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LangChain and Microsoft Agent Framework compared side by side on GitHub stars, downloads, language, license, capabilities, strengths and trade-offs.
TL;DR
Choose LangChain if you need cloud-agnostic prototyping, TypeScript or Python runtimes, and immediate access to the broadest ecosystem of third-party model and vector store integrations. Choose Microsoft Agent Framework if your engineering organization runs on Python and .NET or targets Azure AI Foundry for enterprise deployment and managed governance.
Composable building blocks for LLM apps — chains, agents, retrievers, and integrations.
Open framework for building agents and multi-agent workflows in Python and .NET.
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Comparing agent frameworks is the process of evaluating two or three competing libraries side by side on live community signals, technical capabilities, language, license, and trade-offs so a team can pick the one that fits its stack and roadmap.
Start with the constraints that are not negotiable: the language your team already ships in, the license your legal team will sign off on, and the deployment model you can support. A framework that fails any of those three is not really an option, no matter how popular it is.
Then look at live signals: GitHub stars and contributors show whether the project is gathering momentum, npm and PyPI downloads show whether teams are actually shipping with it, and last-commit date shows whether the maintainers are still around. Capability flags like multi-agent, streaming, tool use, human-in-the-loop, memory, and evaluations narrow the field to frameworks that match your job-to-be-done.
Finish by reading the strengths and trade-offs columns side by side. The smallest framework that covers your real requirements almost always beats the most popular one. Copy the share link once you have a comparison you trust so you can revisit it during planning.
Decisions about agent frameworks rarely happen in isolation. Pair this comparator with the directories and benchmarks that ground the rest of the stack.
Live data from GitHub and the package registries, refreshed every day.
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Choosing between LangChain and Microsoft Agent Framework comes down to ecosystem alignment and runtime requirements. LangChain is the industry default for composing LLM pipelines across varied model providers and vector stores. Microsoft Agent Framework represents the unified successor to AutoGen and Semantic Kernel, specifically engineered for enterprise workflows in Python and C#/.NET with direct integration into Azure AI Foundry.
| Feature | LangChain | Microsoft Agent Framework |
|---|---|---|
| First Released | October 25, 2022 | October 1, 2025 |
| Primary Languages | Python, TypeScript | Python.NET (C#) |
| Type Safety | No (flexible schemas) | Yes (strongly typed) |
| GitHub Stars |
Pick Microsoft Agent Framework if you need: Production agents in Python and .NET
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Neither is universally better, as they serve different stacks. Microsoft Agent Framework is superior for enterprises running Python and .NET on Azure that need built-in durability and OpenTelemetry. LangChain is better for teams requiring TypeScript support, broad third-party tool integrations, and vendor-neutral deployments.
Yes. Teams frequently use LangChain to construct custom retrieval chains, tool definitions, or local prototypes, then deploy them into Microsoft Agent Framework and Azure AI Foundry to take advantage of managed hosting, enterprise identity, and durable execution.
LangChain is generally easier for prototyping simple RAG pipelines due to its massive catalog of tutorials and community examples. Microsoft Agent Framework has a steeper initial architectural curve around workflows and harnesses, but provides a cleaner, typed structure for enterprise software engineers.
Execution speed is dominated by underlying LLM API latency rather than framework overhead. However, Microsoft Agent Framework offers highly optimized .NET execution for C# backends, while LangChain applications benefit from lightweight async primitives in Python and TypeScript.
Both frameworks are open-source and free under the MIT license. Operating costs are driven by model token consumption and infrastructure. Microsoft Agent Framework simplifies cost controls via Azure AI Foundry governance, while LangChain requires self-managed infrastructure or commercial LangSmith plans for advanced tracking.
Yes, but switching requires refactoring. Core prompts and tool logic transfer easily, but LangChain chains must be translated into Microsoft workflow graphs and agent harnesses, or vice versa, especially when shifting between TypeScript and .NET runtimes.
LangChain is more popular on GitHub with 140.3K stars, against 13.8K for Microsoft Agent Framework. Stars measure developer interest, so also compare downloads and contributors above.
LangChain has 3.7K contributors and its last commit was 3 mo ago. Microsoft Agent Framework has 268 contributors and its last commit was 1 wk ago.
LangChain was first released in 2022 and Microsoft Agent Framework in 2025. A longer track record usually means more examples, integrations and answered questions.
LangChain has Evaluations built in. Microsoft Agent Framework does not, based on each project’s documentation. See the capabilities table above for the full list.
Microsoft Agent Framework has Type-Safe built in. LangChain does not, based on each project’s documentation. See the capabilities table above for the full list.
LangChain is a Mixed framework from LangChain Inc., released under the MIT license. Microsoft Agent Framework is a Mixed framework from Microsoft, released under the MIT license. The table above shows where their capabilities and community activity differ.
Start with the language your team already ships in. Then check which capabilities you need, such as multi-agent workflows, human approval steps or memory. Community size matters too: a larger project means more examples, integrations and answers when your team gets stuck.
npm downloads per week
PyPI downloads per month
| 140,318 |
| 13,824 |
| Monthly PyPI Downloads | ~169.3 million | ~166,000 |
| Orchestration Focus | Composable chains and building blocks | Graph-based workflows and multi-agent coordination |
| Default Observability | LangSmith | OpenTelemetry and DevUI |
| Primary Cloud Target | Cloud-agnostic (self-hosted or any cloud) | Azure AI Foundry |
At the time of writing, LangChain commands massive adoption with 140,318 GitHub stars and over 169 million monthly PyPI downloads. Microsoft Agent Framework, released in October 2025, reflects an enterprise-focused consolidation with 13,824 GitHub stars and roughly 166,000 monthly PyPI downloads.
