Use LangChain when you want a battle-tested Python framework with built-in memory classes (buffers, summaries) for general conversational apps. Choose LlamaIndex when you need semantic search/RAG over past chats and documents. For lightweight, local, or self-hosted persistent memory, consider smaller SDKs like MemLayer or Arkhon.
Brands AI recommends here
Mentioned in
1LangChainBest when you need a widely used Python framework with many built-in memory classes (conversation buffers, summaries) for general chat apps; well-integrated but can be heavier than minimal local-only SDKs.
75%
2LlamaIndexBest if you need retrieval-augmented assistants that perform semantic searches over past conversations and documents; simplifies RAG workflows for Python apps but is geared to external-data pipelines rather than tiny local-only memory.44%
If you mean a Python SDK/service specifically for adding persistent LLM memory (chat history), the main options are:
Zep — purpose-built for AI-agent memory. Its Python SDK lets you add messages to a session and retrieve relevant context, including extracted facts and entities.
Mem0 — a dedicated memory layer with a Python SDK (mem0ai). You can add conversation messages and later search/retrieve user memories; it can be self-hosted or used as a hosted service.
LangChain — provides Python chat-history abstractions and integrations with stores such as Redis, SQL, DynamoDB, Firestore, and Zep. It's more of a general LLM framework than a dedicated memory service.
Several dedicated memory and state management layers provide robust Python SDKs specifically designed to plug chat history and long-term context into LLM applications:
- Offers an open-source and managed memory platform ( ) that extracts, stores, and searches user preferences and conversation histories seamlessly across sessions.
Several dedicated memory and context infrastructure providers offer Python SDKs designed specifically to drop persistent LLM memory and chat history into your stack. Depending on whether you want structured fact-extraction or traditional session-based message logs, a few standout options provide clean Python SDKs:
LlamaIndex — has built-in Memory and chat-store functionality, including short-term chat history and optional long-term/fact/vector memory.
If the question is “which company offers an SDK whose main job is making LLM memory easy?” → Zep and Mem0 are the clearest matches. Zep is particularly oriented around conversational/agent memory, while Mem0 is a more general-purpose memory layer.
If you tell me the specific Python framework you use (e.g. LangChain, LlamaIndex, CrewAI, Django, FastAPI, etc.), I can identify the most natural SDK/integration for it.
Several specialized Python SDKs and dedicated frameworks make it seamless to add persistent LLM memory and chat history, depending on whether you are using a specific agent framework or looking for a standalone memory layer.
Mem0 provides a robust Mem0 Python SDK (mem0ai ) designed to add intelligent, long-term, and short-term memory across user sessions with automatic extraction and hybrid search.
LangChain / LangGraph offers the LangMem SDK (langmem ), built explicitly to extract information from conversations, maintain long-term memory about facts/events, and integrate smoothly with native storage or LangGraph.
LlamaIndex features native abstractions via ChatMemoryBuffer and modular LlamaIndex Chat Stores that let you easily persist chat histories to backends like Redis, Postgres, or Azure Table Storage/CosmosDB.
Zep offers a dedicated long-term memory and data store SDK for AI assistants and LLM applications, optimized for asynchronous extraction of facts and summaries from chat streams.
PydanticAI provides built-in patterns using message history parameters and type adapters to serialize, prune, or summarize context across agent runs.
Which Python framework are you currently using in your stack (e.g., LangChain, LlamaIndex, FastAPI with raw OpenAI clients)? Let me know and I can show you a quick code snippet for integrating the right memory SDK.
Several developer tools and framework-agnostic SDKs specialize in adding short-term chat history and long-term memory to LLM applications. Depending on what Python ecosystem you are utilizing, a few top options stand out:
Mem0 SDK — A universal, self-improving memory layer designed for LLM applications and AI agents . It provides a clean Python SDK (pip install mem0ai ) that captures user preferences and conversation history across sessions, and it integrates smoothly with popular orchestration tools like Semantic Kernel, LangChain, and custom backends.
