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langchain-memory skill

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/plugins/babysitter/skills/babysit/process/specializations/ai-agents-conversational/skills/langchain-memory

This skill helps you implement and manage LangChain memory types for chatbots, enabling short-term recall, long-term context, and semantic retrieval.

npx playbooks add skill a5c-ai/babysitter --skill langchain-memory

Review the files below or copy the command above to add this skill to your agents.

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SKILL.md

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---
name: langchain-memory
description: LangChain memory integration including ConversationBufferMemory, ConversationSummaryMemory, and vector-based memory
allowed-tools:
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  - Grep
---

# 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)

Overview

This skill implements LangChain memory patterns to give agents short-term and long-term conversational recall and vector-based semantic retrieval. I provide ready-to-configure ConversationBufferMemory, ConversationSummaryMemory, and vector-store retriever memory types for chatbots and agent workflows. The focus is practical integration, persistence, and retrieval strategies for reliable conversational behavior.

How this skill works

I wire up multiple LangChain memory classes and expose configuration for keys, return formats, token/window limits, and summary LLM selection. ConversationBufferMemory and windowed buffers keep recent messages; summary memories compress older history; vector-based memory indexes embeddings for semantic lookup. I also handle serialization and persistence so memory survives restarts and supports deterministic orchestration.

When to use it

Building chatbots that need contextual recall across turns
Improving agent decisions by supplying condensed long-term context
Enabling semantic search over past interactions or documents
Managing token usage by summarizing or windowing conversation history
Persisting conversational state between sessions or across worker restarts

Best practices

Choose ConversationBufferMemory for full short-term history and windowed buffers when you need bounded context
Use ConversationSummaryMemory or hybrid buffers to reduce token consumption on long conversations
Select a summary LLM appropriate to your budget and fidelity requirements
Back vector-memory with a scalable vector store (e.g., FAISS, Pinecone) and tune similarity thresholds
Name memory keys clearly and standardize return formats (string vs messages) to simplify agent prompts

Example use cases

Customer support bot that keeps recent chat context but summarizes older exchanges to save tokens
Sales assistant that retrieves semantically similar past product discussions via vector memory
Agentic orchestration that resumes deterministic workflows by persisting memory state
Hybrid chatbot that keeps a short buffer plus summarized history for long-running conversations
Research assistant that indexes conversation notes for fast semantic lookup

FAQ

Which memory type should I pick for short sessions?

Use ConversationBufferMemory or ConversationBufferWindowMemory to preserve recent turns with minimal setup.

How do I reduce token usage for long chats?

Add ConversationSummaryMemory or a ConversationSummaryBufferMemory to compress older messages and limit the active token window.