Best AI Agent Builders for Python - Page 2

Dify is an open-source platform designed to streamline the development and operation of generative AI applications. It offers a comprehensive suite of tools, including an intuitive orchestration studio for visual workflow design, a Prompt IDE for prompt testing and refinement, and enterprise-level LLMOps capabilities for monitoring and optimizing large language models. Dify supports integration with various LLMs, such as OpenAI's GPT series and open-source models like Llama, providing flexibility for developers to select models that best fit their needs. Additionally, its Backend-as-a-Service (BaaS) features enable seamless incorporation of AI functionalities into existing enterprise systems, facilitating the creation of AI-powered chatbots, document summarization tools, and virtual assistants.

PromptQL is a platform developed by Hasura that enables Large Language Models (LLMs) to access and interact with structured data sources through agentic query planning. This approach allows AI agents to retrieve and process data in a human-like manner, enhancing their ability to handle complex, real-world user queries. By providing LLMs with access to a Python runtime and a standardized SQL interface, PromptQL facilitates accurate data querying and manipulation. The platform supports integration with various data sources, including GitHub repositories and PostgreSQL databases, allowing users to build AI assistants tailored to their specific needs. PromptQL addresses the limitations of traditional search-based retrieval methods by enabling AI agents to perform tasks such as gathering relevant emails and classifying follow-ups with greater accuracy. Users can get started by connecting their data, adding their LLM API key, and building with AI.

Smolagents is an AI agent framework developed to simplify the creation and deployment of intelligent agents with minimal code. It supports code-first agents where agents execute Python code snippets to perform tasks, offering enhanced efficiency compared to traditional JSON-based approaches. Smolagents integrates with large language models like those from Hugging Face, OpenAI, and others, enabling developers to create agents that can control workflows, call functions, and interact with external systems. The framework is designed to be user-friendly, requiring only a few lines of code to define and execute agents. It features secure execution environments, such as sandboxed spaces, for safe code running. Smolagents also promotes collaboration by integrating deeply with the Hugging Face Hub, allowing users to share and import tools. It supports a variety of use cases, from simple tasks to multi-agent workflows, offering flexibility and performance improvements.

Dendrite is a framework-agnostic platform that empowers developers to create web-based tools for AI agents, enabling them to authenticate, interact with, and extract data from any website. By simulating human-like browsing behavior, Dendrite facilitates seamless web navigation and data retrieval for AI applications. The platform offers a Python SDK, providing developers with the necessary tools to build AI agents capable of performing tasks such as interacting with web elements and extracting information. Dendrite's flexibility allows it to integrate with any tech stack, making it a versatile solution for developers aiming to enhance their AI agents' web interaction capabilities. Your Dendrite client syncs with website authentication sessions in your local browser, no need to share or store login credentials. Use our Chrome Extension, Dendrite Vault, to securely share authentication sessions from your browser with the Dendrite client.

​Amazon Nova Act is an AI model designed to perform actions within web browsers, enabling the development of agents capable of completing tasks such as submitting out-of-office requests, scheduling calendar events, and setting up 'away from office' emails. Unlike traditional large language models that primarily generate natural language responses, Nova Act focuses on executing tasks in digital environments. The Nova Act SDK allows developers to decompose complex workflows into reliable atomic commands (e.g., search, checkout, answer questions about the screen) and incorporate detailed instructions where necessary. It also supports API calls and direct browser manipulation through Playwright to enhance reliability. Developers can integrate Python code, including tests, breakpoints, asserts, or thread pools for parallelization, to manage web page load times effectively.

Describe what you need in plain English. Scottie turns it into a working agent you can run on our cloud or export to your own hosting service. Join our waitlist today to secure your spot and get exclusive early access to premium features. Everything you need to build, test, and deploy AI agents in minutes. Pick from today's leading language models and switch anytime without rebuilding (OpenAI, Gemini, Anthropic, Llama, and more). Bring your company knowledge together from Slack, Google Drive, Notion, Confluence, GitHub, and more. Your data stays private and secure. Scottie supports models from all top vendors. Switch models anytime without rebuilding your agents. Scottie agents adapt to different roles and industries, operating exactly how you need them to. The AI tutor analyzes student responses, provides personalized feedback, and adapts difficulty based on performance.

