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

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.

Several dedicated Python SDKs and frameworks make it straightforward to add conversational memory and chat history management to LLM applications.

• LangChain / LangMem : Offers native abstractions like RunnableWithMessageHistory for short-term chat history management, alongside the open-source LangMem SDK designed specifically to provide agents with advanced long-term (semantic, episodic, and procedural) memory.
• LlamaIndex : Provides robust built-in memory modules such as ChatMemoryBuffer and persistent chat store adapters to seamlessly attach token-limited or summarized history to chat engines and agents.
• Zep : Provides an enterprise-ready Python SDK (zep-python ) with asynchronous auto-summarization, vector search over past messages, and user-level knowledge graphs built directly from ongoing chat history.
• Mem0 : Offers a lightweight, plug-and-play memory SDK tailored for adding personalized, cross-session user memory and continuous fact extraction to LLM workflows.
• Letta (formerly MemGPT): Supplies an SDK geared towards long-running agents that require an OS-like virtual context management system for unlimited memory persistence.

If you share which specific Python framework or orchestrator your team is currently using, I can provide the exact code snippet and SDK setup for adding chat history.

If you're looking for an SDK that can be dropped into a Python LLM application to provide persistent chat history and long-term memory, there are several good options. The best choice depends on whether you want a managed service, self-hosting, or a framework-specific integration.

If you already use a framework, there are also framework-native options:

• Microsoft Agent Framework includes chat history providers (in-memory, Redis, Cosmos DB) plus memory providers such as chat history, Mem0, and Neo4j.
• LangChain itself has message history abstractions, but many teams pair it with an external memory service like Memori, getmem.ai, or Zep for persistent cross-session memory.

Which one I'd choose

• Simplest drop-in SDK: Memori. It wraps common Python LLM clients and automatically captures, stores, and retrieves memories with minimal code changes.
• If you want a hosted memory API: Mnexium or getmem.ai. Both are designed to sit alongside existing OpenAI/Anthropic code rather than replacing your framework.
• If you're building autonomous agents: Letta's AI Memory SDK provides higher-level memory constructs (memory blocks, summaries, policies) rather than just replaying chat history.

If you tell me which Python framework you're using (for example, LangChain, LlamaIndex, Pydantic AI, Agno, OpenAI Agents SDK, CrewAI, or something else), I can narrow the recommendations to SDKs that integrate cleanly with that framework.

If you're looking for an SDK that adds persistent chat history / long-term memory to an existing Python LLM application, there are several strong options. They differ in how opinionated they are and whether they focus on simple chat history or richer semantic memory.

If you're already using a Python agent framework, many frameworks also have their own memory integrations:

• LangChain / LangGraph – native chat history abstractions plus integrations with external memory providers.
• Pydantic AI – supported by Memori.
• Agno – supported by Memori.
• Microsoft Agent Framework – includes pluggable chat history providers and memory providers.

The choice depends on what you mean by "memory":

• Conversation history only → LangChain history stores, Microsoft Agent Framework providers.
• Long-term user preferences and facts → Letta Memory SDK or Memori.
• Semantic memory with search → SharedMemory AI, MemoryStack, OctaMem.
• Open-source/local deployment → mem7.

Which Python framework are you using (for example LangChain, LangGraph, Pydantic AI, LlamaIndex, FastAPI, or something else)? I can recommend the SDK that integrates most naturally with it.

Several Python orchestration and memory frameworks provide dedicated SDKs or native integrations to easily add chat history and LLM memory.

• LangChain / LangGraph: Offers native state management and message history utilities. Through RunnableWithMessageHistory , you can easily plug in session-based history. For long-term or advanced semantic memory, they provide the LangMem SDK , which integrates seamlessly with LangGraph's persistent state checkpointers.
• Zep: Provides robust Python SDKs (zep-python and framework integrations) that handle long-term conversation storage, auto-summarization, vector search, and temporal knowledge graphs. Zep provides drop-in history classes for frameworks like LangChain and LangGraph.
• Mem0: Offers a dedicated Python SDK designed to provide personalized, adaptive memory layers for AI chatbots and agents, automatically extracting, updating, and recalling relevant historical context across sessions.
• LlamaIndex: Features built-in memory abstractions and chat buffers (such as ChatSummaryMemoryBuffer ) that handle token limits and iterative summarization of older chat history out of the box.
• Letta (formerly MemGPT): Offers an SDK and framework designed to give LLMs self-editing long-term memory by treating memory through an operating-system-like abstraction (managing blocks of core and archival memory).

If you let me know which specific Python framework your team is using (e.g., LangChain, LlamaIndex, FastAPI, or a custom setup), I can give you the exact SDK or integration code you need!