Langchain memory
Skill a5c-ai/babysitter/library/specializations/ai-agents-conversational/skills/langchain-memory
Babysitter enforces obedience on agentic workforces and enables them to manage extremely complex tasks and workflows through deterministic, hallucination-free self-orchestration
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LangChain memory integration including ConversationBufferMemory, ConversationSummaryMemory, and vector-based memory
SKILL.md
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LangChain Memory Skill
Capabilities
- Implement various LangChain memory types
- Configure ConversationBufferMemory for short-term recall
- Set up ConversationSummaryMemory for long conversations
- Integrate vector-based memory for semantic search
- Design memory retrieval strategies
- Handle memory persistence and serialization
Target Processes
- conversational-memory-system
- chatbot-design-implementation
Implementation Details
Memory Types
- ConversationBufferMemory: Stores full conversation history
- ConversationBufferWindowMemory: Rolling window of recent messages
- ConversationSummaryMemory: Summarizes older messages
- ConversationSummaryBufferMemory: Hybrid approach
- VectorStoreRetrieverMemory: Semantic similarity-based retrieval
Configuration Options
- Memory key naming conventions
- Return message format (string vs messages)
- Summary LLM selection
- Vector store backend selection
- Token limits and window sizes
Dependencies
- langchain
- langchain-community
- Vector store client (optional)