Iโve put together a list of AI Agent tools & frameworks.
Let me know if you know more.
Thread: ๐งต
1/ PydanticAI is a Python agent framework combining type safety with FastAPI-like features. Its model-agnostic design lets you easily switch between different LLMs.
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2/ CrewAI is one of the most popular AI tools, well known for multi-agent collaboration.
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3/ n8n is a powerful visual workflow builder. It has over hundreds of integrations to enhance your AI agent nodes visually.
4/ Microsoft's AutoGen is another open-source programming framework that helps create multi-agent AI apps.
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5/ Developed by Huggingface, Smolagents helps make powerful agents with simple tools, supporting various environments and language models.
It's goal is to create agents with only a few lines of code.
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6/ Phidata helps build smart agents with memory, tools, and a user-friendly interface. They have a really nice website and well structured documentation.
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7/ AgentOps helps developers create, test, and manage AI agents easily and securely.
It doesn't have a standalone user interface (UI), but it incorporates interfaces for monitoring, deployment, configuration and troubleshooting.
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8/ AI SDK by vercel offers the fundamental components to build AI Agents. It integrates well with the popular ai-sdk
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9/ Rig is a tool built on Rust that helps you build powerful AI Agents.
It's very beginner-friendly.
You can use it with many different AI models and easily connect it to other tools.
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10/ Eliza is a simple AI tool that helps create smart, independent agents that act consistently on different channels.
It has capabilities like document processing and media analysis, and it can be easily expanded with TypeScript support.
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11/ LangGraph helps deploy smart applications using language models, offering cycles, control, and persistence.
It's built on top of LangChain, using the capabilities of LangChain to build more complex and advanced projects.
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12/ Mem0 is a tool for AI Agents that improves memory and personalizes interactions.
It saves important details from user interactions and context like location, device type, and activity to give more personalized assistance.
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๐ต TypeScript
๐ก JavaScript
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PraisonAI is a powerful Multi-Agent Framework that combines PraisonAI Agents, AutoGen, and CrewAI into a low-code solution for building autonomous AI systems
๐ Python
AgentScript
A TypeScript framework for building reliable AI agents, using a custom AST runtime for code-driven planning and execution.
Benefits
- Deterministic task execution
- fine-grained control (stop/start)
- Native state persistence
- Seamless human-in-the-loop workflows.
- enhanced observability.
๐ต Typescript
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AI Prompts (Cursor Rules/Prompts) has already its small ecosystem:
โ Cursor Extension live (Free)
โ Web Directory incl. Search / Filter (Copy Paste)
โ Open Source on Github
๐ Create your own Rules WIP
๐ MCP Server WIP
... more
MCP (Model Context Protocol) is getting tons of attention on the tech side.
To really understand its impact, we need to see how Tool Calling Agents can make a big difference for companies and especially their non-tech employees in the future.
Thread ๐งต
1/ Scattered tool use
Most AI interactions today happen separately. You ask one AI tool for one specific thing. And honestly, even this isn't common yet. Usually, people still have to open multiple tabs or tools, paste data back and forth, and manually search through apps like Slack, HubSpot, or GitHub.
And that's messy. Data and answers get scattered across different platforms. This leads to confusion and bigger expenses.
So, right now, working with multiple isolated AI tools is slow, expensive, and frustrating.
2/ Natural language and one chat app.
That's why ChatGPT or Claude feel easy. Multiple Saas Tools (CRM, ATS, CMS, name it!) or multiple AI assistants add a lot of complexity.
The goal should be a single chat that calls them silently. This isn't MCP's main job, although it can lead in this direction as it handles all these tool connections in a smooth way. (See Claude Desktop)