tryAGI/LangChain: the C# LangChain port, its chains and its SQLite RAG path

What tryAGI/LangChain is for in a .NET codebase

The project describes itself as a C# implementation of LangChain, and the README states the intent plainly: "We try to be as close to the original as possible in terms of abstractions, but are open to new entities." That sentence sets the audience. If you have read Python LangChain examples and want the same vocabulary in C#, this repository is aimed at you. If you have never used LangChain and are choosing an LLM orchestration approach for a greenfield .NET service, the abstractions here will feel like they were designed somewhere else, because they were.

The README also explains why the project exists alongside Semantic Kernel: Semantic Kernel "is useful and we use it wherever possible, it does not cover every scenario and is closely tied to the Microsoft ecosystem." That is the actual positioning. This is not a Semantic Kernel replacement for Microsoft-centric workloads. It is for teams that want provider breadth and LangChain-shaped composition, including third-party libraries, and are willing to work with a smaller, contributor-driven codebase to get it.

How the abstraction layer works: providers, models, chains

The mechanism visible in the README is a provider object that hands out model wrappers, plus a chain pipeline built from composable steps. In the sample, OpenAiProvider is constructed from an OPENAI_API_KEY environment variable, then two models come out of it: OpenAiLatestFastChatModel for generation and TextEmbeddingV3SmallModel for embeddings. Those two objects are the only things the rest of the sample needs. Swapping providers means swapping the provider construction, not rewriting the pipeline.

The retrieval path is explicit about its data flow. A vector database is opened over a SQLite file, and AddDocumentsFromAsync takes an embedding model, a dimensions value, a data source, a collection name and a text splitter. The README notes the default splitter is CharacterTextSplitter with ChunkSize = 4000 and ChunkOverlap = 200, and that dimensions "should be 1536 for TextEmbeddingV3SmallModel". That is a real constraint, not a suggestion: the embedding width and the stored vectors have to agree.

The chain form is a pipeline of operators joined with |. Set puts the question into the default key "text", RetrieveSimilarDocuments pulls the five closest documents, CombineDocuments writes them to "context", Template substitutes context and question into the prompt, and LLM sends the result. RunAsync("text") reads the final value back out. The README presents this as an alternative to calling GetSimilarDocuments and GenerateAsync by hand, and both paths are shown producing the same kind of answer.

Installing LangChain for C# and running a first retrieval chain

The package is published on NuGet under the name LangChain, and the README's badge points at the pre-release feed. The wiki at tryagi.github.io/LangChain is where the project says to start. The sample in the README lists its dependencies in a comment: LangChain, LangChain.Databases.Sqlite and LangChain.DocumentLoaders.Pdf. Install those three:

dotnet add package LangChain --prerelease
dotnet add package LangChain.Databases.Sqlite
dotnet add package LangChain.DocumentLoaders.Pdf

Set the API key the provider reads, then build the index. This is the README's own flow: create the provider, create the two models, open a SQLite vector database, and load a PDF into a collection. The dimensions value must match the embedding model.

var provider = new OpenAiProvider(
    Environment.GetEnvironmentVariable("OPENAI_API_KEY") ??
    throw new InconclusiveException("OPENAI_API_KEY is not set"));
var llm = new OpenAiLatestFastChatModel(provider);
var embeddingModel = new TextEmbeddingV3SmallModel(provider);

using var vectorDatabase = new SqLiteVectorDatabase(dataSource: "vectors.db");
var vectorCollection = await vectorDatabase.AddDocumentsFromAsync<PdfPigPdfLoader>(
    embeddingModel,
    dimensions: 1536,
    dataSource: DataSource.FromUrl("https://example.com/book.pdf"),
    collectionName: "harrypotter",
    textSplitter: null);

After that, either query the collection directly or express the same work as a chain. The chain version is the one worth copying if you plan to extend the pipeline later, because each step is a named operator you can replace:

var chain =
    Set("Who was drinking a unicorn blood?")
    | RetrieveSimilarDocuments(vectorCollection, embeddingModel, amount: 5)
    | CombineDocuments(outputKey: "context")
    | Template(promptTemplate)
    | LLM(llm.UseConsoleForDebug());
var chainAnswer = await chain.RunAsync("text");

The README also shows llm.Usage and embeddingModel.Usage being printed after a run, which is how you see token consumption and the associated price. The README quotes two figures for its own sample: about 0.015 dollars to run from zero, and about 0.0004 dollars to re-run when the database already exists. Those are the project's stated numbers for that sample, not a general cost model.

