Call Fetch.ai Agentverse agents by address. Search the Agentverse marketplace, browse a curated catalog of top agents (Tavily Search, ASI1-Mini, DALL-E 3, Technical Analysis, Asset Signal, Translator, Statistics, Github), and send ChatMessages to any agent. Use when working with Fetch.ai, Agentverse, uAgents, decentralized AI agents, or when the user wants to discover or message an agent on the Fetch.ai network.
Call Fetch.ai Agentverse agents by address. Search the Agentverse marketplace, browse a curated catalog of top agents (Tavily Search, ASI1-Mini, DALL-E 3, Technical Analysis, Asset Signal, Translator, Statistics, Github), and send ChatMessages to any agent. Use when working with Fetch.ai, Agentverse, uAgents, decentralized AI agents, or when the user wants to discover or message an agent on the Fetch.ai network.
Send queries to Fetch.ai Agentverse agents and return the agent's reply in natural language.
CRITICAL RULES — READ FIRST
NEVER show the user a bash command, a script path, or anything that looks like python3 ... or bash .... Those are for YOU to run internally. The user is chatting in Telegram/CLI and wants plain language.
If the user asks "how do I use X" or "how does this work" — DO NOT respond with commands. Respond with plain-English example prompts, like: "Just ask me things like 'get trading signals for TSLA' or 'translate hello to French' and I'll take care of the rest."
When the user makes an actual request (get signals, translate, etc.), run the script yourself and reply with the agent's answer. Format it nicely. Do not tell them to run anything.
The only time you reveal paths or commands is if the user explicitly asks for debug/diagnostic info.
How to answer common user questions
"How do I use this?" / "What can this do?" → Explain in plain English: it calls Fetch.ai Agentverse agents for real-time data, translations, stats, stock signals, image generation, etc. Give 3-4 example prompts the user can try (in natural language, NOT bash).
"What agents are available?" / "Show me the catalog" → Run catalog.py and format the result as a friendly list.
"Find a [topic] agent" → Run search.py and return the top matches in plain text.
Any actual task (signals, translation, stats, etc.) → Run fire.sh, tell the user to hold on ~40 seconds, then run result.sh and reply with the agent's answer in your own words.
Natural-language prompts the user might send
"get me trading signals for TSLA" → call signals agent
"what does ASI1-Mini think about quantum computing?" → call asi agent
"translate 'hello world' to Japanese" → call translate agent