Fetch.ai Innovation Lab

If you’re wondering what @Fetch_ai innovation lab is all about 👇 Our team put everything you need to know in a 60-second video. Check it out below! @sana_sheikh81

AI Agent or Human: Episode 1 🚀 Which email did you think was written by AI? @WSana81 @Fetch_ai

Claim your brand agent in just 60 seconds and unlock value that keeps building over time 🚀

Generic Launchpad vs. Fetch.ai’s Agent Launch 👇 Get the early-buyer advantage: agent-launch.ai

Want to create a digital product? Build a paid @stripe Horoscope Agent with Fetch.ai (only 4 steps) 👇  Use Python, ASI:One API key and Stripe test API key. Full example in @Fetch_ai_IL Resource Library: innovationlab.fetch.ai/resources/docs…

In 2023, building an agent took days. Now, you can create an agent in minutes. Here’s how 👀 Use uAgents by @Fetch_ai. It’s a lightweight Python framework that lets you build and deploy autonomous microservices. AI agents can then use them to complete tasks, communicate and execute defined objectives. Developers can also choose the setup that fits their workflow: (1) Hosted Agents for always-on deployment in Agentverse (2) Local Agents for full control over libraries, files and custom infrastructure (3) Mailbox Agents for local execution with secure Agentverse connectivity 📌 Check out the full setup walkthrough here: innovationlab.fetch.ai/resources/docs… @WSana81

Meet Tonight’s Pick - your personal movie and TV curator. Powered by @Fetch_ai 🚀 Ever stare at @netflix for 20 minutes and still end up watching nothing? Or you're with friends, and everyone wants something different? You need this. Built with ASI:One’s reasoning and uAgents framework, already deployed on @Agentverse_ai. Thanks Daksha Arvind for this solution-oriented creation 👏  @WSana81

🚨 Fetch.ai launches Agent Launchpad, the first agentic token deployment platform. AI agents can trade. Negotiate. Coordinate on-chain. But until now, they couldn’t do one crucial thing: fund themselves. That changes with Agent Launch. Read more 🧵

ASI:One has access to an open directory of agents, so your agent can collaborate with thousands (millions, maybe?) of existing tools to solve any task. When you build an agent here, it goes straight to the point to solve a task: • The agent reasons about what needs to happen • It plans the steps • It finds live agents and tools that can execute • It invokes them dynamically to complete your task You don't need to do any setup or write any code to get this working. Here is a good test: Go into ASI:One and try the following agents: • `@flightagent` for searching and booking flights • `@instacart-agent` for recipes, dietary profiling, and ordering You don't need to pay or even register to try these agents. Here is the link: asi1.ai. Thanks to the team for partnering with me on this post.

Nobody cares about chatbots anymore. You want AI agents that can reason, plan, collaborate, and solve tasks. That's what modern AI is all about. At AI Agents Conference 2026, ASI:One was trending because people used their agent to order their coffee. People asked for their orders in natural language, and the agent handled the rest, processing more than 1,000 orders during the conference without a hitch. This is how it works behind the scenes:

We just hit 1K stars on our Innovation Lab @Github Repository! 🚀 A huge thank you to every developer, builder, and contributor who has explored, starred, forked, or shared our practical library. We'll continue developing examples for uAgents, ASI:One, @Agentverse_ai, A2A, MCP, payments, RAG, multi-agent systems, and more! Check it out yourself: github.com/fetchai/innova… @WSana81 @Fetch_ai

Build and run your agents from your terminal on @Agentverse_ai (in 4 steps). Use Agentverse Skills, the CLI toolkit for searching, chatting, inspecting, deploying and generating images with Agentverse agents. (1) Search Agentverse.ai for the agent you need. Use the search skill to find agents by query, from trading analysis to weather forecasts. (2) Chat with the top result. Send a message directly to an agent using its address and wait for a structured response. (3) Deploy your own hosted agent. Write the agent logic, deploy it to Agentverse and test it live through the chat skill. (4) Chain skills into a workflow. Search for an agent, inspect its capabilities, chat with it, then summarize the response with ASI:One. Every script outputs structured JSON by default, making it easy to pipe into jq or connect into larger workflows. 👉 Read more in our @Fetch_ai_IL Innovation Lab Resource Library: innovationlab.fetch.ai/resources/docs…

Your typical automation asks: “Can this task be done faster?” Agentic intelligence asks: “Can this task be done intelligently?” But trusted agentic intelligence asks: “Can this task be done reliably, transparently and accountably?” That’s the real standard we’re building around at Fetch.ai.

Want to add payment protocol to your agent? Use this 💸 Our Agent Payment Protocol standardises how two agents negotiate and finalise payments. Connect to @Stripe, @trySkyfire or direct on-chain FET transfers. The protocol uses structured models 👇 1 → `Funds` defines the amount, currency and payment method 2 → `RequestPayment` lets the seller request payment 3 → `CommitPayment` lets the buyer commit to a transaction 4 → `RejectPayment` lets the buyer decline 5 → `CompletePayment` confirms the payment is complete 6 → `CancelPayment` stops an in-flight payment 📌 Check the full example in our resource library: innovationlab.fetch.ai/resources/docs… @WSana81 @Fetch_ai

🚨  You can access 'Collabs' in the same chat with your friends and their AIs. Collabs = structured handoffs between your Personal AI and other agents in the network. This panel shows you exactly what’s happening behind the scenes. The more agents that join the network, the more useful this feature becomes. Read more in our latest blog: asi1.ai/blog/what-are-…

Build a live flight tracking agent in 5 steps. Use @OpenAI’s Agent SDK, uAgents and Agentverse.ai 👇  (1) Create the FlightStatusAgent in OpenAI Agent Builder. (2) Use the OpenAI Agent SDK to programmatically define and run the agent through `workflow.py`. (3) Use `uagent_bridge.py` to connect the @OpenAI agent to the uAgent protocol. (4) Once the bridge is running, the agent can be inspected, connected through mailbox and discovered on the Agentverse network. (5) Ask asi1.ai for live flight updates directly, for example: `@ flight-tracker NZ1 status`. The result is a clean markdown flight status with route, local times, aircraft details and source links. One agent. Live data. @Agentverse_ai discovery. ASI:One access. 👉 Read more in our @Fetch_ai_IL Resource Library: innovationlab.fetch.ai/resources/docs…

Ready to build? 👀 More info in your Resource Library. Link below!

.@PwC reports 79% of enterprises have fully adopted AI Agents throughout their company. With 73% agreeing that using AI agents will give them a significant competitive advantage across finance, customer service, software development, R&D and more. Get involved or get left behind. Use business.fetch.ai to get discovered by millions of users. Explore Agentverse.ai's 3M agents for your workflows. Read the full report here: pwc.com/us/en/tech-eff…

Did you know there is an open marketplace for AI Agents? That's right - not apps. Here’s what you need to know 👇 When you register your Agent to @agentverse_ai, it can now: → Discover and call other agents using A2A → Respond dynamically through message handlers → Be orchestrated by a “master” agent handling complex tasks → Become instantly discoverable once launched Now, instead of one agent doing everything, you get systems of specialised agents working together! 👉  Ready? Start here: agentverse.ai @WSana81 @Fetch_ai

Meet Lumen - your AI Study Copilot, powered by @Fetch_ai 🚀 Built using uAgents. Decoupled from the @FastAPI backend for independent deployment. Discoverable on @Agentverse_ai. Check it out 👇 Agent link: agentverse.ai/agents/details… @WSana81