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Memory & Context

Memory is how agents store, retrieve, and maintain information over time. Without memory, agents start every interaction from scratch. With it, they build on past experiences.

Why Memory Matters

The difference between a useful AI agent and an annoying one often comes down to memory. An agent that remembers your preferences, past decisions, and project context feels intelligent. One that asks the same questions every time feels broken.

More importantly, memory enables agents to learn. Not just "what did the user say last time," but "what patterns work? What approaches succeed? What context is relevant for similar tasks?"

Types of Memory

Short-term memory holds active information during the current task. It's fast, temporary, and automatically cleared when the task completes. Think of it as working memory—the variables and data you're currently using.

Long-term memory persists across sessions. It's slower but durable, storing user preferences, historical conversations, learned patterns, and project-specific information. This is how agents remember you between interactions.

Knowledge bases are structured, curated information about your domain. Unlike conversational memory (which is organic and grows naturally), knowledge bases are intentionally organized—like having a well-maintained wiki versus chat logs.

The Context Challenge

AI models have limited "attention spans" called context windows. A small model might handle a few thousand words. A large one might manage an entire codebase. But regardless of size, the window is finite.

This creates a fundamental challenge: what information should occupy that precious context space?

Lovelace addresses this through several strategies:

Hierarchical summarization - Recent information gets full detail, older information gets compressed summaries, distant information becomes high-level overviews.

Relevance filtering - Only include information relevant to the current task. An agent reviewing code doesn't need your lunch preferences.

Semantic search - Find related information using AI-powered search, not just keyword matching.

Incremental context - Add information progressively as needed rather than loading everything upfront.

Memory Architecture

System memory tools provide foundational operations—store, retrieve, list, and delete data. These are always available to agents regardless of workflow state.

For production deployments, the Memory Platform provides advanced capabilities: vector databases for semantic search, time-series data for historical analysis, graph databases for relationship tracking, and distributed storage across regions.

Memory Across Products

The CLI stores memory locally in file-based storage—good for development, portable, and under your control.

Studio provides visual memory management—browse stores, edit knowledge bases, view conversation histories.

Agents Cloud uses the Memory Platform—distributed, scalable, with automatic backup and replication.

Assistant remembers your preferences and learns from interactions, personalizing its help over time.

The Bigger Picture

Memory transforms agents from stateless tools into persistent assistants. But it also introduces responsibility: data retention policies, privacy concerns, security implications.

Lovelace addresses this through data classification, configurable retention policies, encryption, and audit logging. Memory is powerful, but it must be managed thoughtfully.

The goal isn't just agents that remember everything—it's agents that remember the right things, forget appropriately, and use memory to genuinely improve over time.

Related Concepts