tryAGI/LangChain: a C# port of LangChain that keeps the Python abstractions and adds its own HTTP providers
C# implementation of LangChain. We try to be as close to the original as possible in terms of abstractions, but are open to new entities.
At a glance
- What is it?
- This is a community C# implementation of LangChain, distributed on NuGet as LangChain, MIT licensed, that mirrors the Python abstractions closely enough to reuse the same mental model. Its distinguishing feature is that it does not lock you into the Microsoft stack, but that freedom comes with a smaller ecosystem and a different set of maintenance obligations than Semantic Kernel.
- Who is it for?
- Adopt it if your team already thinks in LangChain terms and you want that vocabulary in C# without adopting the Microsoft agent stack, and if you are comfortable reading the wiki and the tests in src/Meta/test/WikiTests.cs as your primary documentation. Do not adopt it if you need a vendor-backed support contract or a library whose abstractions are guaranteed stable across releases.
- 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 4 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 gap this fills: LangChain vocabulary for C# teams that are not on Semantic Kernel
The README is explicit about why the project exists. Semantic Kernel is described as useful and used by this project where possible, but it "does not cover every scenario and is closely tied to the Microsoft ecosystem." That sentence is the whole positioning. If your codebase is .NET but your model providers, vector stores, or document loaders sit outside the Microsoft orbit, the official agent framework may not have an implementation for what you need, and writing the glue yourself means writing it repeatedly across projects.
The target reader is a C# developer who has already seen LangChain in Python or JavaScript and wants the same shape: providers, embedding models, vector collections, prompt templates, and chains that compose with the pipe operator. The stated goal is to stay "as close to the original as possible in terms of abstractions," which means the value is portability of knowledge rather than novelty. You are not learning a new framework. You are reusing one you already know, in a language whose type system will catch some of the mistakes the Python version catches at runtime.
That framing also sets the ceiling. This is not trying to be a better LangChain. It is trying to be the same LangChain, in C#, with a broader provider surface than the Microsoft alternative.
How composition actually works: the pipe operator over a chain of steps
The README's second example is the clearest statement of the mechanism. A chain is built by joining steps with the | operator, and each step is a small transformation over a shared state object. The example reads: Set the question, then RetrieveSimilarDocuments against a vector collection, then CombineDocuments into an output key called context, then Template to substitute into the prompt, then LLM to call the model. The result is executed with chain.RunAsync("text"), where the argument names the key to read out of the final state.
The default key for the question is "text" according to a comment in the example, and CombineDocuments takes an outputKey parameter, so the state is a dictionary-like structure that each step reads from and writes to. That is the same data flow as the Python original: retrieval produces documents, a combiner flattens them to a string, a template interpolates, and the model consumes the rendered prompt. The final two lines print llm.Usage and embeddingModel.Usage, which means token consumption and derived price are tracked on the model objects themselves rather than being returned from the call. That is a design choice worth noting: usage accounting is stateful and attached to the model instance, so a long-lived model object accumulates across calls.
The README also states there are two ways to do the same thing, and shows both. The first example skips chains entirely and calls GetSimilarDocuments followed by llm.GenerateAsync with a hand-written prompt string. The chain version is not doing anything the manual version cannot; it is packaging the same sequence into a reusable object. Whether that packaging is worth it depends on how many variations of the pipeline you need.
Getting a working retrieval pipeline running: the exact pieces from the README
The README's first example is the most complete setup instructions available, and it names its own dependencies in a comment: LangChain, LangChain.Databases.Sqlite, and LangChain.DocumentLoaders.Pdf. So the core package is not sufficient for retrieval. You add the database package for the vector store and the document loader package for PDF ingestion.
Model setup goes through a provider object. The example constructs new OpenAiProvider with the API key pulled from the OPENAI_API_KEY environment variable, and throws if it is missing. From that provider it builds an OpenAiLatestFastChatModel for generation and a TextEmbeddingV3SmallModel for embeddings. The embedding model has a hard constraint stated in the code: dimensions must be 1536 for TextEmbeddingV3SmallModel, and the vector database is created with that dimension count. Mismatching those two values is the kind of error that surfaces as a failed insert rather than a clear message, so it is worth treating the dimension argument as coupled to the model choice.
The vector store is a SqLiteVectorDatabase constructed with a dataSource of "vectors.db", declared in a using statement so it is disposed. Documents are added with AddDocumentsFromAsync, which takes the embedding model, the dimensions, a DataSource.FromUrl pointing at a PDF, an optional collectionName, and an optional textSplitter. The comment states the default splitter is CharacterTextSplitter with ChunkSize 4000 and ChunkOverlap 200, and that omitting collectionName is fine unless you want multiple collections in one database.
Two operational details in the comments are worth repeating because they are unusual to see stated so plainly. The example comments give a cost of 0.015 dollars to run from zero, creating embeddings and calling the LLM, and 0.0004 dollars to re-run when the database already exists. Those are the author's figures for that specific example, not a general benchmark, and they will move with provider pricing. The second detail is the data source itself: the example loads a public PDF of the first Harry Potter book from a school website URL. That is a demo, not a pattern to copy into production, and the question it asks ("Who was drinking a unicorn blood?") is chosen to produce a short, checkable answer.
