Key Takeaways
- Three memory layers: short-term (conversation context), working (intermediate results during tasks), and long-term (persistent knowledge across sessions).
- Summary memory is the most practical buffer strategy: older messages are summarized by the LLM, maintaining context while staying within token limits.
- Vector databases enable semantic long-term memory: store embeddings of past interactions and retrieve relevant memories when similar situations arise.
- Memory management is essential: without decay, consolidation, and pruning, memory stores become noisy and degrade retrieval quality.
- Episodic memory (past experiences) enables agents to learn from mistakes and repeat successful strategies over time.
Memory Architecture: Three Layers
| Layer | Scope | Storage | Retention | Purpose |
|---|---|---|---|---|
| Short-term | Current session | LLM context window | Session only | Maintain conversation flow |
| Working | Current task | Structured JSON/state | Task duration | Track intermediate results |
| Long-term | Cross-session | Vector DB + metadata | Persistent | Learn and personalize |
Each layer serves a distinct purpose and requires different implementation. Short-term memory is managed by the LLM's context window. Working memory is structured state. Long-term memory requires vector databases and retrieval logic.
Conversation Buffer Strategies
| Strategy | How It Works | Pros | Cons | Best For |
|---|---|---|---|---|
| Full history | Include all messages | Complete context | Token limit exceeded quickly | Short conversations (<20 turns) |
| Sliding window | Last N messages only | Bounded token cost | Loses early context | High-volume simple interactions |
| Summary memory | LLM summarizes older messages | Balanced context/cost | Summary loses detail | Most production agents |
| Token buffer | Trim to fit token limit | Predictable cost | Arbitrary cutoff point | Cost-constrained applications |
| Entity memory | Track entities mentioned | Structured recall | Complex implementation | CRM-like applications |
Summary memory is the best default. Keep the last 5-10 messages verbatim and summarize everything older. This preserves recent context while maintaining awareness of the full conversation. The summary step adds ~$0.001-0.005 per summarization.
Long-Term Memory with Vector Databases
Long-term memory enables agents to learn from past interactions and provide personalized experiences:
- After each significant interaction, extract key information: facts learned, decisions made, user preferences, outcomes
- Generate embeddings of the extracted information using an embedding model (OpenAI, Cohere Embed)
- Store in a vector database (Pinecone, Weaviate, Chroma, Qdrant) with metadata (timestamp, context, confidence)
- At the start of each new interaction, retrieve semantically similar memories and inject them into the agent's context
- Implement memory management: decay older memories, consolidate related ones, prune outdated information
Semantic memory stores facts and knowledge ('User prefers email communication'). Episodic memory stores experiences ('Last time we recommended product X, the user was dissatisfied'). Both types improve agent intelligence, but episodic memory enables learning from mistakes.
Memory Management and Maintenance
- Decay: reduce the priority/weight of older memories over time. Recent interactions are more relevant than year-old ones.
- Consolidation: merge related memories into unified entries. Five separate mentions of a user preference become one strong memory.
- Pruning: remove outdated, contradicted, or low-confidence memories. Stale information is worse than no information.
- Relevance scoring: combine semantic similarity with recency, frequency, and importance when retrieving memories.
- Privacy compliance: implement memory deletion for user data removal requests (GDPR right to be forgotten).
Frequently Asked Questions
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