9 AI Agent Tools for 2025
AI agents are one of the most transformative new technologies we’ve seen in decades. Because of this, teams in all industries are rushing to implement agentic solutions.
At the same time, a huge number of vendors have come to market with tools for building and managing AI agents.
However, these can vary greatly. On the one hand, there are highly developer-focused platforms and frameworks, while on the other, there are no-code tools, aimed at non-technical colleagues.
As well as this, we have to contend with dedicated platforms versus other types of SaaS tools that offer agentic functionality for their specific use cases. Picking through this to find the right solution for our needs can be incredibly challenging.
To help, we’re checking out some of the most popular AI agent tools on the market today, covering a range of options aimed at different kinds of users and applications.
Specifically, we’ll be covering:
Let’s jump right in.
What is an AI agent tool?
An AI agent is an LLM-powered software system that’s capable of autonomous reasoning and action in order to complete a specific task.
This works by exposing one or more AI models to tools, data, and other resources that enable it to interpret outputs, determine how to reach the user’s goal, and carry out actions based on this.
AI agent tools allow us to configure our own agentic solutions, largely based on pre-existing elements. For example, exposing models to our tools and data, as well as writing prompts to define what they can do with these.
The goal is to replicate and automate the kinds of workflows and decisions that would otherwise require a human colleague. In turn, this facilitates much deeper automation than would be possible with traditional, non-AI-based tools.
However, this also introduces new challenges and risks. Perhaps most importantly, there’s the issue of balancing autonomous decisions and actions with the need for control, oversight, and governance.
So, the most effective AI agent tools also provide tools for us to monitor and control agent behavior through tracing, observability, and human-in-the-loop capabilities.
Depending on the nature of the specific tool in question, they might also provide tools for helping us deploy our agents to production, or they may simply host the agents for us in the case of cloud-based services.
So…
What kinds of AI agent tools are available?
As we hinted at earlier, AI agent tools can span a variety of different target personas and use cases. For example, some platforms require a high level of technical expertise, whereas others are largely aimed at non-developers.
As such, before we start looking at specific options, it’s worth considering how we can break up the market, at least at a high level.
One way to do this is categorizing tools based on their core experience for creating agents. This includes:
- AI agent frameworks - code-based toolkits for building agents using pre-built, reusable elements.
- Low-code tools - combining elements of visual development with opportunities for us to add custom code, balancing flexibility and expediency.
- No-code tools - using visual experiences to enable us to build AI agents, without requiring any coding skills.
- Observability, monitoring, and management - tools that provide visibility, control, and insights into agent behavior, often with support for agents that have been built with varying tools and methods.
However, it’s important to note that a degree of grey area and overlap exists here. For instance, some frameworks might still incorporate elements of visual development, while agent builders of all kinds could provide some functionality for observability and monitoring.
What’s important is to understand our own more granular requirements in order to make an informed decision about which platforms are most likely to be suitable for particular, real-world use cases.
9 AI agent tools for 2025
Now that we have a better grasp of what AI agent tools are, what they offer, and how we can break up the market, it’s time to start looking at some specific platforms.
We’ve chosen ten tools across the four categories that we outlined a second ago. Our picks are:
Let’s check each one out in turn.
Agent frameworks
First, we’ll check out some frameworks for building AI agents. As we said earlier, these are code-based toolkits that empower developers to build highly customized AI agents. This works by providing reusable elements and functions that can be utilized to build our own solutions.
Here are three of the most prominent examples.
1. LangGraph
LangGraph, part of the LangChain ecosystem, is probably the best-known AI agent framework. As a highly flexible, open-source tool, it’s well-positioned to create advanced solutions, with ample scope for customization.
(LangChain Website)
At its core, LangGraph offers a library of tools for creating stateful, cyclical agent workflows using graph-based models. This essentially means we can create sophisticated LLM interactions, utilizing loops and branching logic.
In this context, a graph is made up of:
Nodes- including models, tools, data sources, and other resources.Edges- how we connect nodes, for instance, sequentially or conditionally executing actions.
On top of this, LangGraph offers a suite of tools for tasks such as orchestration and memory-handling. This also sits alongside the LangGraph Platform, which offers similar capabilities within a visual development and management solution.
2. AutoGen
Microsoft’s AutoGen is a suite of tools, including language frameworks, for creating AI applications and agents. It’s specifically aimed at developers who need to create collaborative, multi-agent systems.
(AutGen Website)
For instance, we might have a system comprising several specialized agents that we need to interact with each other, alongside human colleagues.
AutoGen utilizes an event-driven architecture, providing a scalable solution for creating long-running autonomous agents.
It’s made up of four distinct elements:
AutoGen Core- A framework for creating production multi-agent systems.AgentChat- A framework for prototyping single and multi-agent systems.AutoGen Studio- A web UI for prototyping agents without writing custom code.Extensions- Implementations of Core and AgentChat components that can interface with external tools and libraries.
3. Semantic Kernel
Next up, we have Semantic Kernel. Again, from Microsoft, this is an open-source SDK that enables developers to integrate AI services such as OpenAI or Hugging Face with conventional programming languages.
