Decision comparison
AutoGen vs AutoGPT
AutoGen and AutoGPT come at autonomous agents from different directions. AutoGen is a Python library where agents converse to solve a task, including writing and running code, and it is now in community-managed maintenance mode with Microsoft's new work going to the Agent Framework. AutoGPT is a platform for building and running continuous agents, with a visual builder alongside code.
AutoGen has a named successor from its own vendor
Microsoft merged AutoGen and Semantic Kernel into the Microsoft Agent Framework. AutoGen is in maintenance mode: it receives no new features or enhancements and is community managed going forward. Microsoft directs new projects to the Agent Framework.
Architecture choice. These take different approaches to the same problem. Read the table as a fit question rather than a feature race.
Applies to: where teams bring their own LLM keys and infrastructure, with AutoGPT Cloud plans for hosted automation.
All 2 are agent frameworks.
Quick Comparison
| Decision factor | AutoGen | AutoGPT |
|---|---|---|
| What it is | Microsoft's multi-agent framework, where agents hold a conversation to solve a task, now in maintenance mode | A platform for building and running continuous autonomous agents, grown out of the original goal-driven agent project |
| Model of work | Agents exchange messages until a task is resolved, with group chat patterns for more than two | An agent pursues a goal through a loop of planning, acting and evaluating, run continuously |
| Interface | A Python library you write against | A platform with a visual builder alongside the underlying code |
| Code execution | Agents can write and run code in a sandbox as part of solving a task | Actions are performed through configured blocks and integrations |
| Vendor status | Community-managed maintenance mode; Microsoft's new work is in the Agent Framework | Actively developed as a platform product |
| Predictability | Conversation length and path vary between runs on the same input | Agent loops are goal-driven, so run length depends on how quickly the goal is met |
| Best fit | Python teams wanting conversational multi-agent patterns in code | Teams wanting continuously running agents built with less code |
AutoGen
- What it is:
- Microsoft's multi-agent framework, where agents hold a conversation to solve a task, now in maintenance mode
- Model of work:
- Agents exchange messages until a task is resolved, with group chat patterns for more than two
- Interface:
- A Python library you write against
- Code execution:
- Agents can write and run code in a sandbox as part of solving a task
- Vendor status:
- Community-managed maintenance mode; Microsoft's new work is in the Agent Framework
- Predictability:
- Conversation length and path vary between runs on the same input
- Best fit:
- Python teams wanting conversational multi-agent patterns in code
AutoGPT
- What it is:
- A platform for building and running continuous autonomous agents, grown out of the original goal-driven agent project
- Model of work:
- An agent pursues a goal through a loop of planning, acting and evaluating, run continuously
- Interface:
- A platform with a visual builder alongside the underlying code
- Code execution:
- Actions are performed through configured blocks and integrations
- Vendor status:
- Actively developed as a platform product
- Predictability:
- Agent loops are goal-driven, so run length depends on how quickly the goal is met
- Best fit:
- Teams wanting continuously running agents built with less code
Public signals
Verified factual signals only. Bars appear only for like-for-like metrics with five weekly assessments for every tool; missing evidence stays explicit. These signals do not establish enterprise adoption, product quality, or total cost.
| Metric | AutoGen | AutoGPT |
|---|---|---|
| GitHub commits, 90d(Product adoption) | 0 | 334 |
| GitHub stars(Product adoption) | 60,000+ | 187,000+ |
| Search interest(Market interest) | 1 | 1 |
| Hacker News mentions, 90d(Community interest) | 3 | 0 |
| PyPI weekly downloads(Product adoption) | 87.4k | Not available |
| Stack Overflow questions(Community interest) | 37 | 17 |
| Docker Hub pulls(Product adoption) | Not available | 4.4k |
As of September 14, 2026 — updated weekly.
Health & risk evidence
Observed public-source checks for mapped package versions and repositories.
