Agent Development Kits for Artificial Intelligence

Google Agent Development Kit (ADK) - yet another agent framework (YAAF 🤔) ? I know what you're thinking - does the world really need another agent framework? Well, let me try to make a case for it 🙃 First off, please check out the demo below from Franziska Hinkelmann, Ph.D. from Google's Cloud Next Developer Keynote. She builds a pretty impressive proposal agent live on stage, and in doing so, really shows off some of ADK's highlight features: ⚙️ 𝙎𝙩𝙖𝙣𝙙𝙖𝙧𝙙𝙞𝙨𝙚𝙙 𝙏𝙤𝙤𝙡 𝙄𝙣𝙩𝙚𝙜𝙧𝙖𝙩𝙞𝙤𝙣 In the demo, Fran defines an agent using LLMAgent, letting Gemini handle the reasoning. But ADK doesn't lock you into just LLM-driven decisions. It also provides WorkflowAgents (for strict sequences, loops, parallel tasks) and CustomAgents where you write the exact Python orchestration logic. This is key because it lets you blend deterministic control (vital for reliable business processes) with AI flexibility, offering a level of precision that's sometimes harder to guarantee when only prompting an LLM for complex flows. 🔌 𝙏𝙤𝙤𝙡 𝙪𝙨𝙚 In the demo Fran highlights the ADK's built-in support for Anthropic's Model Context Protocol (MCP). The agent uses MCP to perform RAG against a private database of building codes via Google's open-source MCP Toolbox for Databases. This shows ADK integrating with standardised protocols for robust connections to our own data and APIs, alongside its built-in tools (like Google Search, Code Execution) and wrappers for tools from LangChain/CrewAI. ✨ 𝘿𝙚𝙫𝙚𝙡𝙤𝙥𝙚𝙧 𝙀𝙭𝙥𝙚𝙧𝙞𝙚𝙣𝙘𝙚 (𝘿𝙓) ADK comes with a neat local Dev UI which is more than just a chatbot – it can be used for testing, inspecting events, and handles multimodal input by uploading both text and a floor plan image. Behind the scenes, ADK provides integrated concepts like SessionService (handling conversation state/history) and ArtifactService (managing files, like the PDF the agent generated), giving you essential application plumbing out-of-the-box. ☁️ 𝘿𝙚𝙥𝙡𝙤𝙮𝙢𝙚𝙣𝙩 Fran's initial diagram shows ADK within Vertex AI Agent Engine. ADK is designed with production on Google Cloud in mind, offering easy deployment paths to managed services like Agent Engine or Cloud Run. But critically, it also provides the flexibility for standard Docker/Kubernetes support if you prefer self-deployment. If you're building complex agents where precision, integration, and a code-first approach matter, ADK offers some compelling advantages worth exploring.

Google just released a full-stack toolkit for building AI agents.. and it’s a big deal. 🚀 Until now, building production-grade agents has felt like duct-taping together libraries: One for logic, another for tools, and almost nothing for evaluation or deployment. That changes with Google’s new open-source Agent Development Kit (ADK), an end-to-end operating system for building, testing, and shipping intelligent agents. Here’s why this release stands out: 🔧 Code-first, developer-focused Built for serious devs who need version control, custom logic, and robust testing. 🤖 Multi-agent, by design Easily spin up systems where agents collaborate or specialize across tasks—right out of the box. 🧪 Goes beyond building Most frameworks stop at the prototype. ADK includes tools for evaluating performance and deploying workflows into production. 🧩 Flexible orchestration Define custom flows using built-in agents, or wire up your own with dynamic routing logic. 💻 Great local dev experience CLI + Web UI make it easy to build, test, and debug your agents locally—before pushing to prod. Bonus: It’s cloud-friendly (of course it works well with Google Cloud), but supports any third-party models and tools, so you’re not locked in. To get started: pip install google-adk GitHub repo is linked in the comments👇