Upsonic is an open source framework that simplifies AI agent development for business needs. It enables developers to build, manage, and deploy agents with integrated Model Context Protocol (MCP) tools across cloud and local environments. Upsonic reduces engineering effort by 60-70% with built-in reliability features and service client architecture. It offers a client-server architecture that isolates agent applications, keeping existing systems healthy and stateless. It provides more reliable agents, scalability, and a task-oriented structure needed for completing real-world cases. Upsonic supports autonomous agent characterization, allowing self-defined goals and backgrounds, and integrates computer-use capabilities for executing human-like tasks. With direct LLM call support, developers can access models without abstraction layers, completing agent tasks faster and more cost-effectively.

Airweave is an open source platform that transforms application data into agent-ready knowledge, enabling AI agents to semantically search across various apps, databases, and document stores. It simplifies the process of building intelligent agents by offering no-code solutions, instant data synchronization, and scalable deployment options. Users can connect their data sources using OAuth2, API keys, or database credentials, initiate data synchronization with minimal configuration, and provide agents with a unified search endpoint to access the necessary information. Airweave supports over 100 connectors, including integrations with Google Drive, Slack, Notion, Jira, GitHub, and Salesforce, allowing agents to access a wide range of data sources. It handles the entire data pipeline, from authentication and extraction to embedding and serving, automating tasks such as data ingestion, enrichment, mapping, and syncing to vector stores and graph databases.

String is the first text-to-agent platform by Pipedream that lets you prompt, run, edit, and deploy AI agents in seconds, no drag-and-drop canvases, just natural language commands that generate production-ready code. Backed by Pipedream’s five years of experience building thousands of app integrations and Internet-scale agent infrastructure, String connects to over 2,700 APIs and embeds 10,000+ tools with managed authentication, so your agents can solve ten times more use cases than typical no-code builders. The intuitive web interface guides you through creating agents that monitor GitHub issues and automate ticket creation, schedule meetings, analyze data, send Slack messages, post to social media, update databases, and much more, all with full audit trails, customizable queues, and built-in data stores. Real-time dashboards display agent performance, logs, and metrics, while built-in security and privacy controls (SOC 2 Type II, ISO 27001, HIPAA, GDPR) ensure safe operation.

Sim Studio is a powerful, AI-native platform for designing, testing, and deploying agentic workflows through an intuitive, Figma-like visual editor that eliminates boilerplate code and infrastructure overhead. Developers can immediately start building multi-agent applications with full control over system prompts, tool definitions, sampling parameters, and structured output formatting, while maintaining the flexibility to switch seamlessly among OpenAI, Anthropic, Claude, Llama, Gemini, and other LLM providers without refactoring. The platform supports full local development via Ollama integration for privacy and cost efficiency during prototyping, then enables scalable cloud deployment when you’re ready. Sim Studio connects your agents to existing tools and data sources in seconds, importing knowledge bases automatically and offering over 40 pre-built integrations.

Naptha is a modular AI platform for autonomous agents that empowers developers and researchers to build, deploy, and scale cooperative multi‑agent systems on the agentic web. Its core innovations include Agent Diversity, which continuously upgrades performance by orchestrating diverse models, tools, and architectures; Horizontal Scaling, which supports collaborative networks of millions of AI agents; Self‑Evolved AI, where agents learn and optimize themselves beyond human‑designed capabilities; and AI Agent Economies, which enable autonomous agents to generate useful goods and services. Naptha integrates seamlessly with popular frameworks and infrastructure, LangChain, AgentOps, CrewAI, IPFS, NVIDIA stacks, and more, via a Python SDK that upgrades existing agent frameworks with next‑generation enhancements. Developers can extend or publish reusable components on the Naptha Hub, run full agent stacks anywhere a container can execute on Naptha Nodes.