge-graph memory and cross-session retrieval.
Store and search AI conversation history for cross-session recall of reasoning.
Connect AI assistants to persistent memory for cross-chat long-term context.
Give AI assistants a persistent, searchable memory layer and context management.
Enable AI assistants to store, search, and manage persistent semantic memories.
Build and query persistent knowledge graphs so coding agents remember across sessions.