Comparison

AutoGen vs Ray

On the evidence we track, Ray leads this comparison with a composite score of 71/100. Scores are only directly comparable because these tools share a category; the full breakdown and every source is below.

Machine formatsMarkdownJSONGraphQLMarkdown is open. JSON and GraphQL need a free key.
AutoGen61
★Ray71
Score
vioscaleAI score
AutoGen61 / 100medium · 56%
Ray71 / 100medium · 65%
Pricing
Free tier
AutoGen✓
Ray✓
Model
Price level
AutoGenlow
Rayfree
Transparent
AutoGen✓
Ray–
Integrations
Count
AutoGen11
Ray17
Reliability
Status page
AutoGen✓
Ray✓
Adoption
Dependent repos
AutoGen0
Github stars
AutoGen61,388
Package downloads weekly
Activity
Commits last 30d
AutoGen–
Ray100
Release
Cadence days
AutoGen8
Ray18
History
License
Spdx
Language
Primary
AutoGenPython
Content
Faq
AutoGen5 items

Capabilities

Feature-by-feature on the axes that matter for mlops & llmops tools. “-” means undocumented, not absent.

Core
Tool role
AutoGenAgent / RAG framework
RayAgent / RAG framework
Deployment
Self-hostable / OSS core
AutoGen✓
Ray✓
Managed cloud available
AutoGen✗
Ray✓
On-prem / VPC deployment
AutoGen✓
Ray✓
Observability
LLM tracing / observability
AutoGen✓
Ray✓
Evaluation (offline / LLM-judge / human)
AutoGen✗
Ray-
Dev
Prompt management + versioning
AutoGen✗
Ray-
Tracking
Experiment tracking / model registry
AutoGen✗
Ray✓
Serving
Model serving / inference endpoint
AutoGen✗
Ray✓
Interop
OpenTelemetry / OpenLLMetry compatible
AutoGen✓
Ray✓
Framework-agnostic
AutoGen✓
Ray✓
Gateway
Multi-provider model support
AutoGen✓
Ray✓
Data
No-train-on-customer-data guarantee
AutoGenYes
Ray-

What each one is

The product in its own terms, so the numbers below have context.

AutoGen

A programming framework for creating applications with multiple AI agents that coordinate and communicate to solve tasks. It supports various language models, tool execution, memory management, and distributed execution with built-in observability.

Independently observed

Ray

Leader

Open-source framework for building and scaling distributed ML and Python applications, supporting the full pipeline from data processing and model training to inference and serving with simple APIs.

Independently observed

Pricing

List pricing as published by each vendor, with the date we read it. Always verify at the source before you buy.

AutoGen

free_open_sourceFree tier

Free, open-source framework

as of verify ↗

Ray

Leader
Open source
as of verify ↗

Platform & deployment

Where each product runs and how it can be hosted. A dash means undocumented, not unsupported.

Platforms
Web
AutoGen✓
Ray✗
CLI
AutoGen✓
Ray✓
Deployment
Cloud / SaaS
AutoGen✗
Ray✓
Self-hosted
AutoGen✓
Ray✓

Integrations

What each product connects to. Counts come from the vendor's own integration directory where one exists.

AutoGen

17 total
  • OpenAI
  • Anthropic
  • Azure AI
  • Bedrock
  • Ollama
  • AzureOpenAI
  • DuckDuckGo Search
  • Model Context Protocol (MCP)
  • Azure OpenAI
  • Llama
  • Model-Context Protocol
  • Docker
  • Redis
  • DuckDuckGo
  • OpenTelemetry
  • MCP
  • Semantic Kernel
Independently observed

Ray

Leader
27 total
  • PyTorch
  • TensorFlow
  • AIBrix
  • AReaL
  • Cosmos Curate
  • Daft
  • Data-Juicer
  • DeltaCAT
  • Modin
  • NeMo Curator
  • NeMo-RL
  • OpenRLHF
  • RayDP
  • ROLL
  • SkyRL
  • SLIME
  • Syftr
  • verl
  • vLLM
  • Kubernetes
  • AWS
  • GCP
  • Azure
  • Databricks
  • +3 more
Independently observed
Still deciding?

AutoGen or Ray: which one depends on you

A composite score cannot know your constraints. Describe them and both get re-weighted against what you actually need, with the evidence behind every position.

Free to run, no account needed to start. How the evaluation works

Comparison generated from independently-sourced facts. Every value links to its source and retrieval date. See the method.