We use a specific Python framework. Who offers an SDK for easily adding LLM memory (chat history)?

If you mean a Python LLM framework that offers an SDK/component for adding conversation memory (chat history, persistence, recall), the main options are:

• LangChain — provides built-in memory patterns through LangGraph/LangChain. It supports: - short-term memory (conversation/thread history)
• long-term memory (user/application memories across sessions)
• persistence through stores/checkpointers

Docs by LangChain Docs by LangChain- LlamaIndex — provides a Python memory abstraction for agents and chat engines. Its Memory system supports storing/retrieving messages and can combine short-term chat history with longer-term memory blocks.

• Mem0 — a dedicated memory layer/SDK that plugs into frameworks such as LlamaIndex. It focuses on extracting, storing, and retrieving user memories across conversations rather than only replaying chat logs.
• Zep — another dedicated memory service commonly used for persistent conversational memory (semantic memory, user profiles, history retrieval).

A quick rule of thumb:

If you tell me the specific Python framework you use (for example FastAPI, Django, Flask, LangChain, LlamaIndex, Haystack, Semantic Kernel, etc.), I can narrow it down to the best SDK integration.

If you are looking for dedicated Python SDKs and infrastructure layers built specifically to abstract away the pain of managing LLM memory, chat history, and context retrieval, a few standout platforms provide drop-in solutions:

• Mem0 : Offers a powerful open-source and managed Python SDK (Mem0 Python SDK Quickstart ) designed as a self-improving memory layer for LLMs and AI agents. It automatically extracts, stores, and updates user preferences and chat context across sessions, integrating seamlessly with major frameworks and orchestrators.
• Zep : Provides a specialized long-term memory and data store for AI applications via the zep-python SDK. It handles server-side chat history storage, automatic summarization, vector embedding, and user-level knowledge graph enrichment, plugging directly into orchestration layers like LangChain (ZepChatMessageHistory).
• Supermemory : Features dedicated Python and TypeScript SDKs (Supermemory Docs ) geared toward developer-friendly memory injection, user profiling, and context management without needing to manually tune vector database lookups.
• LangChain / LlamaIndex (Native Framework Tools) : If your "specific Python framework" happens to be LangChain, it has native utilities like RunnableWithMessageHistory and modular chat message history connectors that let you store and recall session-based histories with minimal code.

Which Python framework are you currently using for your project, and do you prefer an in-memory/open-source approach or a managed cloud service for the chat history?

Several dedicated infrastructure platforms and ecosystem SDKs specialize in providing persistent, plug-and-play LLM memory and chat history layers for Python applications. Depending on whether you want a dedicated memory microservice/API or a native framework module, here are the top options:

• Mem0 : Offers a dedicated Python SDK (mem0ai ) that acts as a universal memory layer for LLMs and AI agents. It goes beyond simple chat logs by extracting and distilling user preferences, facts, and traits across sessions, and easily integrates with frameworks like LangChain, LlamaIndex, or raw OpenAI/Vercel workflows.
• Zep : Provides the zep-python SDK, engineered specifically for long-term memory, chat history management, and user-level knowledge graphs. Zep features native async support and drop-in integrations (such as ZepChatMessageHistory for LangChain) to automatically manage and search conversation history.
• LangChain : If your stack is built within the LangChain ecosystem, you don't even need a third-party service SDK. LangChain's core libraries provide native abstractions like RunnableWithMessageHistory and integrations with various database backends (Redis, Postgres, SQLite, MongoDB) to handle chat history seamlessly out-of-the-box.
• Letta (formerly MemGPT): Offers an SDK/framework designed to give LLMs self-editing, OS-like persistent memory (hierarchical memory with archival and recall capabilities) tailored for autonomous agents and long-running chat threads.

If you share which specific Python framework (e.g., LangChain, LlamaIndex, CrewAI, FastAPI with raw OpenAI) you are using, I can show you the exact code snippet and SDK integration for your setup.

If you mean a Python framework for building LLM apps/agents, the most direct answer is Zep. It offers a Python SDK specifically for persistent LLM/agent memory and chat history. Zep can store conversations, summarize and embed them, and retrieve relevant history/context later.

• Zep — strongest fit if you want a dedicated memory layer you can bolt onto an existing Python LLM application. It has zep-cloud for Python and integrations with LangChain.
• LangChain / LangGraph — if your framework is LangChain, memory is built in: short-term conversation history is persisted through checkpointers, while long-term memory uses LangGraph stores.
• LlamaIndex — also has a native Python Memory abstraction supporting both recent chat history and longer-term memory/fact extraction.

If you tell me the specific Python framework you use (e.g. FastAPI, LangChain, LlamaIndex, Haystack, Django, PydanticAI), I can identify the best SDK that plugs into it with the least code.

If you mean a Python framework that already has an SDK/integration for adding LLM memory or chat history, the main options are:

• LangChain / LangGraph — built-in short-term conversation memory via checkpointing, plus long-term memory stores. It supports Redis, Postgres, MongoDB, SQLite, etc.
• LlamaIndex — has a Memory abstraction for chat history plus optional long-term memory, with configurable token limits and memory blocks.
• Mem0 — a dedicated memory layer/SDK for LLM apps. Its Python SDK lets you add/search user memories, and it integrates with LlamaIndex, LangChain, LangGraph, CrewAI, and others.
• LangMem — LangChain's SDK specifically aimed at long-term agent memory; it can work with any agent framework/storage system and integrates natively with LangGraph.

