I Tried AgentOps (an AI Tool) for My AI Projects, and It Blew My Mind
One of the best AI tools for companies & startups developing AI solutions.
Let’s be honest, everyone knows that AI is the future.
We can clearly see that.
More and more people are learning about AI, top companies are working in the AI space, building AI tools to help individuals, and even getting massive investments.
We also have many new startups building AI tools to solve specific problems for the world.
But let’s be honest, building an AI tool is not an easy process.
I’ve tried it myself and talked with multiple startups that keep facing issues while building their AI tools.
So today, I want to talk about an AI tool that can help startups in this process.
I’m talking about AgentOps.
This isn’t just a tool; it’s like having an extra set of hands (or a brain) to keep track of what your AI programs are doing.
Note: This post is from my newsletter, AI Made Simple, where I share real-world examples, step-by-step guides, and insights on how to use AI tools. You can subscribe to it here; it’s FREE.
With that said, let’s get into it.
What is AgentOps, and how do you get started?
Well, AgentOps is a tool that helps developers keep track of what their AI programs (agents) are doing. It automatically records actions like using AI models, calling tools, and logging errors.
And then all this data is shown on a dashboard, making it easier to understand how well the AI is working, find problems, and improve performance.
You just need to add 2 lines of code, and AgentOps does the rest.
So, how do you get started?
You need to visit their website and click on the “Get Started” button.
After that, sign in with Google/GitHub/Magic or by entering your email address.
How to Use AgentOps?
After creating an account, you will be redirected to the “Projects” tab, where you can create your own project.
Next, you’ll see a variety of templates to help you get started.
These templates include code examples for building a simple or multi-agent system, even code examples about how to use AgentOps with Langchain or Crew AI.
But in simple terms, you only need to write two lines of code to use AgentOps.
Here’s how:
1. First, install the AgentOps SDK in your AI project.
pip install agentops2. And then add just two lines of code (to use AgentOps):
import agentops
agentops.init(<INSERT YOUR API KEY HERE>)3. Lastly, run your AI project, you can view the specific data in your AgentOps dashboard, making it easy to visualize your agent’s behavior.
The best part?
You can easily connect AgentOps with frameworks like CrewAI, Autogen, LangChain, Cohere, LiteLLM, and MultiOn.
Here’s a quickstart guide to get started and here are some of the best examples to assist you in the process.
What are its features?
I need to tell you, the team behind AgentOps is working great.
When I was reading about their features, I was surprised by the way they think and have implemented these insane features for users.
Here are some of the best ones:
a) Auto-Instrumentation of LLM calls: After initializing AgentOps, the SDK automatically detects and instruments calls to installed LLM (large language model) providers like GPT, Claude, Gemini, and others.
This automation allows developers to track every interaction visually between their application and the LLM provider without additional code.
b) Comprehensive session management: AgentOps organizes every interaction and event within a session, encapsulating all actions, LLM calls, and agent activities under one execution instance.
And then each session records attributes like session ID, start and end timestamps, success or failure status, and optional tags for easier categorization and retrieval.
Also, this tool supports both single-session and multi-session modes, allowing asynchronous management and seamless scalability for complex projects.
c) Session Waterfall dashboard: They even provide a feature called Session Waterfall on the AgentOps dashboard which offers a timeline view of every LLM call, action, and error in a session.
It’s especially useful for debugging — users can see exactly when each action occurred, review the specific inputs and outputs, and identify failures at a glance.
d) Error handling and logging: AgentOps can be helpful to capture errors across all event types, enabling precise debugging.
Each error is logged with specific details like error type, error code, timestamp, and stack trace.
For each ErrorEvent, users can see the triggering event, making it straightforward to trace the error’s origin and resolve it efficiently.
Well, they have more such useful features, you can read more at the docs.
The pricing model
Now, let’s talk about the pricing.
As now we know from the features that it’s more than just a simple tool — it’s something that can help literally everyone who is building AI tools.
And the best part? You can get started for free.
As you can see, it’s free up to 1,000 events, along with some features.
So, one can easily try it out and see how useful it can be.
Do you really need AgentOps?
Well, it depends.
If you’re building an AI tool and want to visualize each and every details about your AI tool, AgentOps is a must.
You can visually find the errors and solve it in no time, and get the complete info.
After trying it myself, I can confidently say it’s one of the best AI tools for companies and startups developing AI solutions.
What’s even better?
In the upcoming years, we are going to see tons of AI agents that will help individuals complete their work.
As individuals/businesses have websites, we will have multiple agents.
And these AI agents will be useful for almost everything, from helping in marketing and sales to managing workflows, automating specific processes, and more.
Even OpenAI and Anthropic are planning to release AI agents, so I think for AI startups, AgentOps is one of the best solutions to manage their AI agents.
That’s all for now.
Hope you like it.
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