AI is taking over today’s tech world. Everyone needs to integrate AI into their business, develop AI applications, etc. I’m not here to explain what AI is, its subfields, or what an LLM is — none of that bla bla bla. Let’s get straight to the point. My topic is Generative AI and Agentic AI, specifically LLMs and Multi-Agent Systems (MAS). When developing LLM applications and MAS, we need to validate LLM outputs, monitor our agents, and integrate with various tools and services. I’ll share some useful tools and libraries for building these kinds of applications. Everyone knows about LangChain and LlamaIndex. So, I am not going to talk again about those two frameworks.
Instructor is used to get structured outputs (Type Safe) from LLMs. It is built on top of Pydantic. This library has the support for streaming responses, and it is available in Python, TypeScript, Go, Ruby and Elixir. Also, it can be used with many LLMs including self-hosted models. You can simply define a Pydantic model as a response model use it with the instructor to get the output as you need. Instructor also supports Jinja templates which allow you to create dynamic prompts. Also, this can be used with LogFire to monitor performance of the application. In their documentation you can find a bunch of examples of how to use the Instructor.
When building AI agents, we need tools that enhance their capabilities. Composio is a great option—an integration platform for AI agents that provides access to over 250 tools. It allows seamless integration with services like GitHub, Jira, Salesforce, Gmail, Shopify, and more. Composio supports various frameworks, including LangChain, LlamaIndex, CrewAI, AutoGen, and LangGraph, while remaining LLM-agnostic. It also offers authentication mechanisms such as OAuth and JWT. Additionally, Composio enables automatic action execution through its built-in triggers, making AI workflows more efficient and automated. Available in both free and paid plans.
When developing AI agents, it is crucial to monitor their performance, track token usage, and measure response times. AgentOps is an observability platform that enables you to monitor, debug, test, audit, and deploy your AI agents effectively. It supports various agent frameworks, including CrewAI, LlamaIndex, LangChain, and AutoGPT, along with multiple LLMs. AgentOps helps track token usage, monitor costs, and provides clear visualizations for better insights. Very easy to use. With two lines of code, you can add AgentOps to your application. It comes with a user-friendly interface and is available in both free and paid versions. Also, there are similar observability tools like LangFuse, LangTrace, etc.
Parlant lets you create and control AI Agent behaviors to suit your needs. You can define unique Guidelines for each agent, specifying how they act in different situations, set up a custom word dictionary for every agent, and create context-based variables to store data. If you’re building AI Agents for tasks like Customer Support, Parlant is worth considering. This framework also supports for tool calling agents which provide access to third party services and APIs. Parlant operates on an Event-Driven model, unlike the traditional request/response model. This approach makes conversations feel more natural, allowing agents to respond whenever needed. Security is also a strong point of Parlant. It uses Lakera Guard for jailbreak protection. While Parlant has a small but growing community, its capabilities make it a solid choice for AI-driven interactions. There is one drawback. You can change the LLM provider. But you can’t change which model to use. You have to use the model selected by Parlant developers. But they will include the ability to change model versions in the near future.
LM Studio gives you the ability to run LLMs locally. and supports for various LLMs. This is most like Ollama. But there are some differences. LM Studio comes with a GUI. You can explore models within the UI. Additionally, LM Studio includes a built-in chat feature and allows you to start a server with your chosen model in just one click. LM Studio is available for Windows, Linux and Mac OS. The tool can analyze your PC’s specifications and recommend the most suitable quantized version of a model. CrewAI also supports LM Studio.
LangFlow is an open-source, low-code tool designed to simplify the development of RAG applications, AI agents, and workflows that can interact with any API, model, or database. It provides an easy-to-use GUI with drag-and-drop components, allowing you to build LLM applications effortlessly. LangFlow can be used both locally and in the cloud. It supports integration with observability tools like LangSmith, LangFuse, and LangWatch, helping you monitor your applications. LangFlow also supports for many tools that can connect your app with useful third-party services like Google Drive, FireCrawl, Confluence, Serper, etc. To make getting started even easier, LangFlow includes pre-built templates, allowing you to quickly set up and launch your application.
Conclusion
Building AI applications, whether LLM-based or Multi-Agent Systems (MAS), requires the right tools for validation, monitoring, and integrations. AI is evolving very fast. Staying updated is crucial to gain its full potential. We should be aware of tools that can enhance our AI application development. Beyond the tools covered in this article, many others exist that can enhance AI development. Explore these options, experiment, and take your AI projects to the next level.