Where the project is thin: documentation drift and a maintainer who says he cannot do it alone
The README points at a wiki for getting started, then immediately admits a failure mode: "If the wiki contains outdated code, you can always take a look at the tests for this (src/Meta/test/WikiTests.cs)." That is an honest and useful pointer, but it also tells you the documentation is not the source of truth. The tests are. A project where the wiki is verified by a test file named WikiTests is one where you should expect to read test code to confirm current API shape, and where a version bump may change signatures that the prose has not caught up with.
The maintainer notes are unusually candid. The maintainer states he is unlikely to make serious progress alone, that the goal is to unite C# developers around the effort, that he tries to accept any pull request within 24 hours, and that he is looking for core team members he would sponsor and share revenue with. Read that as a description of the project's actual risk profile. Bus factor is the concern here, and the mitigation is stated as recruitment rather than as a foundation or a company backing the work. The 24 hour pull request target is a commitment about responsiveness, not about review depth.
There is also a version signal in the release list. The most recent release shown is v0.15.0 from June 2024, preceded by v0.14.0 in May 2024 and v0.13.0 in March 2024. The zero-major version number is accurate: the API is not being presented as stable. The last push timestamp on the repository is later than the last release, so work continues between releases, but anyone pinning a version should expect the surface to move.
The comparison that matters: Semantic Kernel and the ecosystem trade
The README names Semantic Kernel directly and explains the difference in approach rather than just the difference in features. Semantic Kernel is tied to the Microsoft ecosystem; this project aims for "the broadest practical choice of implementations" and says it is open to using third-party libraries where appropriate. That is a real architectural difference. A framework built around one vendor's abstractions will have first-class support for that vendor's services and a narrower set of adapters elsewhere. A framework built as a port of a language-agnostic design will have adapters for whatever contributors have written.
The cost is on the other side of the ledger. Semantic Kernel has corporate backing, a support path, and a release cadence that does not depend on volunteer availability. This project has a maintainer asking for help and a stated willingness to use third-party libraries, which cuts both ways: you get breadth, and you inherit the maintenance state of whatever those third-party dependencies are. If your organization requires a vendor to escalate to, this is the wrong tool, and no amount of abstraction parity changes that.
A second alternative worth naming is doing nothing: calling the provider's HTTP API directly and writing your own retrieval loop. The README's own first example shows how little code that takes, roughly a similarity search and a prompt string. If your pipeline has one shape and one provider, the framework may be more surface area than you need. The chain abstraction earns its place when you have several pipelines that share steps.
Licence, upgrades, and what maintenance actually costs here
The project is MIT licensed, and the README adds a forward-looking statement from the maintainers: they 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. That is a statement about intent, not a guarantee, and it is worth reading precisely. MIT on the repository you depend on does not tell you the licence of every package in the organization's orbit, so if you pull in additional LangChain.* packages for databases or document loaders, check each one.
There is a second licensing detail that is easy to miss. The README states that some documentation is based on documentation from the dotnet/docs repository under CC BY 4.0, with code examples changed to this project's API. That affects the documentation, not the library code, but if you copy examples out of the wiki into your own docs, the provenance is worth knowing. This is not legal advice; if attribution obligations matter to your organization, have someone check the terms rather than relying on a README summary.
Upgrade cost is driven by the zero-major versioning and the documentation drift already noted. The practical workflow is to pin a specific NuGet package version, and when you bump it, diff the tests under src/tests/LangChain.IntegrationTests and src/Meta/test/WikiTests.cs against the wiki page you followed. Integration tests in the repository are named ReadmeTests, which suggests they exist to keep the README honest. Treat them as the changelog you actually read.
Who should take the dependency, and the two checks to run before you do
Take it if you have a C# codebase, you already understand LangChain's model from another language, and your provider or vector store of choice is not well served by Semantic Kernel. The pipe-based chain composition and the provider abstraction are the parts that carry over, and the SQLite vector database plus PDF loader path in the README is enough to stand up a local retrieval prototype without external infrastructure. The project also states it responds quickly on Discord, which matters more than usual for a library with this bus factor.
Do not take it if you need a support contract, a stable API surface, or documentation that is authoritative without reading test code. Do not take it if your pipeline is a single provider and a single prompt, because the manual path in the README's first example is shorter than the chain setup.
Before you commit, verify two things in your own environment. First, confirm that the NuGet package version you intend to pin still exposes the types the wiki shows; the README itself warns the wiki may be outdated and points at src/Meta/test/WikiTests.cs as the fallback. Second, confirm that the specific provider you plan to call exists in this repository rather than only in the Python original, since the project's stated aim of broad provider choice is a direction, not a guarantee about any one integration. If both checks pass against your target version, the dependency is reasonable. If the second one fails, you are writing the adapter yourself, and the calculus changes.
Editorial conclusion
Adopt it if your team already thinks in LangChain terms and you want that vocabulary in C# without adopting the Microsoft agent stack, and if you are comfortable reading the wiki and the tests in src/Meta/test/WikiTests.cs as your primary documentation. Do not adopt it if you need a vendor-backed support contract or a library whose abstractions are guaranteed stable across releases. Before committing, verify two things in your own environment: that the NuGet package version you pin still matches the API surface in the wiki, and that the provider you actually intend to call is implemented in the repository rather than only in the Python original.
Community notes