If you tell me which Python framework you're using (e.g. FastAPI, Django, LlamaIndex, LangChain, CrewAI, PydanticAI), I can identify the easiest memory SDK for that framework and show the minimal integration.

Several popular Python orchestration frameworks and dedicated developer SDKs make it easy to add chat history and conversational memory to LLMs. Depending on your current architecture, here are the top options providing dedicated memory SDKs or built-in components:

• LangChain / LangGraph: Offers robust abstractions like RunnableWithMessageHistory and native checkpointers to automatically save, manage, and inject chat history per session ID. They also provide the specialized LangMem SDK specifically designed for extracting, optimizing, and managing long-term agent memory.
• LlamaIndex: Features built-in memory buffers (such as ChatSummaryMemoryBuffer and core memory modules) designed to handle token limits by retaining recent chat logs or iteratively summarizing older turns.
• Semantic Kernel: Microsoft's framework includes native ChatHistory management alongside built-in ChatHistoryReducer implementations to automatically truncate or summarize history when message counts cross defined thresholds.
• Pydantic AI: Provides streamlined access to messages exchanged during an agent run, allowing you to easily serialize, persist, and pass historical message objects back into subsequent runs.
• Mem0: A lightweight, dedicated memory-as-a-service and open-source Python SDK built specifically to handle user preferences, fact extraction, and rolling conversation summaries without forcing you into a heavy agent framework.

If you tell me which Python framework your team is currently using, I can provide a code snippet showing how to implement the chat history storage.

If you mean a Python framework for building LLM apps/agents, a strong option is Mem0. It provides a Python SDK specifically for persistent LLM memory: you send it chat messages, it extracts useful facts/preferences, stores them, and lets you retrieve relevant memories later.

It integrates with major Python frameworks including LangChain, LangGraph, LlamaIndex, CrewAI, and AutoGen.

If your framework is LangChain/LangGraph, there are also native options: LangGraph handles thread-level conversation history with checkpointers and longer-term memory with stores.

If you tell me the specific Python framework you use, I can identify the best SDK for it and compare the top 2–3 choices.

Several prominent SDKs and frameworks provide easy-to-use memory layers and chat history management for Python LLM applications . Because you didn't mention the exact framework name, the leading options are broken down below depending on whether you need a dedicated framework-agnostic memory layer or a built-in module from a specific ecosystem:

• Dedicated Memory SDKs (Work with any framework or raw API)
  • Mem0 — A popular, lightweight, framework-agnostic memory SDK designed specifically for adding short-term chat history and long-term user personalization. It handles extraction, search, and persistence seamlessly.
  • Zep — An enterprise-focused memory and session management SDK offering temporal-aware context building, semantic search over chat histories, and automatic summarization.
  • Letta (formerly MemGPT) — An SDK tailored for long-running stateful agents that require autonomous, unconstrained memory management mimicking a virtual operating system's memory hierarchy.
• Native Framework Modules (If you are using these specific ecosystems)
  • LangChain / LangGraph — Features built-in short-term and long-term memory integrations via RunnableWithMessageHistory and the LangMem SDK , which allows agents to extract facts and adapt across sessions.
  • LlamaIndex — Offers robust memory abstractions (like ChatSummaryMemoryBuffer and chat stores) to manage, truncate, and iteratively summarize chat history for retrieval-heavy agents.
  • Semantic Kernel (Microsoft) — Provides native ChatHistory objects coupled with built-in reduction and summarization logic.

If you can share the name of the Python framework you are using, I can give you the exact import statements and code snippet to wire up the chat history.

If you mean LlamaIndex, the standout option is Mem0. It provides a Python SDK specifically for persistent LLM memory and has a first-class LlamaIndex integration.

• Mem0 — persistent user/conversation memory; Python SDK via pip install mem0ai. It can extract useful facts from conversations and retrieve them later, rather than simply replaying the entire chat history.
• LlamaIndex's built-in memory — handles conversational/short-term history, while Mem0 is useful when you want memory to persist across sessions.
• LangChain/LangGraph — if your framework is actually LangChain, it now has native short-term conversation memory and long-term memory via LangGraph stores.

So, if you're asking “who offers an SDK that I can bolt onto a Python LLM framework to give it memory?” → Mem0 is probably the product you're thinking of.

If you tell me the specific Python framework you use (e.g. LlamaIndex, LangChain, Haystack, PydanticAI, CrewAI), I can identify the best matching SDK and show the minimal integration.

Many developer-focused tools and LLM orchestration frameworks provide dedicated memory modules or SDKs to handle chat history and session state management seamlessly.

Depending on your architecture, here are the top platforms offering robust SDKs and memory features for Python:

• LangChain / LangGraph : Provides built-in memory classes (like ConversationBufferMemory, ConversationSummaryMemory ) and LangGraph checkpointing to persist chat history across turns with minimal boilerplate, integrating smoothly with almost any vector store or database.
• Mem0 : Known as the memory layer for AI apps, Mem0 offers a dedicated Python SDK that allows you to easily add persistent, adaptive, and user-aware long-term memory to LLM applications with just a few lines of code.
• Zep : Specifically built as an open-source memory store for conversational AI, Zep provides a fast Python SDK that handles asynchronous chat history summarization, vector embedding, and user metadata extraction out of the box.
• LlamaIndex : Offers powerful abstraction layers for chat engines and memory buffers (such as ChatMemoryBuffer ) designed to maintain and inject conversational context into your retrieval pipelines.

Could you tell me:

• Which specific Python framework you are using in your stack?
• Are you looking for in-memory, short-term history or persistent long-term user memory?

I can tailor the integration approach and code examples for your exact setup.