AutoGen
September 14, 2026Package vulnerabilities
PyPI · autogen-agentchat@0.7.5
0 vulnerabilities
across 1 package
Repository security score
Not available
AutoGPT
Package vulnerabilities
Not available
Repository security score
Not available
Feature Comparison
| Feature | AutoGen | AutoGPT |
|---|---|---|
| Building | ||
| Python library | Full support | Full support |
| Visual or low-code builder | Not verified | Full support |
| Tool calling | Full support | Full support |
| Model-agnostic across providers | Full support | Full support |
| Coordination | ||
| Multiple agents on one task | Full support | Partial support |
| Agent-to-agent conversation | Full support | Partial support |
| Code generation and execution | Full support | Partial support |
| Human-in-the-loop steps | Full support | Full support |
| Operation | ||
| Continuous or scheduled running | Partial support | Full support |
| Hosted platform option | Not verified | Full support |
| Built-in step tracing | Partial support | Full support |
| Iteration limits | Full support | Full support |
| Status | ||
| Receiving new features | Not verified | Full support |
| Security and bug fixes | Partial support | Full support |
| Named vendor successor | Full support | Not verified |
| Large community | Full support | Full support |
Building
Python library
Visual or low-code builder
Tool calling
Model-agnostic across providers
Coordination
Multiple agents on one task
Agent-to-agent conversation
Code generation and execution
Human-in-the-loop steps
Operation
Continuous or scheduled running
Hosted platform option
Built-in step tracing
Iteration limits
Status
Receiving new features
Security and bug fixes
Named vendor successor
Large community
Which approach fits
AutoGen and AutoGPT come at autonomous agents from different directions. AutoGen is a Python library where agents converse to solve a task, including writing and running code, and it is now in community-managed maintenance mode with Microsoft's new work going to the Agent Framework. AutoGPT is a platform for building and running continuous agents, with a visual builder alongside code.
When each approach fits
Choose AutoGen if:
Choose AutoGen when conversational multi-agent patterns in Python are what you want and you are comfortable building on a framework in maintenance mode. Agents exchanging messages, group chat coordination and sandboxed code execution are well established, the API is stable precisely because it is no longer changing, and the installed base is large.
Choose AutoGPT if:
Choose AutoGPT when you want agents that run continuously and would rather assemble them than write a framework integration. The visual builder lowers the amount of code required, the platform handles scheduling and running agents over time, and step tracing is provided rather than added.
These scenarios reflect the available product evidence. Your requirements, existing stack, and team expertise should guide the final decision.
Frequently Asked Questions
What does maintenance mode mean for us?
Microsoft merged AutoGen and Semantic Kernel into the Microsoft Agent Framework, which reached 1.0 in April 2026. AutoGen is community-managed and receives no new features; Semantic Kernel v1.x continues to get critical bug and security fixes while new development goes to the Agent Framework. Both remain installable and both run in production today. The question that follows is how long you expect your code to live, and how much of it you would rewrite if you migrated.
Is an agent that writes and runs code safe to deploy?
Only inside a sandbox you control. Generated code should execute in a container with no network access, a CPU and memory limit, a read-only filesystem apart from a scratch directory, and a timeout. Treat the generated code as untrusted input, because that is exactly what it is. Frameworks provide sandbox integrations, but the isolation guarantees come from your infrastructure rather than the library.
How do we stop an agent looping forever?
Set a hard iteration ceiling and a token budget per run, and make exceeding either a terminal state that reports failure rather than a condition that retries. Goal-driven loops are the most expensive failure mode in this category, because a task the model cannot complete produces attempts indefinitely. Both frameworks support limits; the mistake is leaving them at generous defaults.
What do these need to run?
Python in most cases, an API key for a model provider, and somewhere to run the process — a container on Kubernetes, a serverless function, or a service behind a REST endpoint returning JSON. Retrieval adds a vector store, often PostgreSQL with pgvector. Agents that execute generated code need a sandbox, usually a Docker container with no network access and a CPU limit, which is the one genuinely unusual infrastructure requirement in this category.
What does running one of these in production require?
Tracing every step with inputs, outputs, latency and token cost; a hard ceiling on iterations so a loop that fails to converge stops rather than spends; retries that distinguish a transient API error from a model producing unusable output; and evaluation that catches quality regressions when a prompt or model changes. Frameworks provide different amounts of that, and the gap is where the engineering effort goes.
Should maintenance mode rule a framework out?
Not automatically. A stable API with a large installed base is a reasonable foundation for something you build once and run for years, and switching costs are real. It does argue against starting a long-lived project on it if the alternative is comparable, and it argues for keeping prompts, tool implementations and business logic in plain modules so framework wiring is the only thing a migration touches.