LLMTornado: A Provider-Agnostic .NET SDK for Agents, MCP and A2A
The .NET library to build AI agents with 30+ built-in connectors.
At a glance
- What is it?
- LLMTornado is an MIT-licensed C# library that wraps 30+ model providers, a handful of vector databases, and an agent orchestration layer behind one API. The pitch is real, but the release cadence and the breadth of connectors are the two things to weigh before adopting it.
- Who is it for?
- Adopt LLMTornado if you are writing C# and want one call shape across OpenAI, Anthropic, Google, local vLLM or Ollama, and you accept that the connector surface moves fast. Do not adopt it if you need long-term API stability with no upgrade work, or if your stack is not .NET.
- Can I use it commercially?
- Yes. MIT is a permissive licence: you can use, modify and sell software built on it, as long as you keep its copyright and licence notices.
- Is it still maintained?
- Yes. The repository last received commits 29 days ago.
- What is it written in?
- Mainly C#, according to GitHub's language statistics.
Answers come from the project's GitHub data, last synced on September 15, 2026, and from our analysis. They are not legal advice.
DEEP OPEN-SOURCE ANALYSIS
The problem LLMTornado targets in .NET shops
Most .NET teams that call a hosted model start by taking a first-party SDK, wiring it into a service, and then discovering that the second provider they need has a different client shape, different streaming types, and different names for the same concept. The README states the library's position directly: it is a provider-agnostic SDK with built-in connectors to more than thirty API providers, and it claims no dependencies on first-party SDKs. That last part matters more than it looks. If the connectors are implemented against HTTP and the providers' own wire formats rather than by wrapping vendor packages, then adding a provider does not drag a second dependency graph into your solution, and swapping providers does not change your transitive package set.
The stated workflow is that you write pipelines once and execute them against any provider by changing the model's name. That is the concrete promise: the call shape stays fixed while the model identifier varies. The audience is C# developers building chatbots, RAG pipelines, and autonomous agents who want a single abstraction rather than N vendor clients, and who may need to move between a hosted API and a local deployment without rewriting the calling code.
Orchestration is three concepts, not a framework
The agent layer is described with three named pieces: Orchestrator is the graph, Runner is a node, and Advancer is an edge. That is a small vocabulary for a multi-agent system, and it is deliberately narrower than the graph abstractions you find in Python agent frameworks. The README lists handoffs, parallel execution, Mermaid export, and a builder pattern as the features built on top of those three concepts.
Mermaid export is the detail worth pausing on. A graph that can be rendered as Mermaid text can be inspected in a pull request, pasted into a diagram tool, or diffed between commits. For teams that have been burned by agent flows that only exist as runtime state, having the topology as a serializable artefact is a real difference in how you review changes. The builder pattern is the other practical piece: it suggests you can assemble an orchestration without hand-constructing every graph object, which keeps short flows readable.
What the material does not give is the failure semantics. There is no statement in the README about what happens when a Runner throws, whether an Orchestrator retries a node, or how partial state is handled when one branch of a parallel execution fails. Those are the questions that decide whether an orchestration layer is usable in production, and they are not answered here.
Connectors, local models, and the vector store list
The provider list spans hosted and local. On the hosted side the README names Alibaba, Anthropic, Azure, Blablador, Cohere, DeepInfra, DeepSeek, Google, Groq, LiteLLM, MiniMax, Mistral, MoonshotAI, OpenAI, OpenRouter, Perplexity, Requesty, Upstage, Voyage, xAI, and Z.ai, with the note that more exist. On the local side it names vLLM, Ollama, and LocalAI, with what the README calls integrated support for request transformations. That transformation layer is the interesting part of the local story, because local runtimes rarely implement every field a hosted API accepts, and something has to reconcile the request before it is sent.
For retrieval, the built-in vector database connectors are Chroma, PgVector, Pinecone, Faiss, and QDrant. PgVector and Faiss sit at opposite ends of the operational spectrum (one is a Postgres extension you may already run, the other an in-process index), so the list is not a single deployment model dressed up as five options.
The README also points to a FeatureMatrix document that tracks detailed endpoint support. That is the honest admission that connector coverage is uneven: a provider can be listed while individual endpoints on it are not implemented. Treat the provider list as a directory, not a compatibility guarantee.
Getting it running from the published packages
Installation is through NuGet, and the README's own badge links give the package identifiers: LlmTornado for the core SDK, LlmTornado.Agents for orchestration, LlmTornado.Mcp for Model Context Protocol support, and LlmTornado.A2A for agent-to-agent collaboration. There is also a separate Microsoft.Extensions.AI package, LlmTornado.Microsoft.Extensions.AI, which the README says enables interoperability with Microsoft.Extensions.AI so the library can be plugged into Semantic Kernel applications. The README links a demo file, MicrosoftExtensionsAiDemo.cs, under src/LlmTornado.Demo, as the worked example for that path.
What the supplied material does not contain is a code sample, a configuration key, or an environment variable name. The README points to llmtornado.ai/getting-started for the quickstart rather than inlining it, and the only source paths it names are demo files in the repository (ChatDemo2.cs for request preview and transformation, and the Microsoft.Extensions.AI demo). So the accurate statement is: the packages and their identifiers are documented, the quickstart is hosted on the project site, and any article claiming a specific builder call or API key variable would be guessing. If you are evaluating this, read the quickstart page and the demo files in src/LlmTornado.Demo before you write your first call, because the repository's demos appear to be the working reference rather than the README.