Essentially, it acts as middleware, giving us a reliable, efficient way to build and orchestrate applications involving AI models as well as traditional coding languages, such as Python, Java, or C#.
This opens up a range of possibilities for adding AI functions to existing code bases, as well as enabling us to develop AI agents.
Semantic Kernel also offers orchestration and planning tools, as well as prompt templates, response handling, and more, making it a great choice for complex enterprise use cases.
Low-code
Having seen some of the most popular AI agent frameworks, we can next begin to think about platforms that leverage more visual development experiences for creating agentic systems.
Here are two of the most prominent examples.
4. IBM WatsonX.AI
WatsonX.AI is IBM’s suite of tools for building AI solutions. This includes a range of interfaces, workflows, APIs, SDKs, and other tools for utilizing large language models within development projects.
(IBM Watson Wesbite)
As such, it offers a highly powerful platform for building a range of AI use cases, with or without custom code.
Watson supports a variety of foundation models, including IBM’s own offerings, third-party options, and open-source models from the likes of Hugging Face, enabling us to tailor solutions to granular requirements using domain-specific LLMs.
As we might expect from IBM, WatsonX.AI also offers a high level of enterprise-readiness, including tools for deploying and governing AI tools, making it easier to manage solutions on both private and public networks.
5. Langflow
Next up, we have one of the bigger names in the low-code AI agent builder space - Langflow. Langflow is a visual framework for building RAG tools and multi-agent systems.
(Langflow Website)
This is based around a visual flow editor that’s well optimized for teams that need a fast, flexible way to output custom AI solutions. This works by enabling us to configure and connect a range of components, such as prompts, LLMs, and data sources.
As a low-code option, Langflow also offers scope for more advanced customization. For instance, we can expand functionality by writing our own custom components using Python.
There’s also a dedicated Agent component, allowing us to utilize agents as steps within wider automation flows, positing Langflow as a strong option for AI-powered workflow automation solutions.
No-code
Having seen some of the key code-based and low-code AI agent tools, we can next start to think about some of the best options on the market for no-code AI agents.
As noted earlier, these aim to empower users to build agentic systems without needing to write any custom code. This opens agent development up to a wider range of personas - but may also sacrifice some flexibility and customizations.
6. Flowise
Flowise is another highly prominent name in the AI agent tools market. It utilizes an intuitive drag-and-drop interface to output working AI agents, making it a great fit for developers and users with less extensive technical abilities.
(Flowise Website)
It’s also a strong offering for integration options, with support for over 100 LLMs, embeddings, and vector databases, as well as pre-built MCP tools for empowering agents to interact with external systems.
However, its no-code nature doesn’t mean that Flowise isn’t a strong offering for controlling and observing agent behavior. It offers execution traces and human-in-the-loop feedback tools, making it a viable option for a huge range of use cases.
On top of this, Flowise offers APIs, embedded widgets, and Python and TypeScript SDKs, allowing users with higher levels of development knowledge to expand on its existing functionality.
7. Zapier
Zapier has long been one of the biggest names in the world of automation, offering a streamlined, effective experience for connecting a huge range of tools and data sources within workflows.
(Zapier Website)
In recent years, it’s also become one of the leading no-code AI agent tools. Notably, we can create agents using natural language instructions, connecting to Zapier’s library of thousands of native integrations.
We can simply explain what we’d like an agent to do, and Zapier will use this to define the specific steps and tools that are required to meet our goals. Importantly, we can also provide feedback with natural language, helping to refine and improve our agents.
Zapier provides over 7,000 integration options, as well as a rich library of agent templates, making it one of the fastest, easiest ways to create working systems that can interact with real data.
Observability, monitoring, and management
Lastly, we can consider some AI agent tools that are less focused on building solutions, aiming instead to help us deploy, manage, or monitor live agentic systems.
While most agent builders offer some level of functionality under this umbrella, many teams also opt for standalone observability or orchestration tools, either for more advanced capabilities or to handle agents that have been built in multiple platforms.
8. LangSmith
Part of the LangChain ecosystem, LangSmith is a unified observability platform for testing, debugging, and monitoring AI apps, whether they’ve been developed with LangChain tools or other platforms.
(LangChain Website)
In large part, this centers around a set of tools and interfaces for tracing and debugging non-deterministic behavior, enabling us to quickly and easily see what agents are doing.
On top of this, LangSmith offers helpful capabilities for monitoring agent performance, including dashboards, gathering human feedback, and LLM-as-judge evaluators based on traces.
LangSmith also offers a set of tools for creating and improving prompts. We can use Playground to experiment with prompts and models, comparing responses, while the Prompt Canvas UI enables teammates to recommend and improve prompts.
9. LangFuse
Lastly, we have LangFuse. This is an open-source LLM engineering platform, offering a range of capabilities including traces, evals, prompt management, and debugging for LLM applications.
(LangFuse Website)
LangFuse offers extensive connectivity for a wide range of applications, via Python and TypeScript SDKs, native integrations for popular libraries, and support for OpenTelemetry.
Once we’ve connected our tools, LangFuse presents detailed information in an intuitive, navigable set of UIs, making it a good choice for teams that need effective insights into how AI systems are working in production.
The prompt management features are also well-optimized for production use cases, including version control, change tracking, and testing tools.
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