Agent Development Kits for Artificial Intelligence

Mozilla launches any-agent to unify the fragmented AI agent development landscape Mozilla has released any-agent, a unified interface that consolidates seven major AI agent frameworks under a single development environment. The tool supports Agno, Google ADK, LangChain, LlamaIndex, OpenAI Agents SDK, Smolagents, and TinyAgent through standardized configuration changes. Agent development teams can now build once and switch between frameworks without code rewrites. any-agent standardizes trace formatting across all supported frameworks using GenAI open telemetry standards, enabling direct performance comparisons and failure analysis that were previously impossible due to inconsistent logging approaches. The platform integrates Model Context Protocol (MCP) and Agent2Agent capabilities, positioning it as infrastructure for interconnected agent systems. Built-in evaluation methods leverage standardized tracing to identify framework-specific performance characteristics and failure patterns. This consolidation addresses a critical pain point in enterprise AI deployment where teams often commit to single frameworks without adequate comparison data. Organizations can now evaluate agent performance across multiple frameworks using consistent metrics, reducing vendor lock-in risks while accelerating development cycles through framework-agnostic tooling. 🔗https://lnkd.in/eS4aM9ec

With 4,000 stars on GitHub, this YC-backed startup is making waves with an open-source framework that automates operational workflows with LLM-powered agents. Superagent empowers developers to enhance their applications with robust LLM-powered AI assistants. Imagine a customer support workflow. With Superagent, an agent, could access various data sources like FAQs, product manuals, and customer data in databases to provide accurate and contextually relevant responses. The memory feature ensures the conversation context is maintained, enhancing customer experience. For inquiries requiring more sophisticated handling, the workflow feature can route the conversation to human agents (using the "hand-off" feature) or escalate it through a sequence of increasingly sophisticated AI agents. This system can significantly reduce response times, improve customer satisfaction, and decrease operational costs. Highlights: (1) Ingest various data sources, including PDFs, CSVs, Airtable, and YouTube videos (2) Execute different actions, from searching on Bing, to generating speech from text, calling a custom function, or hitting a Zappier endpoint (3) Features different generative models such as OpenAI’s GPT, Mixtral, or Stable Diffusion (4) Integrates with known vector bases such as Pinecone, Weaviate, and Supabase (5) Supports Langfuse and LangSmith for LLM observability (cost, latency, etc.) It is fully open-source and has Python + Node/Typescript SDKs. Superagent GitHub repo https://lnkd.in/gKrMq-sQ I recently wrote about the rise of autonomous agents and how packages like Superagent facilitate such a change https://lnkd.in/gNsKaeA4

Feeling overwhelmed by the flood of AI news and new tools? You're not alone. The AI ecosystem isn’t just evolving. It’s exploding. New applications are reshaping how we work in real time. Over the past few months, I’ve watched my own workflows (and those of many peers) transform, boosting productivity with tools that didn’t even exist a year ago. To help make sense of this fast-moving landscape, I’ve categorized a list of curated AI tools based on relevant use and application. I’ve personally explored the majority of these. Some are now part of my daily workflows, and it’s been incredible to see how they’re changing the way we strategize, plan, and execute. 𝟭. 𝗦𝗺𝗮𝗿𝘁𝗲𝗿 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻 𝗧𝗼𝗼𝗹𝘀 We all know ChatGPT, but there’s a growing family of conversational AIs that generate contextual content with impressive strength. 𝗘𝘅: ChatGPT (OpenAI), Claude (Anthropic), Gemini (Google), Grok (X) 𝟮. 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 & 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗧𝗼𝗼𝗹𝘀 These tools excel at finding, summarizing, and structuring insights. Think of them as your on-demand research or organizing assistants. 𝗘𝘅: Perplexity, DeepResearch by OpenAI, Google NotebookLM, Notion AI 𝟯. 𝗖𝗿𝗲𝗮𝘁𝗶𝘃𝗲 𝗧𝗼𝗼𝗹𝘀 For image, video, and audio generation, these tools unlock stunning creative control with just a prompt. 𝗘𝘅: Midjourney, DALL·E, Adobe Firefly, Figma, HeyGen, Google Veo, Gamma 𝟰. 𝗩𝗶𝗯𝗲 𝗖𝗼𝗱𝗶𝗻𝗴 𝗧𝗼𝗼𝗹𝘀 My personal favorite: These tools turn ideas into visual drafts in minutes. From code to UI mockups, they help teams move from debate to decisions to momentum faster. They turn abstract ideas into visual drafts, backed by supporting code. 𝗘𝘅: Replit, Lovable, V0, Cursor 𝟱. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗦𝘁𝘂𝗱𝗶𝗼𝘀 Empower developers and non-developers to create custom AI agents and automate workflows, without writing code. 𝗘𝘅: MindStudio, n8n, Lindy, Langflow, Crew.ai, LangGraph 𝟲. 𝗔𝗜 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗦𝗗𝗞𝘀 Full IDE development frameworks, from SOPs to prompt templates to orchestration, deployment, and monitoring capabilities. 𝗘𝘅: LangChain, LlamaIndex, Autogen, MCP, A2A 𝟳. 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 Industry-specific or enterprise tools embedded into business applications to build intelligent agents for tasks like case summaries, lead scoring, knowledge agents, and more. 𝗘𝘅: Salesforce AgentForce, Microsoft Copilot for Business, Writer, You.com I’m still learning and exploring, but many of these are now baked into my daily work. And the more I explore, the more value I find. What else would you add to this list? ___ If you’re curious to see these tools in action and want to try building your own AI agents (no coding needed!), come join us. We’re hosting a 𝗵𝗮𝗻𝗱𝘀-𝗼𝗻 𝗔𝗜 𝗕𝘂𝗶𝗹𝗱𝗲𝗿 𝗪𝗼𝗿𝗸𝘀𝗵𝗼𝗽 𝗼𝗻 𝗙𝗿𝗶𝗱𝗮𝘆, 𝗔𝘂𝗴𝘂𝘀𝘁 𝟭𝘀𝘁, where we’ve distilled months of AI learning into just 4 hours! Check out the details here - https://lnkd.in/eMU6nFJV

Carnegie Mellon University researchers have developed AgentKit, a new AI framework that allows the creation of AI agents through natural language, making the process more accessible and flexible. Unlike traditional methods that rely heavily on coding, AgentKit uses a graph-based design where tasks are defined as nodes in a directed acyclic graph, facilitating intuitive task management and real-time adjustments. The framework has shown promising results in simulations, significantly enhancing task efficiency and adaptability.