LangGraph vs
Microsoft Agent Framework

Open and closed model rankings with benchmarks, context windows, modalities, and live API prices.

LangGraph models agent workflows strictly as directed graphs. You define nodes (which represent Python or TypeScript functions, agent logic, or tool executions) and edges (which represent conditional control flow or deterministic transitions). State is stored in a centralized, mutable state schema passed from node to node. When state changes, LangGraph writes an update through a reducer function, enabling full cyclical loops, branches, and arbitrary joins.

Microsoft Agent Framework unifies high-level conversational agent teams with structured workflow graphs. It formalizes common orchestration patterns into dedicated primitives: sequential runs, concurrent fan-out, direct handoffs, and group chat. Underneath these patterns sits a graph-based workflow engine with typed inputs and outputs. Developers working in C# receive idiomatic asynchronous task patterns and dependency injection, while Python developers get an API designed to match the same conceptual patterns.

LangGraph treats multi-agent orchestration as a graph wiring exercise. You construct supervisor agents, peer-to-peer handoffs, or hierarchical networks by routing edges based on agent outputs. Tool execution is modeled as standard graph nodes. For memory, LangGraph separates short-term working state (preserved in the graph's thread context) from long-term memory (persisted across independent threads via custom stores).

Microsoft Agent Framework includes built-in multi-agent primitives out of the box, building on the multi-agent conversational patterns popularized by AutoGen. It natively supports handoff workflows and group orchestrations without requiring you to manually wire complex conditional routing tables. Tool calling uses standard Model Context Protocol (MCP) conventions and function calling. Memory and conversational context are managed through built-in session state handlers and native middleware components.

Both frameworks prioritize durability, but they implement it differently.

LangGraph uses explicit checkpointers, such as MemorySaver for local testing and SQLite or PostgreSQL for production systems. Every step in the graph writes a state checkpoint to disk. This architecture makes human-in-the-loop workflows dependable: you can trigger an interrupt mid-execution, pause indefinitely, inspect the payload, edit state, and resume. You can also rewind graph execution back to any previous checkpoint to replay failed tool executions.

Microsoft Agent Framework provides workflow checkpoints, state persistence, and time-travel debugging capabilities. For observability, it relies on standard OpenTelemetry instrumentation across both Python and .NET, along with a dedicated local DevUI for inspecting runs. In production, its operational safety features (like prompt injection defenses, PII detection, and task-adherence checks) activate when deployed inside Azure AI Foundry.

At the time of writing, LangGraph holds a clear lead in community size and package downloads. With 39,709 GitHub stars, 283 contributors, and over 43 million monthly PyPI downloads, it is supported by a large ecosystem of community tutorials, enterprise integrations, and pre-built architectures.

Microsoft Agent Framework has 13,824 GitHub stars, 268 contributors, and 166,265 monthly PyPI downloads at the time of writing. While newer to market, it benefits from Microsoft's enterprise reach and the merged communities of AutoGen and Semantic Kernel. Its ecosystem is rapidly expanding, particularly among enterprise .NET engineering groups that were previously excluded from Python-first AI agent tooling.

Both projects are open source under the permissive MIT license, meaning both can be self-hosted in any environment without licensing fees.

For managed hosting, LangGraph offers LangGraph Platform, a purpose-built deployment layer providing managed task queues, state persistence, and operational APIs. It can be run on LangChain's managed cloud or deployed as a self-hosted instance inside your own VPC.

Microsoft Agent Framework is designed to integrate with Azure AI Foundry. It provides one-command deployment to Foundry Hosted Agents, giving teams managed infrastructure, enterprise security controls, and integration with Azure Functions for durable serverless execution. While you can self-host the open-source engine anywhere in Docker containers, teams running on AWS or Google Cloud must assemble their own deployment harnesses and security wrappers.

Choose LangGraph if:

• Your primary programming languages are Python or TypeScript.
• You want complete architectural control to build custom, non-linear workflows with complex conditional loops and joins.
• You require a cloud-agnostic solution that avoids binding your operational infrastructure to Azure.
• You want an established framework backed by massive download volumes and extensive public troubleshooting resources.
• You need fine-grained human-in-the-loop controls to inspect and alter internal state before continuing a run.

Choose Microsoft Agent Framework if:

• You develop production systems in C#/.NET or require full API parity between Python and .NET teams.
• Your organization standardizes on Azure infrastructure and plans to deploy agents using Azure AI Foundry.
• You are modernizing existing applications built on AutoGen or Semantic Kernel using Microsoft's official migration guides.
• You prefer high-level orchestration abstractions (like built-in handoffs and group chats) over manually wiring low-level graph edges.
• You want native OpenTelemetry metrics and local DevUI tooling integrated into your development workflow.

Migrating between the two engines requires rethinking how state and control flow are structured.

Moving from LangGraph to Microsoft Agent Framework is easiest when refactoring multi-agent networks. You can replace custom LangGraph router nodes with built-in primitives like handoffs and agent groups. However, you will need to map LangGraph checkpointer schemas to Microsoft's session and workflow persistence APIs. Teams migrating from AutoGen or Semantic Kernel will find official migration paths provided by Microsoft.

Moving from Microsoft Agent Framework to LangGraph requires expressing agent coordination as an explicit directed graph. You must convert high-level agent teams into individual graph nodes connected by conditional edges. While this shift adds initial boilerplate, it provides absolute control over how every state transition and retry executes in your application.