AutoGen
A framework for building AI agents and applications
TL;DR
AutoGen is an open-source framework from Microsoft Research for building conversational single- and multi-agent AI applications using LLMs. It's ideal for developers prototyping complex agentic workflows with Python. Key differentiator: Microsoft backing and strong support for multi-agent conversations with human-in-the-loop.
Our Take
AutoGen occupies a strong position in the open-source AI agent framework market, particularly for multi-agent systems, backed by Microsoft Research. Its primary value proposition is simplifying the creation of conversational agents that collaborate, making it easier to prototype sophisticated LLM applications without building everything from scratch. It stands out in analytical pipelines and software development tasks where agent interactions are key. Strengths include flexibility in multi-agent setups, human-in-the-loop support, and integrations like Docker for secure code execution. Users appreciate it for rapid prototyping and its no-revenue-pressure development due to Microsoft funding. However, it's often seen as a research prototype rather than production-ready, with complaints about high costs in testing (due to LLM calls), failures in complex chats, and less maturity compared to alternatives. Limitations include potential instability, steep learning curve for production, and reliance on external LLMs incurring costs. Review data is mostly from Reddit, lacking depth from enterprise review sites, suggesting early-stage adoption. Best suited for researchers, developers experimenting with multi-agent AI, or Microsoft-centric teams; less ideal for straightforward business automations needing fine control.
Pros
- + Excellent for prototyping multi-agent systems and conversational workflows.
- + Microsoft backing ensures ongoing development without revenue focus.
- + Strong human-in-the-loop and flexible conversation support.
- + Docker integration for secure code execution.
- + Useful in software development and analytical tasks.
Cons
- - Prototype/research stage, not fully production-ready.
- - High costs from LLM usage in testing/complex chats.
- - Frequent failures in group/multi-agent scenarios.
- - Lacks fine-grained control for business use cases.
- - Sparse professional reviews; mostly community feedback.
Sentiment Analysis
Limited formal reviews on professional sites like G2, Capterra, TrustRadius (none found for Microsoft Research AutoGen). On Reddit and X, mixed but leaning positive feedback highlights innovation in multi-agent AI, simplicity, potential for complex tasks, and strong community adoption; some concerns about maturity, prototype status, and recent drama around development.
Sentiment Over Time
By Source
50 mentions
Sample quotes (3)
- "I stumbled upon Microsoft's autogen a few days ago and was pretty taken by its potential."
- "Review: AutoGen framework from Microsoft. I've checked the documentation, watched the impressive demo."
- "Though Autogen has been widely adopted, it is still a prototype product from Microsoft Research."
20 mentions
Sample quotes (3)
- "The open-source Autogen from @Microsoft is cool. Its a framework for building AI agents."
- "Microsoft's AutoGen is the #1 framework for anyone building multi-agent applications."
- "The AutoGen drama is such a mess."
Screenshot
Features
Prompt Management
Editing and tracking of LLM prompts
Allows to version prompts and track / compare different variants over time
Compliance & Security
Security certifications, compliance features, and access control capabilities.
SOC 2 Type I or Type II certification.
ISO 27001 information security certification.
Built-in tools for GDPR compliance (data export, deletion, consent).
Complete audit log of all data changes.
Granular permissions based on user roles.
Single Sign-On integration support.
AI Engine Coverage
Coverage and support for various AI models, LLMs, and search engines.
List of AI models and LLMs supported for tracking (e.g., ChatGPT, Gemini).
How often metrics are updated (e.g., real-time, daily).
Support for tracking in multiple countries or regions.
Orchestration Capabilities
Core features for coordinating and executing AI agent workflows.
Supports orchestration of multiple collaborating agents.
Maintains agent state and memory across interactions.
Automatically routes requests across multiple LLM providers.
Supports agents calling external tools or functions.
Deployment & Scalability
Deployment models and scalability features for production use.
Primary way to deploy and run the orchestration.
Supports multiple teams or users from single deployment.
Automatic scaling for high-load agent workflows.
Compatible with serverless/serverless-like deployments.
Observability & Monitoring
Tools for tracking performance, costs, and debugging agent runs.
Monitors and budgets LLM usage costs per run.
Detailed traces of agent steps and decisions.
Visual graphs or dashboards of agent flows.
Metrics like latency, throughput for agent executions.
Developer Experience
Tools and abstractions easing agent development and iteration.
No-code/low-code UI for designing agent workflows.
OpenAI API-compatible endpoints or SDKs.
Available as open-source with community contributions.
Programming languages with official SDK support.
