Clawcamp X Fetch.ai
April 16, 2026 to April 17, 2026
STAK Space - 1920 Broadway
Prizes
Winner - First Place
$500
Cash Prize
Runner Up - Second Place
$250
Cash Prize
Fetch.ai is your gateway to the agentic economy. It provides a full ecosystem for building, deploying, and discovering AI Agents. With Fetch.ai, you can:
- Build agents using the uAgents framework.
- Register agents (built with uAgents or any other framework) on Agentverse, the open marketplace for AI Agents.
- Make your agents discoverable and accessible through ASI:One, the world’s first agentic LLM.
AI Agents are autonomous pieces of software that can understand goals, make decisions, and take actions on behalf of users.
The Three Pillars of the Fetch.ai Ecosystem
- uAgents – A Python library developed by Fetch.ai for building autonomous agents. It gives you everything you need to create agents that can talk to each other and coordinate tasks.
- Agentverse - The open marketplace for AI Agents. You can publish agents built with uAgents or any other agentic framework, making them searchable and usable by both users and other agents.
- ASI:One – The world’s first agentic LLM and the discovery layer for Agentverse. When a user submits a query, ASI:One identifies the most suitable agent and routes the request for execution.
Challenge statement
Imagine Openclaw evolving through specialized intelligence. Your challenge is to extend Openclaw by connecting it to Agentverse and enabling it to discover, invoke, and use external capabilities through ASI1 model.
Build a specialist capability that Openclaw can use in real interactions. Create a new Agentverse agent for a specific use case, or integrate an existing one, then build the Openclaw skill layer that allows Openclaw to discover, invoke, and use that capability when needed. Design your solution so Openclaw uses ASI1 for reasoning, deciding when delegation is needed, and routing requests to the appropriate Agentverse capability. Show a clear use case where this delegation adds value. Register your agent on Agentverse, connect it to Openclaw, and demonstrate a full end to end interaction through a channel such as Telegram, IRC, or another supported interface.
Deliverables
A working specialist skill for Openclaw An Agentverse agent profile link for the agent used for the skill a. a newly built agent registered on Agentverse, or b. an existing Agentverse agent successfully integrated into the workflow
A working Openclaw skill that allows Openclaw to discover and invoke that capability A live or recorded demo showing the full end to end interaction a. a user interacts with Openclaw through a channel such as Telegram, IRC, or another supported interface b. Openclaw identifies the need for a specialist capability c. Openclaw invokes the Agentverse skill or specialist agent d. the result is returned back through Openclaw to the user
A short technical explanation covering a. what the capability does b. whether the team built a new agent or used an existing one c. how Openclaw routes the request d. how the result is returned to the user through the chosen channel
Dive into the docs from Agentverse and start building.
Note: You are only eligible for this track if you use ASI1 LLM
Tool Stack
Quick start example
This file can be run on any platform supporting Python, with the necessary install permissions. This example shows two agents communicating with each other using the uAgent python library.
Try it out on Agentverse ↗
from datetime import datetime
from uuid import uuid4
from uagents.setup import fund_agent_if_low
from uagents_core.contrib.protocols.chat import (
ChatAcknowledgement,
ChatMessage,
EndSessionContent,
StartSessionContent,
TextContent,
chat_protocol_spec,
)
agent = Agent()
# Initialize the chat protocol with the standard chat spec
chat_proto = Protocol(spec=chat_protocol_spec)
# Utility function to wrap plain text into a ChatMessage
def create_text_chat(text: str, end_session: bool = False) -> ChatMessage:
content = [TextContent(type="text", text=text)]
return ChatMessage(
timestamp=datetime.utcnow(),
msg_id=uuid4(),
content=content,
)
# Handle incoming chat messages
@chat_proto.on_message(ChatMessage)
async def handle_message(ctx: Context, sender: str, msg: ChatMessage):
ctx.logger.info(f"Received message from {sender}")
# Always send back an acknowledgement when a message is received
await ctx.send(sender, ChatAcknowledgement(timestamp=datetime.utcnow(), acknowledged_msg_id=msg.msg_id))
# Process each content item inside the chat message
for item in msg.content:
# Marks the start of a chat session
if isinstance(item, StartSessionContent):
ctx.logger.info(f"Session started with {sender}")
# Handles plain text messages (from another agent or ASI:One)
elif isinstance(item, TextContent):
ctx.logger.info(f"Text message from {sender}: {item.text}")
#Add your logic
# Example: respond with a message describing the result of a completed task
response_message = create_text_chat("Hello from Agent")
await ctx.send(sender, response_message)
# Marks the end of a chat session
elif isinstance(item, EndSessionContent):
ctx.logger.info(f"Session ended with {sender}")
# Catches anything unexpected
else:
ctx.logger.info(f"Received unexpected content type from {sender}")
# Handle acknowledgements for messages this agent has sent out
@chat_proto.on_message(ChatAcknowledgement)
async def handle_acknowledgement(ctx: Context, sender: str, msg: ChatAcknowledgement):
ctx.logger.info(f"Received acknowledgement from {sender} for message {msg.acknowledged_msg_id}")
# Include the chat protocol and publish the manifest to Agentverse
agent.include(chat_proto, publish_manifest=True)
if __name__ == "__main__":
agent.run()
Important links
Fetch.ai Resources




Examples to get you started:
Judging Criteria
Prizes
Winner - First Place
$500
Cash Prize
Runner Up - Second Place
$250
Cash Prize