🎯 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗿𝗲𝗮𝗱𝘆 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀? IBM Research just dropped 𝗔𝗟𝗧𝗞, a game-changing toolkit that transforms brittle demos into enterprise-grade solutions. Let’s face it: Most AI agents work perfectly in demos but crumble in production. They hallucinate tool calls, fail silently, and struggle with real-world complexity. That’s where 𝗔𝗟𝗧𝗞 (𝗔𝗴𝗲𝗻𝘁 𝗟𝗶𝗳𝗲𝗰𝘆𝗰𝗹𝗲 𝗧𝗼𝗼𝗹𝗸𝗶𝘁) steps in. 𝗔𝗟𝗧𝗞 is a modular, open-source toolkit that enhances agents across their entire lifecycle, from reasoning to tool execution to output validation, without locking you into any specific framework. Key capabilities that make ALTK indispensable: → Spotlight focuses LLM attention on critical instructions for better reasoning → SPARC performs semantic pre-execution analysis to validate tool arguments before they run → Silent Review catches those sneaky tool errors that would otherwise go unnoticed → JSON Processor dynamically generates code to parse massive API responses → RAG Repair recovers gracefully from failed tool calls using domain knowledge What sets 𝗔𝗟𝗧𝗞 apart is its seamless integration with existing workflows. Through the ContextForge MCP Gateway, you can configure components externally without touching your agent code. Works beautifully with Langflow for visual development too. The implementation is refreshingly simple with just three steps: prepare input, instantiate component, process payload. No complex rewrites needed. 𝗔𝗟𝗧𝗞 is completely open-source, with IBM Research inviting the community to extend, remix, and evolve the toolkit. IBM This isn’t just another framework, it’s the missing reliability layer your agents need to go from prototype to production. 𝗥𝗲𝗮𝗱𝘆 𝘁𝗼 𝗺𝗮𝗸𝗲 𝘆𝗼𝘂𝗿 𝗮𝗴𝗲𝗻𝘁𝘀 𝗯𝘂𝗹𝗹𝗲𝘁𝗽𝗿𝗼𝗼𝗳? 📖 Deep dive into the research: https://lnkd.in/ekq4UKKa 💻 Get the code on GitHub: https://lnkd.in/eYWuk8yn

🚀 Are you facing challenges moving your AI Agents from POC to Production? You might want to explore ADK Over the past few months, I’ve been exploring how AI agents are moving from isolated demos to real, production-grade systems. What’s making that possible is the emergence of a new foundational layer, the Agent Development Kit (ADK), supported by the Model Context Protocol (MCP) and Agent-to-Agent (A2A) communication. The easiest way to understand this is, like how SDKs helped developers build software applications, ADK does something similar for AI, but instead of building apps, you’re building agents that can act, collaborate, and reason across systems. And the real benefit comes when you combine ADK with: 👉 MCP, that ensures that agents retain and share context consistently, even as they move between models or tasks. 👉 A2A, that allows these agents to talk to each other, passing insights, delegating work, and completing workflows together. I believe that this combination of ADK + MCP + A2A is quietly redefining how multi-agent systems are designed. More importantly, it’s helping teams move proof-of-concepts to production faster and at lower cost, because the orchestration, context management, and communication layers are already built in. Let us take a simple example, hiring automation for recruitment, to explain this. With ADK, you can design specialized agents for each stage: resume screening, interview scheduling, feedback collection, and offer generation. ◾ ADK provides the structure to build and orchestrate these agents. ◾ MCP ensures every agent operates with shared context such as a candidate’s details, interview notes, and communication history. ◾ A2A enables these agents to coordinate, for example, the screening agent handing shortlisted candidates to the scheduling agent automatically. What you get is not another chatbot but a coordinated, context-aware system that handles repetitive work autonomously, freeing recruiters to focus on decisions that matter. And because this setup sits on a well-defined architecture, teams can transition from POC to production with far less engineering overhead and significantly lower cost than traditional one-off integrations. I write about #artificialintelligence | #technology | #startups | #mentoring | #leadership | #financialindependence   PS: All views are personal Vignesh Kumar

🤖 Building AI agents is no longer a monolith! Many developers believe creating powerful AI agents means being locked into a single ecosystem. The reality is a rapidly expanding universe of specialized frameworks designed for interoperability, especially with the rise of standards like MCP (Multi-agent Conversation Protocol). Here’s a look at the diverse toolkit available for building next-gen AI agents: ✅ Ecosystem-Specific SDKs: Major players are providing native tools to build on their platforms, including the OpenAI SDK, Vercel AI SDK, and Google ADK. ✅ Open-Source Powerhouses: Frameworks like Langchain, Semantic Kernel, and Praison AI offer incredible flexibility and community support for orchestrating complex agentic workflows. ✅ Standard-Driven Development: Dedicated MCP SDKs for Python and TypeScript, along with frameworks like Lastmile MCP Agent, are pushing for a future where agents can communicate seamlessly, regardless of how they were built. ✅ Specialized Tooling: From Composio for integrating tools to Copilotkit for frontend support, the ecosystem is filling every niche to accelerate development. Takeaway: The future isn't about building one master agent, but orchestrating a team of specialized agents that work together effectively. What frameworks are you experimenting with for your AI agents? Share your favourites below! #AI #GenerativeAI #AIAgents #LLMs #MCP #DeveloperTools #OpenAI #Langchain 👉 Follow Sarveshwaran Rajagopal for more insights on AI, LLMs & GenAI.