Where the documentation is thin and the tests are the contract

The README is upfront that the wiki can drift. It says: "If the wiki contains outdated code, you can always take a look at the tests for this (src/Meta/test/WikiTests.cs)". Read that as a maintenance signal. The wiki is the teaching surface, but the test file is what is checked against the code. If a wiki snippet fails to compile, the project's own guidance is to compare it with the test rather than assume the wiki is authoritative.

The release history is the second thing to weigh. The repository's recent releases are v0.15.0 on 2024-06-27, v0.14.0 on 2024-05-03 and v0.13.0 on 2024-03-06. The README badge points at the pre-release NuGet feed, so the package you restore may not line up with the newest release entry you can see. The last push to the default branch was on 2026-09-08, so the repository is being touched, but that activity is not the same as a documented release train, and nothing in the README describes a versioning or deprecation policy. Treat a version pin as something you verify, not something the README promises.

When tryAGI/LangChain is the wrong choice

The maintainer notes are unusually candid and belong in any adoption decision. The README states: "I'm unlikely to be able to make serious progress alone, so my goal is to unite the efforts of C# developers to create a C# version of LangChain and control the quality of the final project." It also says pull requests are usually accepted within 24 hours and that the maintainer is looking for core-team developers to sponsor. That is a project asking for contributors, and it tells you the bus factor is a live concern rather than a hypothetical one.

There is a second boundary in the README itself. The project says Semantic Kernel does not cover every scenario and is tied to the Microsoft ecosystem, which is the reason this port exists. If your workload is already well served by Semantic Kernel and you do not need LangChain's composition style, adding this dependency buys you abstractions you will not use. The README does not present the two as interchangeable, and neither should your architecture review.

The third limitation is scope of documentation. The README does not document rollback, migration between package versions, or a support policy. It points to issues, discussions and Discord for help. For a team that needs a vendor-backed support contract, that is a gap the project does not claim to fill.

How it differs from Semantic Kernel and from Python LangChain

The nearest real alternative named in the README is Semantic Kernel, and the difference is not feature parity but ecosystem alignment. The README says Semantic Kernel is used "wherever possible" inside this project, and that it is "closely tied to the Microsoft ecosystem". tryAGI/LangChain takes the opposite stance on provider choice: it aims "to offer the broadest practical choice of implementations" and is "open to using third-party libraries where appropriate". In practice, that means the two projects can coexist in one solution, with Semantic Kernel handling the Microsoft-shaped parts and this library handling the LangChain-shaped ones.

The comparison with the Python original is a matter of intent rather than competition. This repository is a port that tries to stay close to the original abstractions, and the README's own example uses the same chain operators a Python reader would recognize. The cost of that fidelity is that when the Python project moves, the C# side has to follow, and the release list here is short. If you need parity with a specific Python feature, check whether it exists here before designing around it.

Licence and the cost of keeping up

The repository is MIT licensed. The README adds a clarification: "We do not plan to change the license in any foreseeable future for this project, but projects based on this within the organization may have different licenses." So the licence you inherit is MIT, while sibling projects from the same organization may not be, which matters if you vendor more than one of them. The README also credits some documentation to the dotnet/docs repository under CC BY 4.0, with code examples adapted for this project. That is an attribution obligation on the documentation, not on your application code.

Upgrade cost is the harder number to estimate. The project lists three releases across roughly four months in 2024, and the README directs readers to the wiki and to WikiTests.cs when snippets go stale. The practical implication is that upgrading is a test-and-compare exercise rather than a changelog read. The examples directory and the integration tests named in the README are the places to look when a version bump changes behaviour.

Editorial conclusion

Adopt tryAGI/LangChain if you are a C# team that wants LangChain-style composition (Set, RetrieveSimilarDocuments, CombineDocuments, Template, LLM) inside a .NET solution and you accept that the wiki is the primary reference while the tests act as the correctness check. Do not adopt it if you need a documented release cadence tied to the current package, or if your stack is already committed to Semantic Kernel and you only need Microsoft-ecosystem abstractions. Before writing production code, verify that the wiki page you are following still matches src/Meta/test/WikiTests.cs, and confirm the version you restore from NuGet against the v0.15.0 release entry.

Frequently asked questions

Official sources

1. License: MIT
2. Project website
3. README
4. Releases
5. tryAGI/LangChain on GitHub