📦 New Python Package: langchain-tensorlake

What’s New

We just launched a native integration between LangChain and Tensorlake!

Now you can pass unstructured documents to a LangGraph agent and trust that parsing, chunking, and field-level accuracy are handled by Tensorlake’s document engine — no hacky pipelines required.

Why it matters

Many LangChain projects break down when document structure is inconsistent, or field extraction needs to be accurate and explainable. Tensorlake’s integration provides:

✅ Reliable document ingestion

✅ Schema-driven field extraction

✅ Native support for RAG pipelines

✅ Built-in SDK + Playground for fast iteration

It’s now super simple to use documents (like SEC filings, contracts, or invoices) as context in LLM apps with structured outputs and markdown chunks.

🛠 Try it

The integration is open-source and available now:

📘 Read the blog post

📦 Try the package

We’d love your feedback — and show us what you build!