Agent Memory
Agent memory is the capability of an AI agent to store, retrieve, and utilize information from past interactions, observations, and actions to inform future behavior, enabling persistent context across sessions.
Understanding Agent Memory
A stateless agent that forgets everything between conversations is severely limited. Agent memory transforms a session-bound AI into a persistent digital colleague that knows your preferences, remembers past discussions, and builds an ever-richer model of your work and relationships. Agent memory operates across multiple timescales and types. Short-term memory holds the current conversation context within the active session. Long-term memory persists across sessions, storing facts, preferences, and learned patterns. Episodic memory records specific past events and interactions. Semantic memory stores general knowledge about entities, relationships, and concepts. Working memory is the active subset being used in the current reasoning step. Different storage mechanisms serve different memory types. The LLM's context window provides short-term working memory. Vector databases like ChromaDB enable semantic long-term memory through embedding and retrieval. Structured databases like PostgreSQL store episodic records. Knowledge graphs capture entity relationships. Memory retrieval is as important as memory storage. An agent with a million stored facts is only useful if it can efficiently retrieve the right facts for each situation. Semantic search, graph traversal, and recency-weighted retrieval are common strategies for surfacing relevant memories from large stores.
How GAIA Uses Agent Memory
GAIA maintains persistent memory across multiple storage layers. Short-term context is managed within LangGraph's state during each workflow. Long-term memory is stored in ChromaDB for semantic retrieval, PostgreSQL for structured records, and MongoDB for flexible document storage. GAIA remembers your communication preferences, past project context, key relationships, and workflow patterns, building a richer model of your work over time.
Related Concepts
Graph-Based Memory
Graph-based memory is an AI memory architecture that stores information as interconnected nodes and relationships, enabling rich contextual understanding and persistent knowledge across interactions.
Vector Database
A vector database is a database system designed to store, index, and query high-dimensional vector embeddings at scale, enabling fast similarity search across large collections of embedded data.
Knowledge Graph
A knowledge graph is a structured representation of information that organizes data as entities, their attributes, and the relationships between them, enabling machines to understand and reason about connected information.
Context Awareness
Context awareness in AI is the ability to understand the full situation surrounding a task or interaction, including who is involved, what has happened before, related projects, deadlines, and the user's preferences and patterns.
LangGraph
LangGraph is a framework for building stateful, multi-agent AI applications that supports complex workflows with cycles, branching, conditional logic, and persistent state management.
