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The VectorShift Python SDK targets the same backend as the visual editor — anything you build in the SDK is editable in the platform UI and vice versa. Pick a primitive and start shipping.

Pipeline

A graph of nodes (LLMs, loaders, transforms, knowledge readers) you build, save, and run.

Agent

Tools, typed I/O, and multi-turn conversations via Session.

Knowledge Base

Managed vector + document store you query directly or expose as a tool on an Agent.

Table

Typed schema, AI-generated columns (Pipeline / Agent), and SQL-grade filters.

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Get started in 60 seconds

1

Install

pip install vectorshift
2

Authenticate

export VECTORSHIFT_API_KEY="vs-..."
Generate a key under Account → API Keys.
3

Run your first pipeline

Follow the Quickstart for a five-line LLM pipeline you can paste and run.

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What you can build

RAG pipelines

Wire a knowledge-base reader into an LLM in a few lines.

Conversational agents

Tools, approvals, and streamed tool events out of the box.

Background batch runs

Kick off long-running jobs and poll for results.

Streaming chatbots

Multi-turn sessions with token-level streaming.

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Reference

Pipeline reference

Every public method, grouped by topic.

Agent reference

Lifecycle, tools, running, types.

Errors

Full exception hierarchy with status codes.
The SDK ships with type hints and a mypy plugin that catches pipeline-wiring and tool-definition mistakes at static-check time.