Google’s Agent Development Kit (ADK) - an open-source, flexible framework designed to simplify the creation and deployment of AI agents and multi-agent systems. With ADK, developers can design intelligent agents that think, act, and coordinate, whether it’s for conversational assistants, automation, or complex multi-step workflows. What sets ADK apart is its model-agnostic and deployment-agnostic design. Though optimized for Google’s Gemini and cloud stack, it supports other LLMs, tools, and infrastructures as well. It makes agent development feel more like building software - structured, modular, and highly adaptable. Core Concepts An Agent in ADK is a self-contained unit capable of reasoning, using tools, and collaborating with other agents. Developers can create LLM-based agents for natural language tasks, workflow agents for automation, or custom agents for domain-specific logic. Tools & Ecosystem ADK supports a rich tool ecosystem, including pre-built utilities for search, code execution, and custom functions. Agents can even use other agents as tools, allowing highly modular and scalable AI architectures. Orchestration & Workflow Developers can control agent behavior using workflow types like Sequential, Parallel, or Loop, or rely on LLM-driven routing for dynamic orchestration. This combination enables hybrid systems that balance rule-based logic with adaptive intelligence. Deployment Options Once built, agents can be packaged into containers and deployed across environments, from Vertex AI Agent Engine and Cloud Run to custom infrastructures like Docker, GKE, or on-prem servers. Ready to experiment with AI agents? Install it with: pip install google-adk Then build your first multi-agent application using Python or Java and deploy it wherever you want. #AgentDevelopmentKit

🚀 Big win for the AI builder community – Google just launched the 𝐀𝐠𝐞𝐧𝐭 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 𝐊𝐢𝐭 (𝐀𝐃𝐊)! If you’re curious about #AgenticAI – where #AIagents don’t just respond, but plan, take action, use tools, and remember past interactions – this release is a must-see. 📚 Explore the docs here: https://lnkd.in/g9Bzwe6G What is ADK? Google’s 𝐀𝐠𝐞𝐧𝐭 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 𝐊𝐢𝐭 (𝐀𝐃𝐊) is a new open-source framework that makes it easier to build modular, testable, goal-driven agents — the kind that can: ✅ Understand instructions ✅ Plan a sequence of actions ✅ Use external tools (like APIs or browsers) ✅ Store and retrieve memory ✅ Work across text and multimodal inputs In short: it's a toolkit for building smarter AI agents that can do more than just chat. 𝐖𝐡𝐲 𝐢𝐭’𝐬 𝐞𝐱𝐜𝐢𝐭𝐢𝐧𝐠 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐜𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲: 🔧 Built with developers in mind — plug-and-play structure 🎯 Clear lifecycle: goal → plan → act 🧠 Built-in memory & tool use 🔍 Replays and logs make debugging & learning easier 🧪 Designed for both research and real-world use cases Whether you're a solo dev, AI researcher, or part of a startup building AI copilots, this gives us a solid foundation to create agents that actually get things done. 𝐖𝐡𝐚𝐭 𝐲𝐨𝐮 𝐜𝐚𝐧 𝐛𝐮𝐢𝐥𝐝: -Personal research agents -Workflow automation bots -Customer support copilots -Data analysis assistants -Multimodal reasoning tools 💬 The best part? It’s open source and ready for community contributions. Let’s build, test, and push the boundaries of what agents can do — together. Are you exploring agent frameworks? Got a cool use case in mind? Let’s connect and share ideas! #GoogleADK #AgentDevelopmentKit #OpenSourceAI #AgenticAI #AICommunity #BuildInPublic #AIBuilders #GenAI #AutonomousAgents #ToolUse #AIWithMemory #AIProductBuilders

