How To Give Your Agent Memory
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Implement the memory loop with LangSmith Observability, Engine, and Context Hub.
Implement the memory loop with LangSmith Observability, Engine, and Context Hub.
Summary
Memory is durable context that an agent can retrieve across runs to guide its behavior, including facts, preferences, past interactions, instructions, skills, and learned patterns. The memory loop consists of capturing traces, analyzing traces, and updating memory. Short‑term (working) memory holds context during a run, while long‑term memory persists beyond the run. Semantic, episodic, and procedural memory taxonomies help classify what the agent knows, has experienced, and how it should behave. LangSmith Observability captures traces, LangSmith Engine analyzes them for recurring issues, and LangSmith Context Hub stores durable context. Updates must be refreshed in runtime to avoid stale context. Evaluations protect important behavior, and only a small subset of trace data becomes durable context. The process ensures that agents learn from mistakes and improve over time.
Implementing this loop with LangSmith Observability, Engine, and Context Hub allows developers to automatically capture, analyze, and update agent memory, turning trace evidence into actionable improvements.
Key changes
- Captures traces with LangSmith Observability
- Analyzes traces with LangSmith Engine for recurring issues
- Updates memory in LangSmith Context Hub
- Distinguishes short‑term and long‑term memory
- Uses semantic, episodic, procedural memory taxonomy
- Refreshes runtime context to avoid staleness
- Protects behavior with evaluations
- Only a subset of trace data becomes durable context