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AgentOps, the Best Tool for AutoGen Agent Observability

AgentOps and AutoGen

TL;DR

  • AutoGen® offers detailed multi-agent observability with AgentOps.
  • AgentOps offers the best experience for developers building with AutoGen in just two lines of code.
  • Enterprises can now trust AutoGen in production with detailed monitoring and logging from AgentOps.

AutoGen is excited to announce an integration with AgentOps, the industry leader in agent observability and compliance. Back in February, Bloomberg declared 2024 the year of AI Agents. And it's true! We've seen AI transform from simplistic chatbots to autonomously making decisions and completing tasks on a user's behalf.

However, as with most new technologies, companies and engineering teams can be slow to develop processes and best practices. One part of the agent workflow we're betting on is the importance of observability. Letting your agents run wild might work for a hobby project, but if you're building enterprise-grade agents for production, it's crucial to understand where your agents are succeeding and failing. Observability isn't just an option; it's a requirement.

As agents evolve into even more powerful and complex tools, you should view them increasingly as tools designed to augment your team's capabilities. Agents will take on more prominent roles and responsibilities, take action, and provide immense value. However, this means you must monitor your agents the same way a good manager maintains visibility over their personnel. AgentOps offers developers observability for debugging and detecting failures. It provides the tools to monitor all the key metrics your agents use in one easy-to-read dashboard. Monitoring is more than just a “nice to have”; it's a critical component for any team looking to build and scale AI agents.

  • in AutoGen
  • 5 min read

FSM Group Chat -- User-specified agent transitions

FSM Group Chat

Finite State Machine (FSM) Group Chat allows the user to constrain agent transitions.

TL;DR

Recently, FSM Group Chat is released that allows the user to input a transition graph to constrain agent transitions. This is useful as the number of agents increases because the number of transition pairs (N choose 2 combinations) increases exponentially increasing the risk of sub-optimal transitions, which leads to wastage of tokens and/or poor outcomes.

  • in AutoGen
  • 5 min read

AutoGen with Custom Models: Empowering Users to Use Their Own Inference Mechanism

TL;DR

AutoGen now supports custom models! This feature empowers users to define and load their own models, allowing for a more flexible and personalized inference mechanism. By adhering to a specific protocol, you can integrate your custom model for use with AutoGen and respond to prompts any way needed by using any model/API call/hardcoded response you want.

NOTE: Depending on what model you use, you may need to play with the default prompts of the Agent's

  • in AutoGen
  • 6 min read

AutoGenBench -- A Tool for Measuring and Evaluating AutoGen Agents

AutoGenBench

AutoGenBench is a standalone tool for evaluating AutoGen agents and workflows on common benchmarks.

TL;DR

Today we are releasing AutoGenBench - a tool for evaluating AutoGen agents and workflows on established LLM and agentic benchmarks.

AutoGenBench is a standalone command line tool, installable from PyPI, which handles downloading, configuring, running, and reporting supported benchmarks. AutoGenBench works best when run alongside Docker, since it uses Docker to isolate tests from one another.

  • in AutoGen
  • 2 min read

Code execution is now by default inside docker container

TL;DR

AutoGen 0.2.8 enhances operational safety by making 'code execution inside a Docker container' the default setting, focusing on informing users about its operations and empowering them to make informed decisions regarding code execution.

The new release introduces a breaking change where the use_docker argument is set to True by default in code executing agents. This change underscores our commitment to prioritizing security and safety in AutoGen.

  • in AutoGen
  • 7 min read

All About Agent Descriptions

TL;DR

AutoGen 0.2.2 introduces a description field to ConversableAgent (and all subclasses), and changes GroupChat so that it uses agent descriptions rather than system_messages when choosing which agents should speak next.

This is expected to simplify GroupChat’s job, improve orchestration, and make it easier to implement new GroupChat or GroupChat-like alternatives.

If you are a developer, and things were already working well for you, no action is needed -- backward compatibility is ensured because the description field defaults to the system_message when no description is provided.

However, if you were struggling with getting GroupChat to work, you can now try updating the description field.

AutoGen Studio: Interactively Explore Multi-Agent Workflows

AutoGen Studio Playground View: Solving a task with multiple agents that generate a pdf document with images.

AutoGen Studio: Solving a task with multiple agents that generate a pdf document with images.

TL;DR

To help you rapidly prototype multi-agent solutions for your tasks, we are introducing AutoGen Studio, an interface powered by AutoGen. It allows you to:

  • Declaratively define and modify agents and multi-agent workflows through a point and click, drag and drop interface (e.g., you can select the parameters of two agents that will communicate to solve your task).
  • Use our UI to create chat sessions with the specified agents and view results (e.g., view chat history, generated files, and time taken).
  • Explicitly add skills to your agents and accomplish more tasks.
  • Publish your sessions to a local gallery.

See the official AutoGen Studio documentation here for more details.

AutoGen Studio is open source code here, and can be installed via pip. Give it a try!

pip install autogenstudio