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swarmzero

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Multi-Agent Platforms & Frameworks
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Basic Information

SwarmZero SDK is a developer-focused Python library for creating and running AI agents and coordinated swarms of agents. It provides primitives to define individual Agent instances with instructions and callable tools, compose multiple agents into a Swarm for collaborative tasks, and orchestrate multi-step workflows that can run sequentially, in parallel, conditionally, or in loops. The SDK integrates with multiple LLM providers and supports configuration via TOML or YAML files, managed through an SDKContext. It includes retriever capabilities for building vector indices from supported document types and exposes an API endpoint for chat interactions. The project targets Python 3.11 and offers installation via pip or poetry. Documentation, examples, and a local Swagger UI are provided to help developers run agents and test chat endpoints.

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App URL
https://github.com/swarmzero/swarmzero
Github Repository
https://github.com/swarmzero/swarmzero/blob/main/README.md

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App Details

Features
The README documents core features including an Agent class for defining behavior and functions, a Swarm construct to coordinate multiple agents, and a Workflow abstraction with WorkflowStep and StepMode to compose complex pipelines and nested workflows. SDKContext centralizes configuration and resources. Tool integration lets agents call custom functions such as web3 helpers. Retriever support builds vector indices from local documents and offers multiple retrieval backends and supported file types. The package lists supported LLM providers and provides a local API with a chat endpoint and Swagger docs. Examples, sample prompts configuration, and testing instructions are included to accelerate development and validation.
Use Cases
This SDK helps developers prototype and deploy multi-agent systems by providing reusable building blocks for agent behavior, collaboration, and orchestration. Workflows enable deterministic or parallel execution patterns and nested composition, making complex pipelines easier to manage. Retriever tools let agents access semantic document search and summarization over common file formats. Tool integration enables connecting agents to external services such as blockchains. The provided API and examples simplify running agents locally and integrating them into applications. Configuration management and sample prompts make agents easier to configure and demonstrate to users. Testing guidance and CI badges indicate a focus on maintainability and contribution.

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