Version cadence is the real adoption cost
Look at the release dates. v3.8.65 landed on 2026-07-27, v3.8.66 on 2026-08-03, and v3.8.67 on 2026-08-16. Three patch releases inside three weeks, all on the 3.8 line. The news entries in the README show the same rhythm over a longer window: a new connector, a new endpoint, or a new protocol landing roughly every month through late 2025 and into 2026, including MiniMax, Upstage, Requesty, batch endpoints for OpenAI, Anthropic and Google, a videos endpoint for OpenAI/Sora, and a tokenize endpoint across providers that support it.
That cadence cuts both ways. It means the library tracks provider changes faster than you could maintain your own HTTP layer, which is the whole reason to take a dependency like this. It also means the surface you depend on is moving. The README claims stable APIs, and the patch-level version numbers are consistent with that claim for the 3.8 line, but a project that ships every two to three weeks is a project you will be upgrading regularly. Budget for that: pinning an old version means missing the connector fixes, and tracking the head means periodic upgrade work. Neither is free, and the README does not describe a long-term support branch or a deprecation policy.
Where it is the wrong tool
The obvious boundary is the platform. This is a .NET library, published on NuGet, written in C#. If your services are Python or TypeScript, nothing here applies, and the agent orchestration concepts will not transfer as code even if the ideas do.
The less obvious boundary is scope. LLMTornado is a client and orchestration layer, not a runtime. The README describes guardrails, request preview and transformation, and OpenTelemetry support, but it does not describe hosting, scheduling, durable execution, or state persistence for long-running agents. If your problem is keeping an agent alive across process restarts with resumable state, this library gives you the graph and the model calls, not the durability layer underneath them.
There is also a coverage trap. With more than thirty connectors, the depth per provider varies, and the README itself defers to the FeatureMatrix for endpoint-level detail. A provider being listed does not mean the one endpoint you need is implemented. Teams that pick a provider for an unusual capability (a specific audio format, a batch mode, a tokenizer) should check that provider's row in the matrix before designing around it.
How it differs from Semantic Kernel and Microsoft.Extensions.AI
The nearest comparison in the .NET ecosystem is Microsoft.Extensions.AI and Semantic Kernel. The difference is in what each one is trying to be. Microsoft.Extensions.AI defines abstractions for chat clients and related interfaces, and Semantic Kernel builds orchestration and plugin plumbing on top of that Microsoft-centred stack. LLMTornado takes the opposite starting point: it implements its own provider-agnostic client layer against more than thirty providers, and then offers a bridge so that layer can be consumed through Microsoft.Extensions.AI and used inside Semantic Kernel applications.
That ordering matters for portability. If you build directly on Microsoft.Extensions.AI, your provider set is whatever implements those abstractions. If you build on LLMTornado, your provider set is the connector list, and the Microsoft abstractions become an interop surface rather than the foundation. The trade is that you are betting on a smaller project's connector maintenance instead of Microsoft's abstraction stability. The README's claim of no dependencies on first-party SDKs reinforces the first position: the library is not a thin shim over vendor packages, so its behaviour is its own to maintain.
Licence, maintenance, and what to check first
The licence is MIT, per the repository metadata and the badge in the README. That is permissive: you can use it in commercial and closed-source products, and you can modify and redistribute it, provided the copyright notice and permission notice are preserved. The MIT text also disclaims warranty, so the maintenance burden sits with you. This is a description of the licence terms, not legal advice; if your organisation has a policy on third-party dependencies, route it through whoever owns that policy.
Maintenance cost has two components. The first is upgrades, and the release dates above show why: you should expect to move versions every few weeks if you want connector fixes, so the question is whether your process can absorb a NuGet bump on that schedule. The second is the breadth of the dependency itself. A library with connectors to thirty-plus providers, five vector databases, MCP, A2A, and an agent runtime has a large surface, and the parts you do not use are still code you are shipping. The README does not describe an option to trim unused connectors, so assume the packages are not modularised per provider.
Before adopting, verify three things. Check the FeatureMatrix row for the exact endpoint you need, not just the provider name. Check that the NuGet versions for LlmTornado, LlmTornado.Agents, LlmTornado.Mcp, and LlmTornado.A2A are consistent with each other and with the release you are reading about. And read the demo files under src/LlmTornado.Demo, particularly MicrosoftExtensionsAiDemo.cs and ChatDemo2.cs, because they are the concrete reference the README points to for the interop and request-transformation paths.
Editorial conclusion
Adopt LLMTornado if you are writing C# and want one call shape across OpenAI, Anthropic, Google, local vLLM or Ollama, and you accept that the connector surface moves fast. Do not adopt it if you need long-term API stability with no upgrade work, or if your stack is not .NET. Before committing, verify the FeatureMatrix entry for the exact endpoint you depend on, confirm the NuGet package versions for LlmTornado, LlmTornado.Agents, LlmTornado.Mcp and LlmTornado.A2A line up with the release you are reading about, and check whether the Microsoft.Extensions.AI bridge covers the Semantic Kernel path you plan to use.
Community notes