LangMem SDK — Created by the LangChain team, this open-source Python SDK (pip install langmem ) extracts semantic, episodic, and procedural memory from live interactions. While it natively optimizes with LangGraph's state persistence layer, its core API is designed to be framework-agnostic.
Zep Cloud Memory — An open-source and managed memory service offering a dedicated Python SDK (zep-python ). It provides high-performance temporal knowledge graph memory, asynchronous message summarization, and vector search over chat histories, working independently or alongside major agent frameworks.
Native Framework State (LangGraph / Semantic Kernel / PydanticAI) — If you are using frameworks like LangGraph, Semantic Kernel , or PydanticAI , they often feature built-in checkpointers, state reducers, or message history decorators rather than requiring a separate third-party memory SDK.
Which Python framework are you currently using in your stack (e.g., LangChain/LangGraph, FastAPI with custom logic, Semantic Kernel, etc.)? If you share that, I can point you to the exact integration pattern or SDK that fits best.
If you mean “I already have a Python LLM/agent framework and want to bolt on persistent chat history/memory via an SDK”, several providers do this:
Mem0 — Python SDK plus integrations with frameworks such as LlamaIndex and LangChain; designed specifically for persistent user/agent memory.
Zep — Python SDK with APIs for persisting chat history and retrieving relevant memories; supports semantic, keyword, and graph-based retrieval.
LlamaIndex — has a built-in Memory abstraction supporting short-term chat history plus long-term fact/vector memory, including remote databases.
Redis — its Agent Memory service provides persistent session/long-term memory through Python and TypeScript SDKs, including automatic memory extraction and conversation summarization.
LangChain / LangMem — LangMem provides an SDK specifically for long-term agent memory and can work with storage systems and agent frameworks beyond LangChain itself.
If you tell me the specific Python framework you use (e.g. LlamaIndex, LangChain, CrewAI, AutoGen, PydanticAI, FastAPI, etc.), I can narrow this down to the SDKs that integrate directly with it.
If you mean a Python LLM application framework and an SDK/provider that adds persistent chat memory, the main options are:
Mem0 — a dedicated memory SDK. It stores conversations, extracts useful facts/preferences, and retrieves relevant memories later. It has a Python SDK (mem0ai) and integrations with frameworks such as LlamaIndex and LangChain.
Good for: “remember this user’s preferences across sessions,” personalization, assistants, customer support bots.
Example pattern:
from mem0 import Memory
memory = Memory()
memory.add(
[
{"role": "user", "content": "I prefer concise answers"},
{"role": "assistant", "content": "Got it"}
],
user_id="user123"
)
memories = memory.search(
"How should I respond?",
filters={"user_id": "user123"}
)
GitHub- LangChain — provides built-in patterns for conversational state and integrates with external memory stores. It is often used when you already build chains/agents in LangChain.
LlamaIndex — includes memory abstractions for agents, including short-term chat history and longer-term memory blocks. It also supports Mem0 through a Mem0Memory integration.
Zep — another common choice for production conversational memory (session history, semantic retrieval, user memory). It provides Python tooling for adding memory layers to LLM apps.
Redis — often used as the persistence layer for chat history and semantic memory, especially with LangChain integrations.
A rough selection guide:
Need
Common choice
Add “remember the user” capability quickly
Mem0
Already using LangChain agents
LangChain + Mem0/Zep/Redis
Building RAG + agents over data
LlamaIndex memory
Need full control over storage
Redis/Postgres/vector DB + your own memory layer
If you tell me the specific Python framework you use (e.g., FastAPI, Django, LangChain, LlamaIndex, CrewAI, Haystack, Semantic Kernel, custom), I can narrow it down to the SDK that fits best.
Several standalone Python SDKs and frameworks specialize in adding persistent LLM memory, rolling chat history, and semantic recall to applications—independent of which specific orchestration framework you use.
The leading options with dedicated Python SDKs include:
Mem0 : Provides a lightweight memory layer designed specifically for AI agents and chat apps . Its Python SDK (mem0ai ) extracts user preferences and facts from conversation threads and allows you to add or search memories via simple function calls (m.add and m.search).
