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Basic Information

uAgents is a Python library and framework for creating autonomous AI agents. It provides a simple, decorator-based programming model so developers can define agents that run scheduled tasks or respond to events. Agents can be instantiated with an optional seed to produce deterministic addresses. On startup agents register on the Fetch.ai Almanac smart contract, enabling network connectivity and discovery. The repository includes a python package with core agent definitions and runtime utilities, a separate examples repository for application patterns, and documentation for installation and advanced usage. The project targets Python 3.10 through 3.13 and is distributed via pip. The README demonstrates a quickstart flow: create an Agent object, attach event or interval handlers, and call run to start the agent.

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Features
The README highlights easy creation and management of agents using expressive decorators and built-in scheduling and event handlers. Agents automatically join the uAgents network by registering on the Almanac smart contract, providing discoverability within the Fetch.ai ecosystem. Messages and wallets are cryptographically secured to protect identities and assets. The library contains core definitions and runtime code in a python/uagents-core folder and points to an examples repository with sample agents and integrations. Installation is supported via pip for supported Python versions. Documentation and tutorials cover key concepts such as addresses, storage, synchronous communication, and broadcasting. The project includes tests and development guidelines to help contributors.
Use Cases
uAgents lowers the barrier to building decentralized, autonomous agents by offering a lightweight, opinionated framework that handles common concerns such as scheduling, event-driven handlers, identity, and network registration. Automatic Almanac registration and cryptographic wallet integration make it easier to deploy agents that participate in the Fetch.ai marketplace and ecosystem. Example projects and comprehensive documentation speed up learning and prototyping. The core library abstracts messaging and storage patterns so developers can focus on agent behavior rather than low-level plumbing. Community guidelines, tests, and a separate examples repository support extension and contribution, enabling teams to iterate on agent designs and integrations.

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