“memory”
Memory-Augmented Prompts: Injecting Recall into Every Agent Turn
Transform static prompts into dynamic, memory-aware prompts that automatically inject relevant past context. Build agents that remember without being told to.
Short-Term vs. Long-Term Memory: Architecting Multi-Horizon Recall
Design agent memory systems that span multiple time horizons. Balance fast short-term context with durable long-term knowledge for effective agent recall.
Memory Pruning: Forgetting, Expiry, and Context Budgets for Agents
Agents that never forget eventually drown in irrelevant context. Learn strategies for pruning memory — TTL-based expiry, relevance decay, and budget-aware selection.
Choosing an Agent Memory Store: Vector DBs, Key-Value, and Graphs
A practical comparison of memory store backends for AI agents. Learn when to use vector databases, key-value stores, and graph databases for different memory patterns.
Conversation Summaries as Memory: Compressing Sessions for Context
Learn how to compress long conversations into concise summaries that preserve key information, reducing token usage while maintaining agent context.
Semantic Recall: Making Agent Memory Searchable with Embeddings
Transform agent memory from keyword-matching to semantic understanding. Use embeddings to let agents find relevant past experiences by meaning, not just keywords.
Long-Term Knowledge Stores: Moving Beyond Rolling Chat History
Stop treating agent memory as a sliding window. Learn how to build long-term knowledge stores that persist insights, facts, and context across thousands of interactions.
Episodic Memory in Agents: Recording Sessions as Replayable Experiences
Learn how episodic memory enables AI agents to record, store, and replay entire sessions — turning one-off interactions into reusable, learnable experiences.
Episodic Memory in Agents: Recording Sessions as Replayable Experiences
Learn how episodic memory enables AI agents to record sessions as replayable experiences, with implementation patterns using LlamaIndex and production architectures.
Session Persistence and Memory for Long-Running Agent Workflows
Give your agents memory that survives restarts. Learn session backends, context trimming and summarization, and compaction with the OpenAI Agents SDK.
The "Long-Horizon" Task: Keeping Gemini 3 Focused Over Months-Long Projects
Long-horizon tasks outlast a single context window. Learn to beat context rot with execution-state memory, agent-managed context tools, file-centric state, and proactive memory intervention.
The "Continuous-Learning" Agent: Updating Gemini 3 Knowledge via Live Feeds
An LLMs knowledge freezes at training time. Learn the continuous-learning agent pattern: frozen base + live vector store, temporal scoring, EWC-guarded micro-adaptation, and indexes that learn from validated queries.