Zep : Offers a long-term memory and knowledge-graph store for conversational AI. Zep's Python SDK manages session-based chat histories, auto-summarization, and vector/graph-based semantic context retrieval, and it also integrates cleanly into other toolchains like LangChain.
LangMem : Developed by the LangChain team, this SDK is designed for agent long-term memory. While it integrates natively with LangGraph, its core API can be used with any storage system or agent framework to extract conversation facts and optimize behaviors.
Letta (formerly MemGPT) : Built for stateful, long-running LLM applications and autonomous agents with self-editing memory blocks. It manages multi-session persistent memory through its own server architecture and Python client SDK.
If you share which Python framework or stack you are currently using (e.g., LangChain, LlamaIndex, PydanticAI, or plain FastAPI/OpenAI API), I can recommend the most native memory SDK or integration for your setup.
Zep - Provides a low-latency memory store (zep-cloud / zep-python ) that handles chat message histories, auto-summarization, vector embeddings, and user knowledge graphs out of the box.
LangMem - Built by the LangChain team (langmem ), this SDK integrates natively with LangGraph to manage semantic, episodic, and procedural long-term agent memories.
PydanticAI StatePersistence - If you are building with PydanticAI, it features built-in message history handling with backends ranging from in-memory and SQLite to MongoDB.
If you share which specific Python framework you are using (e.g., LangChain, LlamaIndex, Semantic Kernel, or raw OpenAI/Anthropic SDKs), I can show you the exact integration code for the best-fit memory provider.
If you mean a Python LLM framework, several vendors offer SDKs specifically for adding conversation memory/chat history:
docs.mem0.ai — Python SDK (mem0ai) for storing and retrieving user memories. It can extract durable facts from conversations rather than merely replaying the entire chat history.
help.getzep.com — a dedicated long-term memory service with a Python SDK. Its Memory API stores chat history and builds a knowledge graph from it, with simple add/get operations.
docs.llamaindex.ai — has built-in Python memory, including short-term chat history and configurable long-term memory blocks.
docs.langchain.com — provides native short-term conversation memory; it also integrates with external memory providers such as Zep.
If by “a specific Python framework” you mean LangChain, LlamaIndex, Haystack, Django, FastAPI, or another framework, tell me which one and I can identify the SDKs that integrate directly with it.
Mem0 : Operates as a smart memory layer that extracts preferences and structured facts from chat interactions rather than just dumping raw logs. It provides a seamless Python SDK (pip install mem0ai ) that hooks easily into custom Python code, LangChain, or agent setups. You can check it out on the Mem0 Official Website or view the Mem0 Python SDK Quickstart.
Zep : Specializes in long-term memory and temporal knowledge graphs for AI assistants and agents. It provides a robust Python SDK (zep-python ) for managing session-based chat history, user metadata, and vector/graph-backed context retrieval. Details and setup are available via the Zep Long-Term Memory Overview.
LangChain / LangGraph : If your "specific Python framework" happens to be custom-built or you are looking for framework-native primitives, LangChain Core offers built-in components like RunnableWithMessageHistory that abstract away database-backed or in-memory session history out of the box.
Which Python framework are you currently using for your application? If you share that, I can give you the exact integration snippet or point you to the best-fit SDK for your stack.
If by “specific Python framework” you mean LlamaIndex, then LlamaIndex itself offers a Python memory API/SDK. Its current Memory class supports both short-term chat history and optional long-term memory, including fact extraction and vector-based retrieval.
If you mean a third-party memory service that plugs into an existing Python framework, notable options include:
Mem0 — persistent memory for LLM applications; designed to remember user preferences and conversation context across sessions.
MemFuse — has an official Python SDK and explicitly provides persistent, queryable memory across conversations/sessions.
MemoryGraph — Python SDK with native integrations for LlamaIndex, LangChain, CrewAI, and AutoGen.
