This is the conceptual video for the LangMem SDK launch. For "How-to" guides on particular memory types, check out the following:
Semantic memory: • Build Agents that Never Forget: LangMem Se...
Procedural memory: • Build Self-Improving Agents: LangMem Proce...
This conceptual guide introduces three memory types useful for developing adaptive LLM agents. Understanding how to implement semantic, procedural, and episodic memory enables more precise control over agent knowledge, behavior, and learning capabilities.
Semantic memory stores domain knowledge and contextual information, implemented either as searchable collections or compressed profiles. Collections enable vector search across unbounded datasets, while profiles optimize rapid context retrieval for user-facing applications.
Procedural memory handles behavioral adaptation through dynamic prompt optimization. This eliminates manual prompt engineering by automatically updating behavior based on interaction patterns and feedback signals.
Episodic memory bridges these systems by capturing successful interaction patterns. These patterns form a searchable database of proven solutions that inform future agent responses.
The LangMem SDK emphasizes domain-specific implementation over generalized solutions. Each memory type maps to concrete engineering requirements, enabling targeted development of agent capabilities.
Documentation: https://langchain-ai.github.io/langmem/
Conceptual guide: https://langchain-ai.github.io/langme...
Chapters:
[0:00] Memory architectures overview
[0:24] Implementation principles
[0:51] Memory types
[2:03] Semantic memory ("What")
[4:48] Procedural memory ("How")
[6:09] Episodic memory
[6:45] Conclusion
#LangGraph #AgentDevelopment
