---
id: vellum-ai
version: "1.14.7"
license: MIT
license_treatment: permissive
maintenance: active
---

# vellum-ai

License: permissive · Maintenance: active · Downloads: 119.8K/mo

## What it is and what it does

Vellum is a Python SDK for building and managing production AI applications. It provides two main layers: a low-level Client SDK for direct interaction with the Vellum API, and a high-level Workflows SDK for defining the control flow and structure of AI systems as declarative graphs. The package includes built-in support for prompt management, evaluation frameworks, retrieval services, deployment, and real-time observability.

The Workflows SDK can be used independently without a Vellum account to define and debug AI system control flow, though a paid account unlocks additional features like UI-based editing, managed retrieval, and deployment integration. The package is actively maintained, supports Python 3.9 through 3.12, and has low installation friction with a pure Python wheel distribution.

Use it for:

- Define and orchestrate multi-step AI workflows with declarative graph syntax without requiring a Vellum account.
- Iterate on and compare prompts across different LLM providers using the built-in prompt playground.
- Measure and evaluate the quality of AI system outputs at scale with the evaluations framework.
- Monitor and debug production AI applications in real-time with detailed logs, metrics, and user feedback.
- Deploy AI system updates independently from application code using one-click deployment.
- Transform unstructured content into context-aware retrieval solutions optimized for AI systems.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Vellum is an SDK for building and deploying production-grade AI applications, providing orchestration, prompt management, evaluations, retrieval, and observability through both a low-level client API and a high-level workflow engine.

Yes, if you are building production AI applications and want a structured SDK for orchestration and observability. The Workflows SDK alone justifies installation for control-flow definition without vendor lock-in. MIT licensing, active maintenance, and low install friction make it a low-risk addition. A Vellum account is required to unlock full platform features (prompt management, deployment, managed retrieval), so evaluate whether the free tier meets your needs before committing to paid features.

## Install

pip install vellum-ai
uv add vellum-ai
poetry add vellum-ai

## Installing vellum-ai

Before you install: Low install friction with a pure Python wheel distribution. Active maintenance with recent commits (2026-04-06) and steady releases. Requires Python 3.9 or later and brings in 17 runtime dependencies including common utilities like pydantic, httpx, and pyyaml.

License in practice: MIT license (permissive) allows free use, modification, and distribution with minimal restrictions. No license-based barriers to adoption.

Quickstart:

pip install vellum-ai

from vellum.workflows import Workflow, Node

# Define a workflow with nodes and relationships
workflow = Workflow(name="my_ai_app")
node = Node(name="example_node")
workflow.add_node(node)

Requires Python 3.9 or later. Most features require a Vellum account and API key; however, the Workflows SDK can be used without an account to define control flow.

Verify before relying:

- Whether the 17 runtime dependencies introduce any transitive security concerns beyond the direct package.
- Performance characteristics and scalability limits for the workflow engine with large or complex graphs.
- Specific integrations or LLM providers supported by the orchestration layer.

## Package facts

- License: MIT (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 119.8K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

ai application orchestration sdk, prompt management and deployment, workflow engine for ai systems, ai observability and monitoring, llm application framework, llm-orchestration, workflow-engine, ai-observability

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