langchain-memory
LangChain memory integration including ConversationBufferMemory, ConversationSummaryMemory, and vector-based memory
npx skills add a5c-ai/babysitter --skill langchain-memory --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 1. **ConversationBufferMemory**: Stores full conversation history 2. **ConversationBufferWindowMemory**: Rolling window of recent messages 3. **ConversationSummaryMemory**: Summarizes older messages 4. **ConversationSummaryBufferMemory**: Hybrid approach 5. **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)
- Capabilities
- Target Processes
- Implementation Details
- Memory Types
- Configuration Options
- Dependencies
What does the langchain-memory skill do?
LangChain memory integration including ConversationBufferMemory, ConversationSummaryMemory, and vector-based memory
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill langchain-memory --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From a5c-ai/babysitter, a repository with 1,642 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.