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Tensorlake gives AI agents secure, stateful sandboxes and durable orchestration for long-running workflows. It plugs into the rest of your AI stack — agent frameworks, vector databases, and data platforms — so you can build end-to-end agentic workflows, including document understanding when a workflow needs it.

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Orchestration Frameworks

Run your agents in Tensorlake sandboxes and orchestration, and call Tensorlake tools — including document understanding — from your preferred framework.

LangChain

Use Tensorlake loaders and tools directly within your LangChain chains and agents.

OpenAI Agents SDK

Power your OpenAI agents with structured data extraction and document processing tools.

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Vector Databases

Index your parsed documents efficiently for Retrieval Augmented Generation (RAG).

Qdrant

Upsert structured points and embeddings directly into Qdrant collections.

Chroma

Store and retrieve document chunks with rich metadata in ChromaDB.

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Data Platforms

Turn unstructured documents into analytical tables for SQL queries and business intelligence.

Databricks

Load extracted data into Databricks Delta Tables for large-scale analytics.

MotherDuck

Query your parsed documents using SQL with DuckDB in the cloud.