AutoGen vs AutoGPT

Side-by-side comparison of features, pricing, and ratings

Analysis generated Live tool data as of 2026-11-09
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AutoGen or AutoGPT: which should you choose?

DimensionAutoGenAutoGPT
PricingFreeFrom $50/mo
Best forDevelopers building multi-agent apps in Python or .NET, Researchers studying multi-agent collaboration and conversation topologiesFounders and operators scheduling recurring research, briefings, and monitoring work, Sales teams building prospect shortlists and first-draft outreach with a human review step
Standout featuresAutoGen Studio web UI for prototyping agents without writing code (pip install, port 8080) · AgentChat Python framework for conversational single- and multi-agent apps, Python 3.10+ · Core event-driven framework for scalable multi-agent systemsNamed AI Experts: Maria for SEO content, Max for sales outreach, Jules for LinkedIn posts, Kai and Frankie for · Otto chat: a Head of AI that coordinates Experts, answers questions, and creates or runs workflows · Workflow generation from a plain-English prompt, with clarifying questions, save, and dry-run
Viability score73/10086/100
APIYesYes

AutoGen is the stronger pick for developers building multi-agent apps in python or .net; AutoGPT fits better for founders and operators scheduling recurring research, briefings, and monitoring work.

Built from live tool data, last verified 2026-11-09.

AutoGen
AutoGen

AutoGen is Microsoft's open-source framework for building conversational and event-driven multi-agent AI apps in Python and .NET.

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AutoGPT
AutoGPT

AutoGPT gives you a team of named AI Experts coordinated by Otto, your Head of AI, briefed in plain English

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Pricing
Free
Paid
Plans
Open SourceFree
Pro$50/mo
Max$320/mo
TeamCustom
Popularity
5.3k views
5.9k views
Skill Level
Intermediate
Beginner-friendly
API Available
Platforms
—
WebAPI
Categories
🕸️ Agent Frameworks & Orchestration
🤖 Automation & Agents🕸️ Agent Frameworks & Orchestration
FeaturesAutoGenAutoGPT
AutoGen Studio web UI for prototyping agents without writing code (pip install, port 8080)
AgentChat Python framework for conversational single- and multi-agent apps, Python 3.10+
Core event-driven framework for scalable multi-agent systems
Deterministic and dynamic agentic workflows for business processes
Distributed multi-language agents via GrpcWorkerAgentRuntime over gRPC
AssistantAgent with model clients such as OpenAIChatCompletionClient
Customizable conversation patterns including group chat and sequential turns
McpWorkbench for connecting agents to Model Context Protocol (MCP) servers
OpenAIAssistantAgent for using the OpenAI Assistant API
DockerCommandLineCodeExecutor for running model-generated code in a Docker container
Community extensions you can discover, use, or publish
Human-in-the-loop approval and intervention inside agent workflows
Parallel .NET API surface alongside Python
Documented migration path for projects moving off AutoGen 0.2
MIT-licensed source you can self-host and modify
Named AI Experts: Maria for SEO content, Max for sales outreach, Jules for LinkedIn posts, Kai and Frankie for
Otto chat: a Head of AI that coordinates Experts, answers questions, and creates or runs workflows
Workflow generation from a plain-English prompt, with clarifying questions, save, and dry-run
Dry-run self-repair loop that simulates new workflows, catches errors, and fixes them automatically
Visual drag-and-drop workflow builder with branching, loops, nested workflows, and inline field validation
Parallel tool execution: Otto fires independent tools concurrently instead of one at a time
Context compaction keeps long chats coherent via automatic compression and gap-filling
Multimodal inputs: drop images and PDFs into chat for a vision model to read
Voice in and out: speak to Otto and hear responses via natural-sounding TTS voices
Teach Otto Skills: reusable procedures Otto learns from your processes
Browser automation via Stagehand and a Chromium-backed agent browser
MCP tool support: discover and execute tools from any compatible MCP server
Web search with Perplexity Sonar and WebFetch, plus a read-only SQL analytics block in chat
GitHub CLI in Otto: run gh commands in chat with commits signed by your GitHub profile
Discord and Telegram bot: tag @AutoGPT to hand off research, monitoring, and tasks
IntegrationsAutoGenAutoGPT
OpenAI
Model Context Protocol (MCP) servers
Docker
gRPC
OpenAI Assistant API
Discord
Telegram
Google Drive
WordPress
GitHub
Perplexity Sonar
WebFetch
Stagehand

What do real users say about AutoGen vs AutoGPT?

Not marketing copy and not our opinion — a structured sweep of public discussion (reviews, forums, communities and video comments), showing what people praise and what they complain about for each tool.

AutoGen

65 mentions across 5 sources · 51% positive — mixed (weighted across 5 sources)

Hacker News, YouTube, Stack Overflow, GitHub, Lemmy

What users praise

  • • MIT-licensed and free to self-host, with 61k+ GitHub stars and active development
  • • Three-layer architecture (Studio, AgentChat, Core) lets developers pick the abstraction they need
  • • Native MCP support via McpWorkbench keeps it current with the protocol everyone is adopting
  • • DockerCommandLineCodeExecutor sandboxes model-generated code — a rare out-of-the-box safety win

What frustrates them

  • • ChatCompletionCache silently omits tool_choice from its cache key — a genuine correctness bug
  • • BaseTool still lacks an approval function, so guarding destructive tool calls is manual
  • • AutoGen Studio had a code-execution flaw — agent runtimes require real sandboxing
  • • Stack Overflow threads show onboarding friction: simple conversational agents not working cleanly

Researched Oct 9, 2026

AutoGPT

71 mentions across 6 sources · 39% positive — critical (weighted across 6 sources)

Hacker News, YouTube, App Store, Stack Overflow, GitHub, Lemmy

What users praise

  • • 187,483 GitHub stars make it the most-recognized open-source agent project ever released
  • • Named AI Experts (Maria, Max, Frankie) give non-technical users a real mental model for delegation
  • • Otto's natural-language briefing removes the need to learn agent configuration or API keys
  • • Dry-run self-repair loop and inline node validation reduce breakage in visual workflows

What frustrates them

  • • Independent security scans rate it 65/100 risk with no agent identity or signing
  • • App Store reviewers repeatedly report the mobile app freezing or refusing to respond
  • • Community threads now treat it as the legacy agent framework, behind OpenClaw and CrewAI
  • • Lemmy and HN critics flag it as a 'passive LLM' primitive that hasn't evolved conceptually

Researched Oct 8, 2026

Frequently Asked Questions

Which is better, AutoGen or AutoGPT?

The best choice between AutoGen and AutoGPT depends on your specific use case — we compare them independently on features, current pricing, integrations, and real-world signals (with an on-demand sentiment scan available for each). See the side-by-side breakdown above to match them to your needs.

What are the main differences between AutoGen and AutoGPT?

The key differences include pricing model, feature set, platform support, and skill level requirements. Review the full comparison on RightAIChoice for a detailed breakdown.

Is there a free version of AutoGen or AutoGPT?

Check the pricing section in the comparison for the latest pricing details on both tools, including free tiers, trial options, and paid plans.

What else are AutoGen and AutoGPT compared with?

Where can you read more about AutoGen and AutoGPT?

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Analysis generated by our model pipeline from live data: