CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

Project Overview

C# implementation of the LangChain framework for building applications with LLMs through composability. Provides chains, memory, RAG (Retrieval-Augmented Generation), document loaders, text splitters, and a serving layer. Distributed as multiple NuGet packages under the LangChain namespace. The meta-package LangChain bundles the most commonly used providers (OpenAI, Anthropic, Google, Ollama, Azure, etc.) with the core library.

Build and Test Commands

# Build the entire solution
dotnet build LangChain.sln

# Run all integration tests
dotnet test src/Meta/test/LangChain.IntegrationTests.csproj

# Run core unit tests
dotnet test src/Core/test/UnitTests/LangChain.Core.UnitTests.csproj

# Run splitter tests
dotnet test src/Splitters/Abstractions/test/LangChain.Splitters.Abstractions.Tests.csproj

# Run a specific test
dotnet test src/Meta/test/LangChain.IntegrationTests.csproj --filter "FullyQualifiedName~WikiTests"

# Validate trimming/NativeAOT compatibility
dotnet build src/Helpers/TrimmingHelper/TrimmingHelper.csproj

Integration tests require API keys via environment variables (e.g., OPENAI_API_KEY). Tests skip (not fail) if keys are unset.

Architecture

Project Structure

src/
├── Core/src/                  # LangChain.Core — chains, memory, prompts, retrievers, schema
├── Meta/src/                  # LangChain (meta-package) — bundles Core + popular providers
├── Meta/test/                 # Integration tests (WikiTests, ReadmeTests)
├── DocumentLoaders/
│   ├── Abstractions/          # LangChain.DocumentLoaders.Abstractions
│   ├── Pdf/                   # LangChain.DocumentLoaders.Pdf (PdfPig-based)
│   ├── WebBase/               # LangChain.DocumentLoaders.Html
│   └── Word/                  # LangChain.DocumentLoaders.Word
├── Splitters/
│   ├── Abstractions/          # LangChain.Splitters.Abstractions (CharacterTextSplitter, etc.)
│   └── CSharp/                # LangChain.Splitters.CSharp
├── Extensions/
│   ├── DependencyInjection/   # LangChain.Extensions.DependencyInjection
│   └── Docker/                # LangChain.Extensions.Docker
├── Serve/
│   ├── Abstractions/          # LangChain.Serve.Abstractions
│   ├── src/                   # LangChain.Serve (ASP.NET Core middleware)
│   └── OpenAI/                # LangChain.Serve.OpenAI (OpenAI-compatible serving)
├── Utilities/
│   ├── Pollyfils/             # LangChain.Polyfills (framework polyfills)
│   ├── Sql/                   # LangChain.Utilities.Sql
│   └── Postgres/              # LangChain.Utilities.Postgres
├── Cli/                       # LangChain.Cli
└── Providers/Abstractions/    # Local copy of provider abstractions (mirrors LangChain.Providers)
examples/                      # Sample projects (OpenAI, Azure, Memory, Serve, LocalRAG, etc.)

Core Abstractions (src/Core/src/)

Chains (Chains/):

• Chain static class — fluent API for composing stackable chains using the | operator

• Key chain factory methods: Set(), Template(), LLM(), RetrieveSimilarDocuments(), CombineDocuments(), LoadMemory(), UpdateMemory(), TTS(), STT(), ReActAgentExecutor(), GroupChat()

• StackableChains/ — individual chain implementations (LLM, ReAct agents, Crew agents, file chains, image generation)

• Chains are composed via the pipe | operator and executed with chain.RunAsync("key")

Memory (Memory/):

• ConversationBufferMemory — stores full conversation history

• ConversationSummaryMemory — stores summarized conversation history

• ConversationSummaryBufferMemory — hybrid (recent messages + summarized history)

• ConversationWindowBufferMemory — sliding window of recent messages

Prompts (Prompts/):

• Prompt template system with variable substitution via {variable_name} placeholders

Base Classes (Base/):

• BaseChain — abstract base for all chains

• BaseCallbackHandler — callback system for tracing and debugging

Dependencies

LangChain.Core depends on:

• LangChain.Providers.Abstractions (NuGet) — provider interfaces (IChatModel, IEmbeddingModel)

• LangChain.Databases.Abstractions (NuGet) — database interfaces (IVectorDatabase, IVectorCollection)

• LangChain.DocumentLoaders.Abstractions (project reference)

• LangChain.Splitters.Abstractions (project reference)

The meta-package additionally references:

• LangChain.Providers.OpenAI, LangChain.Providers.Anthropic, LangChain.Providers.Google, LangChain.Providers.Ollama, LangChain.Providers.Azure, LangChain.Providers.DeepSeek, LangChain.Providers.HuggingFace

• LangChain.Databases.InMemory

Key Patterns

Chain Composition — the primary pattern for building LLM workflows:

var chain =
    Set("question text")
    | RetrieveSimilarDocuments(vectorCollection, embeddingModel, amount: 5)
    | CombineDocuments(outputKey: "context")
    | Template(promptTemplate)
    | LLM(llm);
var result = await chain.RunAsync("text");

Provider/Model Pattern — providers authenticate, models execute:

var provider = new OpenAiProvider(apiKey);
var llm = new OpenAiLatestFastChatModel(provider);
var embeddingModel = new TextEmbeddingV3SmallModel(provider);

RAG Pattern — load documents, create embeddings, query with similarity search:

var vectorCollection = await vectorDatabase.AddDocumentsFromAsync<PdfPigPdfLoader>(
    embeddingModel, dimensions: 1536, dataSource: DataSource.FromUrl(url));
var similarDocuments = await vectorCollection.GetSimilarDocuments(embeddingModel, question);

Key Conventions

• Target frameworks: net4.6.2, netstandard2.0, net8.0, net9.0

• Language: C# preview, nullable reference types enabled, implicit usings

• Strong naming: All assemblies signed with src/key.snk

• Versioning: MinVer with v tag prefix

• Testing: MSTest framework

• Central package management: src/Directory.Packages.props

• Cross-project dependencies between LangChain ecosystem repos are via NuGet packages, not project references