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.
理解する 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.
GAIAの活用方法 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.
関連概念
グラフベースメモリ
グラフベースメモリは、情報と関係を相互接続されたノードと関係として保存し、リッチなコンテキスト理解と相互作用全体での永続的な知識を可能にするAIメモリアーキテクチャです。
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.
ナレッジグラフ
ナレッジグラフは、データとエンティティ、その属性、およびそれらの間の関係を整理する情報の構造化表現であり、機械が接続された情報を理解し、推論できるようにします。
コンテキスト認識
AIにおけるコンテキスト認識とは、関係者、先行する出来事、関連プロジェクト、期日、ユーザーの好みやパターンを含む、タスクまたは相互作用を取り巻く状況全体を理解する能力です。
LangGraph
LangGraphは、サイクル、分岐、条件付きロジック、永続的な状態管理を備えた複雑なワークフローをサポートする、ステートフルなマルチエージェントAIアプリケーションを構築するためのフレームワークです。