AI Agent framework guide that I wish I had when I was starting out Here's how to choose the best framework for your AI Agents... You can build AI agents from scratch with Python, but frameworks make it easier, with templates, tool integrations, evals, and more. With so many options out there, picking the right one is tough. Here’s a quick guide to the most common ones and when to use them: LangGraph – Built on LangChain, ideal for complex multi-step reasoning. 📌 Use it when building complex agents with extensive tool support. Google ADK – Modular, model-agnostic, and built for multi-agent orchestration. 📌 Use for building enterprise agents with Google Cloud, code execution, and role-based planning. CrewAI – Designed for role-based agent teams with auto task delegation. 📌 Great for autonomous teams like research assistants, dev agents, and report generators. OpenAI Agents SDK – Lightweight, Python-first, production-ready. 📌 Use for quick deployment of OpenAI-powered agents that use tools, APIs, or loops. AutoGen (Microsoft) – Conversational, human-in-the-loop, async agents. 📌 Best for collaborative agents like Deepresearch. - Semantic Kernel (Microsoft) – Plugin-based with memory and planners. 📌 Use for AI copilots in enterprise apps that need planning + memory. Microsoft Agent Framework – Unified agents + graph workflows with multi‑agent patterns and open tools. 📌 Use for production copilots/automations needing checkpointed long‑runs and Azure deployment. - AWS Strands – Deep AWS integration with model-first reasoning. 📌 Ideal for secure, scalable, Bedrock-based agent systems. - Pydantic Agents – Focused on data validation & schema enforcement. 📌 Use alongside other frameworks to ensure structured outputs from LLMs. - LlamaIndex – Specialized in connecting data to LLMs with RAG support. 📌 Use for knowledge agents answering from PDFs, APIs, or DBs. - Haystack – Pipeline-focused, supports RAG + multimodal inputs. 📌 Great for document Q&A, search agents, and flexible GenAI workflows. - IBM Bee – Built for distributed multi-agent systems at scale. 📌 Use in enterprise ops where many agents collaborate on complex workflows. - Smol Agents (Hugging Face) – Simple, plug-and-play, multimodal ready. 📌 Best for fast prototyping, education, or building fun AI tools with vision/audio/text. Agno – Multi‑agent with fast, step‑based workflows, built‑in FastAPI runtime. - 📌 Use for high‑performance Python agents/teams with private + production deployment. For more in-depth analysis of their feature, make sure to check the entire carousel and the comment section for their GitHub Repos. Save 💾 ➞ React 👍 ➞ Share ♻️ & follow for everything related to AI Agents

🚨 Breaking : Google just open-sourced its own AI Agent framework It's the same one powering Google's own AI products ! It's called ADK (Agent Development Kit) A code-first framework for building, deploying, and orchestrating AI agents That lets you build agents the way you build software With real code, real tests, real CI/CD The core philosophy : → Code-first development : Python, Java, TypeScript, Go → Event-driven runtime, not request-response → Version control, testing, CI/CD all work natively → It's the same framework powering Google's own products (Agentspace, Customer Engagement Suite) The agent primitives are clean : → LlmAgent for reasoning → SequentialAgent, ParallelAgent, LoopAgent for deterministic pipelines → AgentTool : use agents as tools inside other agents → LLM-driven routing for dynamic task delegation → A2A protocol for remote agent-to-agent communication across services The tool ecosystem is where it gets ridiculous : → Google Search, Code Execution built-in → Full MCP support (Google Maps, BigQuery MCP servers) → Point it at any OpenAPI spec → auto-generates tools → Direct LangChain, LlamaIndex, CrewAI, LangGraph integration → Human-in-the-loop confirmation before tool execution → Long-running async tools with background execution And it streams bidirectional audio and video out of the box 📌 Quick setup : 1. Install: pip install google-adk 2. Create a project: adk create my_agent 3. Add your API key to the .env file: GOOGLE_API_KEY=your_key_here 4. Run it: adk web (launches a local web UI) or adk run my_agent (CLI) 🔗 Links : The full docs : google.github.io/adk-docs Repo : https://lnkd.in/e3VeKWDS

🚀 Google has released the Agent Development Kit (ADK), a new open-source framework for building, managing, evaluating, and deploying multi-agent systems. Yes, multi-agent systems. Once the stuff of science fiction, academic conferences, and speculative conversations over burnt espresso, they are now available to anyone with a keyboard, a working knowledge of Python, and a mild curiosity about machines talking to each other behind our backs. The truly astonishing part? You can get a multi-agent application running in under 100 lines of code. That’s right. Fewer lines than the average email thread about whether a meeting should be a meeting. Suddenly, creating autonomous systems that can collaborate, coordinate, and possibly outwit you is not only feasible—it’s intuitive. The ADK provides a flexible API interface that allows you to customize, control, and debug your agents. Which is to say: it’s not just about letting the agents run wild. You’re not Dr. Frankenstein. You’re more like an urban planner for digital personalities—designing the infrastructure of interaction, then stepping back to watch them negotiate traffic. To begin your foray into this elegantly orchestrated madness, you’ll find the ADK repo, documentation, and sample projects below. Proceed wisely. With great power comes… the undeniable temptation to automate your ☕️ coffee order. ADK repo https://lnkd.in/eAesHHVW ADK Docs https://lnkd.in/e7Jf49MM ADK Samples https://lnkd.in/eKr3XXHA -- ☕️👨💻 #Google #AI #Python #AgenticAI #OpenSource