langchain-memory
LangChain memory integration including ConversationBufferMemory, ConversationSummaryMemory, and vector-based memory
Best use case
langchain-memory is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
LangChain memory integration including ConversationBufferMemory, ConversationSummaryMemory, and vector-based memory
Teams using langchain-memory should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/langchain-memory/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How langchain-memory Compares
| Feature / Agent | langchain-memory | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
LangChain memory integration including ConversationBufferMemory, ConversationSummaryMemory, and vector-based memory
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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)
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