Graphlit

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Graphlit is a semantic memory platform designed to empower AI agents with persistent, structured, and time-aware memory. By integrating semantic relationships and temporal sequences, Graphlit enables agents to retain and recall prior interactions, decisions, and context across sessions, transforming them from stateless responders into stateful systems capable of long-term collaboration and adaptive planning. Key Features and Functionality: - Agent Memory Platform: Provides the infrastructure for AI agents to maintain durable, time-aware memory across workflows, handling the full lifecycle from content ingestion to enabling agents to reason over accumulated history. - Knowledge Fabric: Creates an interconnected network of organizational memory that spans tools, teams, and time, integrating information from various sources into a unified, queryable structure. - Stateful Agent Support: Enables agents to retain memory of previous steps, outcomes, and decisions, allowing them to learn from experience and adapt behavior based on past results. - Memory Indexing: Organizes memory by entities, relationships, time ranges, topics, and metadata, facilitating fast retrieval of relevant context without reprocessing entire histories. - Context Engine: Dynamically assembles the most relevant structured memory for a given task, combining semantic retrieval, temporal awareness, entity relationships, and importance scoring. Primary Value and Problem Solved: Graphlit addresses the challenge of AI agents lacking continuity and context in their operations. By providing a durable, structured memory system, it eliminates manual context updates, enables multi-agent workflows, and supports complex, long-running tasks. This results in agents that improve over time, maintain consistent behavior, and deliver reliable results without requiring full context re-briefing at every interaction.