LangChain is an open-source framework for building LLM applications with chains, agents, and tools. Use it when you need Retrieval-Augmented Generation (RAG), multi-step workflows, or agentic automation. We build production-ready LangChain apps with vector stores, document loaders, and LLM orchestration.

We use LangChain's document loaders, text splitters, and embeddings to ingest your data into Pinecone, Weaviate, Chroma, or FAISS. We chain retrievers with LLMs for question-answering and citation. We optimize chunk size, overlap, and retrieval strategies for your domain.

Yes. LangChain agents can use tools (APIs, search, code execution) to complete multi-step tasks. We build custom tools and ReAct-style agents for research, automation, and workflows. For complex loops and state, we often use LangGraph as an extension of LangChain.

LangChain supports OpenAI, Anthropic, Google, Cohere, and 100+ providers via a unified interface. We use the native integrations and can add custom LLM wrappers. We help you choose and switch models for cost, latency, and quality.

We deploy LangChain apps as REST APIs or serverless functions on AWS, GCP, or Azure. We use LangSmith or custom observability for tracing, evaluation, and debugging. We optimize for throughput, caching, and cost—including async and batch processing.

LangChain excels at chains, agents, and tool use. LlamaIndex is optimized for data indexing, retrieval, and RAG over documents. Use LangChain for agentic workflows and complex orchestration; use LlamaIndex when retrieval quality and document ingestion are the primary focus. We can combine both.

We provide end-to-end LangChain development: architecture, implementation, testing, and deployment. We offer ongoing maintenance, upgrades when LangChain versions change, and extension to new use cases. We also train your team on LangChain best practices.