LangChain approaches LLM applications through modular building blocks. It centers on the LangChain Expression Language (LCEL), which provides a declarative pipeline interface for composing prompts, models, output parsers, and retrievers. LangChain is intentionally model-agnostic, giving developers identical interfaces whether calling OpenAI, Anthropic, Google, or open models via Ollama. However, the core library lacks strict static typing, and its fast-moving API has historically gone through multiple architectural shifts. For complex stateful flows, LangChain steers users toward companion tools like LangGraph or Deep Agents.
Microsoft Agent Framework treats agents and graph workflows as core first-class abstractions. Instead of generic chains, it organizes logic into sequential, concurrent, handoff, and group orchestrations. Its design prioritizes language parity across Python and C#/.NET. Developers writing enterprise software in .NET receive the exact same architectural capabilities, interfaces, and patterns as Python developers. The framework emphasizes strict typing, structured middleware, and clear separation between agent definitions, tools, and execution harnesses.
LangChain offers extensive tool use and memory primitives. It connects to hundreds of external services using standard tool decorators and schemas. For memory, LangChain provides conversation buffers, windowed summaries, and vector store backed retrievers. While LangChain can run multi-agent routines via harness methods like create_agent, teams building advanced multi-turn or multi-agent handoffs often have to add LangGraph to handle shared state and cyclic loops effectively.
Microsoft Agent Framework inherits the multi-agent lineage of AutoGen alongside the enterprise patterns of Semantic Kernel. It treats multi-agent patterns, such as handoffs and group orchestrations, as standard orchestration primitives. Agents can coordinate natively without bolting on separate packages. Its tool calling subsystem supports standard code tools as well as enterprise Azure integrations like Bing Grounding and Code Interpreter. Memory management includes conversational persistence and explicit session checkpointing.
Production readiness exposes the clearest philosophical divergence between the two tools.
LangChain relies heavily on LangSmith for production observability, tracing, debugging, and evaluations. LangSmith provides tracing across chains, tool executions, and subagents, along with evaluation suites to monitor token drift and regression. For durability and long-running execution, raw LangChain chains need external state management or the LangGraph checkpointing runtime. Scaling a LangChain deployment generally requires custom containerization on Kubernetes or serverless providers.
Microsoft Agent Framework was designed around production durability from day one. It incorporates graph checkpoints and time-travel debugging directly into its workflow engine. If an agent crashes mid-task, it can resume from the last checkpoint. For observability, it uses built-in OpenTelemetry standards and ships with a local DevUI for interactive inspection. When it comes to scaling, Microsoft Agent Framework features one-command deployment to Azure AI Foundry Hosted Agents, handling managed infrastructure, container hosting, Azure Functions durability, and managed identity out of the box.
LangChain has one of the largest open-source communities in the AI ecosystem. With 3,691 contributors and more than 3.6 million weekly npm downloads at the time of writing, almost every new vector database, retrieval technique, or hosted API ships an official LangChain integration on launch day. If you hit an edge case, a solution usually exists on GitHub issues or developer forums.
Microsoft Agent Framework is much younger, having launched in late 2025. With 268 contributors at the time of writing, its third-party community recipe ecosystem is smaller. However, it benefits from Microsoft backing and unifies two existing user bases: teams migrating from Semantic Kernel and those coming from AutoGen. For .NET teams, it is essentially the only modern, officially supported agent framework available.
Both frameworks are licensed under the permissive MIT license, permitting self-hosting and commercial modification without licensing fees.
LangChain is completely cloud-agnostic. You can run it locally, inside private AWS or GCP clusters, or in serverless environments. Its commercial layer is LangSmith, which is an external observability platform available as managed SaaS or self-hosted enterprise software.
Microsoft Agent Framework can also be self-hosted anywhere Python or .NET runs. However, its tooling and hosting patterns are tuned for the Microsoft ecosystem. Its managed path relies on Azure AI Foundry, Azure Functions, and Azure managed identity. Teams not using Azure will find that non-Microsoft cloud patterns feel secondary.
Migrating between the two frameworks requires shifting mental models. LangChain emphasizes pipelines and composable steps via LCEL, while Microsoft Agent Framework emphasizes agent harnesses, workflows, and stateful graphs.
When moving from LangChain to Microsoft Agent Framework, your tool definitions using @tool convert cleanly into Microsoft Agent Framework tool definitions. However, custom LCEL chains must be refactored into explicit workflow steps or agent skills. In return, you gain native checkpointing, OpenTelemetry tracing, and straightforward deployment paths to Azure AI Foundry.
Moving from Microsoft Agent Framework to LangChain usually occurs when a project requires TypeScript support or needs to run completely outside the Azure ecosystem. Migrating away means replacing Microsoft workflow graphs with either LCEL chains or LangGraph state graphs. You will also need to replace Azure-managed grounding tools with third-party retrievers and swap OpenTelemetry tracing configurations for LangSmith or custom tracing exporters.
