Memory in Agentic Frameworks: LangChain, CrewAI, AutoGen, and What They're All Missing

April 2026

Every major agentic framework now ships some form of memory. LangChain has memory stores and checkpointers. CrewAI has cognitive memory. AutoGen defers to external integrations. But every one of these systems is scoped to the framework that built it. A developer using Claude Code in the morning and Cursor in the afternoon carries zero memory between them. A LangGraph agent cannot access CrewAI memories. The frameworks treat memory as a feature of the framework; the actual problem is memory as infrastructure that works across all of them.

Here is how each framework handles memory today, what it does well, and where it stops.

LangChain / LangGraph + LangMem

LangChain has invested more in memory than any other framework. LangGraph provides two persistence primitives: checkpointers for saving graph state at each step (enabling replay and human-in-the-loop), and stores for shared key-value data across threads and conversations. The LangMem SDK adds long-term memory on top: Memory Managers that automatically extract, consolidate, and update memories using configurable strategies (full replacement, patch-based updates, or insert-only).

This is genuinely capable. A LangGraph agent can accumulate user preferences, project facts, and procedural knowledge across sessions. The Memory Manager handles deduplication and conflict resolution. You can namespace memories by user, project, or any custom key. LangMem supports both semantic search and scoped retrieval from the store.

Where it stops: Memory is scoped to LangGraph deployments. If you build with LangGraph, you get access. If you use Claude Code, Cursor, GitHub Copilot, or any tool outside the LangChain ecosystem, you do not. There is no MCP server, no standalone API, and no CLI for accessing LangMem from external tools. The memory lives inside LangGraph's runtime.

CrewAI

CrewAI implements a cognitive memory architecture with three layers: short-term memory (current crew execution), long-term memory (persisted across executions using SQLite), and entity memory (structured facts about people, organizations, and concepts). All three use LLM-driven encoding — the framework calls an LLM to process and store memories, not just raw embeddings.

Agents within a crew share memory automatically. A researcher agent's findings are available to a writer agent in the same crew. Entity memory extracts structured relationships and attributes. The local storage backend uses LanceDB for vector search and SQLite for structured data, which means no external infrastructure is required to get started.

Where it stops: Memory is tied to the CrewAI runtime. There is no API endpoint, no MCP server, and no way to query CrewAI's memory store from an external tool. If you build a crew that accumulates valuable project knowledge, that knowledge is accessible only from within CrewAI. Storage is local by default — there is no built-in sync, sharing, or team access.

AutoGen / Microsoft Agent Framework

AutoGen takes a different approach: minimal built-in memory, maximum extensibility. The framework provides a plugin architecture where memory is handled by external integrations. Common choices include Mem0 for managed memory, ChromaDB for local vector search, and Redis for key-value state.

Microsoft's Agent Framework (the successor direction for enterprise AutoGen) adds Azure Cosmos DB, Azure AI Search, and Azure Blob Storage as memory backends. This gives you production-grade persistence and search — if you commit to Azure infrastructure.

Where it stops: Out of the box, AutoGen has no persistent memory. Every integration requires setup: configuring a vector database, writing retrieval logic, managing storage infrastructure. The Azure-backed Agent Framework path provides full-featured memory but introduces Azure service dependencies. There is no framework-level abstraction for "remember this across sessions" — you build it yourself from the available components.

Semantic Kernel

Semantic Kernel provides a vector store abstraction layer that normalizes access across multiple backends (Azure AI Search, Qdrant, Pinecone, Redis, and others). This is useful plumbing. You define a data model, register a vector store, and Semantic Kernel handles serialization, embedding, and retrieval across whichever backend you choose.

Where it stops: Semantic Kernel is a vector store connector, not a memory system. It gives you a consistent interface for storing and retrieving embeddings. It does not extract entities, track facts, manage memory lifecycle, deduplicate knowledge, or handle any of the higher-level concerns that make a memory system useful for agents. The developer builds everything above the storage layer. Much of the memory-related API surface is still in alpha or preview as of early 2026.

LlamaIndex

LlamaIndex includes a FactExtractionMemoryBlock that extracts structured facts from conversation history using an LLM. This is architecturally interesting — it moves beyond raw embedding storage to explicit knowledge extraction. Facts are stored as natural-language statements that can be retrieved and composed into agent context.

Where it stops: LlamaIndex's memory is designed for RAG pipelines. The FactExtractionMemoryBlock works within LlamaIndex's agent framework, but custom memory blocks are not serializable by default. There is no built-in persistence layer for long-running agent memory — you wire it to your own storage. The extraction is useful; the infrastructure around it is left to the developer.

The Comparison

| Capability | LangChain | CrewAI | AutoGen | Semantic Kernel | LlamaIndex | MemNexus | |---|---|---|---|---|---|---| | Cross-session persistence | Yes (LangGraph stores) | Yes (SQLite) | Via external integration | Developer-built | Developer-built | Yes | | Cross-project memory | Namespaced stores | No | No | No | No | Yes | | Cross-tool access | No (LangGraph only) | No (CrewAI only) | No (AutoGen only) | No | No | Yes (MCP, API, CLI, SDK) | | Knowledge graph / entities | No | Entity memory (local) | Via Mem0 integration | No | Fact extraction | Yes (auto-extracted) | | Automatic extraction | Yes (LangMem managers) | Yes (LLM-driven) | No | No | Yes (FactExtractionMemoryBlock) | Yes | | Team / shared memory | Namespaced stores | Within a crew | Via shared backend | Via shared backend | No | Yes | | No infrastructure required | Requires LangGraph | Yes (local SQLite/LanceDB) | Requires external DB | Requires vector DB | Requires external storage | Yes (managed service) |

The Gap

Every framework's memory is siloed within its own runtime. This is not a criticism of any individual framework — it reflects the natural design choice of building memory as a feature alongside agents, rather than as a standalone layer.

But developer workflows do not stay within a single framework. A typical day might involve Claude Code for implementation, Copilot for inline completions, Cursor for refactoring, and a LangGraph pipeline for a specific automation. Each tool starts with a blank memory. Architectural decisions made in one session do not carry to the next tool. Debugging history from Monday is unavailable on Tuesday. Knowledge that a team member captured in their CrewAI workflow is invisible to the rest of the team.

The consequence is concrete: developers re-explain context, agents re-discover solutions, and teams lose accumulated knowledge every time they cross a tool boundary. The frameworks have built good memory within their walls. Nothing connects those walls to each other.

Where MemNexus Fits

MemNexus is a dedicated memory layer that sits outside any single framework or tool. It is accessible via MCP (Model Context Protocol), REST API, CLI, and SDK. It works with Claude Code, Codex, Copilot, Cursor, and any framework or tool that can make HTTP calls. Memories are automatically processed through entity extraction, fact extraction, and knowledge graph construction. Cross-project scoping, team sharing, behavioral pattern detection, and semantic search are built in — not left to the developer to assemble. The memory persists across tools, projects, sessions, and team members because it is infrastructure, not a feature of any single agent framework.

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