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AWorld

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Basic Information

AWorld is an open source Python framework and runtime designed to build, orchestrate, train, and evolve AI agents and multi-agent systems with an emphasis on continuous self-improvement. The repo supplies abstractions for individual LLM-based agents, swarms and team topologies, runners for synchronous and streamed execution, and tools for integrating external MCP services and memory stores. It targets researchers and developers who want to prototype agent behaviors, run high-concurrency rollouts, synthesize training trajectories and function-call datasets, or deploy agent-driven web applications. The project includes examples and tutorials (BFCL, GAIA, IMO) and supports multiple LLM and embedding providers, vector stores, and cloud-native distributed training scenarios. Quickstart code and scripts show how to define agents, configure providers, run local web or API servers, and integrate memory and tool services for production-like workflows.

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

App Details

Features
AWorld exposes modular building blocks including Agent and AgentConfig abstractions, Runner classes for execution loops, and Swarm/TeamSwarm/HandoffSwarm constructs for multi-agent topologies. It provides MemoryFactory with configurable embedding models and vector DB backends, an MCP tool integration mechanism for external tool servers, context management for dynamic prompts and state tracking, and a tracing subsystem for observability. The repo bundles sandboxes and example applications, distributed environment integration for backward/forward process training, utilities to synthesize function-call training data, and web and API server entry points. It is cloud-native oriented with emphasis on scalability, high concurrency, multi-model provider support, and pluggable tool and memory configurations to support both research experiments and production deployments.
Use Cases
AWorld accelerates development and evaluation of single agents and coordinated multi-agent systems by providing ready-made runtime components, orchestration patterns, and integrations that reduce boilerplate. Teams can prototype tool-using agents, compose collaborative workflows, add long- and short-term memory, and connect to various LLM and embedding providers without rebuilding infra. The framework supports collecting training trajectories, generating synthetic data for fine-tuning, and running distributed rollouts for scalable training. Built-in tracing and context management improve debugging and experiment reproducibility. Web and API server modes ease deployment and integration into applications. Example recipes and benchmarks demonstrate practical gains for tasks such as tool use, math problem solving, and retrieval-augmented workflows, helping researchers and engineers iterate faster.

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