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langchain-memory

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LangChain memory integration including ConversationBufferMemory, ConversationSummaryMemory, and vector-based memory

QUICK START

How to use this skill

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  1. Open your project in Codex.
  2. Copy the prompt below and paste it into your agent.
  3. Review the proposed files and risks before you approve installation.
Prompt to paste
I want to install this Agent Skill for this project in Codex.

Source SKILL.md: https://github.com/a5c-ai/babysitter/blob/HEAD/library/specializations/ai-agents-conversational/skills/langchain-memory/SKILL.md

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First, summarize what it does, its dependencies, license status if identifiable, and any risks. Show the exact files you propose to add under .agents/skills/langchain-memory/. Do not write files or run scripts until I approve.

After I approve, install the complete skill folder, including required referenced files, into that project location. Verify it is discoverable, then tell me its actual invocation name and how to use it. Do not claim it is installed until you have verified it.

Copying this prompt does not install or run the skill. Review third-party files before use. Codex skill guide

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)