awesome-repositories.com
Blog
MCP
awesome-repositories.com

Discover the best open-source repositories with AI-powered search.

ExploreCurated searchesOpen-source alternativesSelf-hosted softwareBlogSitemap
ProjectMCP serverAboutHow we rankPress
LegalPrivacyTerms
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
spring-ai-alibaba avatar

spring-ai-alibaba/examples

0
View on GitHub↗

Examples

This project provides a collection of example implementations for building AI agents and workflows using the Spring AI Alibaba framework. It focuses on demonstrating how to create intelligent agents that iteratively reason and act to solve problems, coordinate multiple agents across services, and integrate human oversight into automated processes.

The examples showcase key differentiators such as graph-based workflow automation with conditional routing, nested graphs, and parallel execution, as well as real-time streaming of agent responses to clients. The project also illustrates how to manage agent context through prompt engineering, handle errors with automatic retries, and orchestrate complex multi-agent workflows using patterns like sequential, parallel, and routing execution.

Additional capabilities include connecting agents to various LLM providers, tools, and the Model Context Protocol through Spring AI abstractions, enabling comprehensive API integration for building sophisticated AI applications.

AI search

Explore more awesome repositories

Describe what you need in plain English — the AI ranks thousands of curated open-source projects by relevance.

Start searching with AI
java2ai.com
↗

Features

  • Multi-Agent Orchestration Patterns - Combines multiple agents using sequential, parallel, routing, and loop execution patterns.
  • LLM Tooling Integrations - Connects agents to multiple LLM providers, tool-calling, and the Model Context Protocol.
  • MCP Protocol Integrations - Integrates with multiple LLM providers, tool-calling, and the Model Context Protocol.
  • Human-in-the-Loop Workflows - Inserts human feedback and approval steps into agent workflows for supervised execution.
  • Multi-Agent Orchestration - Coordinates multiple AI agents across services with sequential, parallel, and routing patterns.
  • ReAct Agents - Provides example implementations for building ReAct agents that iteratively reason and act.
  • ReAct Agent Development - Provides example implementations for building ReAct agents that iteratively reason and act.
  • Graph-Based Workflow Orchestrators - Uses a graph runtime with conditional routing, nested graphs, and parallel execution.
  • Agent Failure Mitigation - Ships automated retry policies and failure recovery for maintaining agent reliability.
  • Agent Context Management - Applies built-in prompt engineering and conversation flow control to improve agent reliability.
  • Service-Based Coordination - Demonstrates coordinating agents across services using Nacos for distributed collaboration.
  • Agent Response Streams - Streams agent output to clients in real time as it is produced.
  • Real-Time Text Streaming - Sends agent output to clients in real time as it is produced for interactive experiences.
2,744 stars·1,159 forks·Java·Apache-2.0·48 views

Star history

Star history chart for spring-ai-alibaba/examplesStar history chart for spring-ai-alibaba/examples

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

Frequently asked questions

What does spring-ai-alibaba/examples do?

This project provides a collection of example implementations for building AI agents and workflows using the Spring AI Alibaba framework. It focuses on demonstrating how to create intelligent agents that iteratively reason and act to solve problems, coordinate multiple agents across services, and integrate human oversight into automated processes.

What are the main features of spring-ai-alibaba/examples?

The main features of spring-ai-alibaba/examples are: Multi-Agent Orchestration Patterns, LLM Tooling Integrations, MCP Protocol Integrations, Human-in-the-Loop Workflows, Multi-Agent Orchestration, ReAct Agents, ReAct Agent Development, Graph-Based Workflow Orchestrators.

Which projects share features with spring-ai-alibaba/examples?

Projects with overlapping indexed features include: microsoft/agent-framework — The agent-framework is an LLM agent orchestration framework and multi-agent workflow engine designed for building… joaomdmoura/crewai — CrewAI is a multi-agent orchestration framework and autonomous agent workflow engine. It provides a system for… jetbrains/koog — Koog is an LLM agent framework used to build autonomous entities that execute tool-based workflows. It utilizes a… lintsinghua/claude-code-book — This repository is a long-form technical book about how a production AI coding agent runtime, often called an agent… i-am-bee/beeai-framework — The BeeAI Framework is an LLM agent framework and multi-agent orchestration engine used to build autonomous agents… alibaba/spring-ai-alibaba — This project is a Java-based framework integration that provides an AI agent runtime, a graph-based AI workflow…

Projects sharing features with Examples

These projects share indexed features with Examples. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • microsoft/agent-frameworkmicrosoft avatar

    microsoft/agent-framework

    14,579View on GitHub↗

    The agent-framework is an LLM agent orchestration framework and multi-agent workflow engine designed for building autonomous AI agents. It provides a tool integration layer for binding external functions, APIs, and sandboxed code as executable tools for language models. The framework distinguishes itself through a graph-based system for designing sequential and parallel task flows, featuring state management and checkpointing for long-running processes. It implements comprehensive conversational state management and an observability suite that uses telemetry to trace execution flows and monit

    Pythonagent-frameworkagentic-aiagents
    View on GitHub↗14,579
  • joaomdmoura/crewaijoaomdmoura avatar

    joaomdmoura/crewai

    53,752View on GitHub↗

    CrewAI is a multi-agent orchestration framework and autonomous agent workflow engine. It provides a system for coordinating autonomous AI agents with specific roles and goals to solve complex tasks through collaborative intelligence. The framework distinguishes itself through a collaborative AI agent system that enables multiple language model instances to share intelligence and execute multi-step objectives via role-playing. It incorporates human-in-the-loop mechanisms, allowing for manual review checkpoints to validate decisions and refine outcomes within autonomous execution paths. The pl

    Python
    View on GitHub↗53,752
  • jetbrains/koogJetBrains avatar

    JetBrains/koog

    4,654View on GitHub↗

    Koog is an LLM agent framework used to build autonomous entities that execute tool-based workflows. It utilizes a graph-based workflow engine to define agent behaviors and decision paths as a directed graph of nodes and edges. The framework distinguishes itself through a model provider orchestrator that enables dynamic switching, load balancing, and automatic fallbacks between different AI backends. It implements the Model Context Protocol to connect agents to remote tool servers and features a RAG memory system using vector embeddings to maintain long-term conversation context. The project

    Kotlinagentframeworkagentic-aiagents
    View on GitHub↗4,654
  • lintsinghua/claude-code-booklintsinghua avatar

    lintsinghua/claude-code-book

    4,359View on GitHub↗

    This repository is a long-form technical book about how a production AI coding agent runtime, often called an agent harness, is built. It dissects a real-world coding agent's architecture chapter by chapter and turns it into transferable lessons, and it also works as a step-by-step guide to designing and building your own harness around a language model. Python scripts in the repository support the book itself rather than the agent. The book's core subject is the machinery around the model: a streaming conversation loop that repeats tool calls until the model returns plain text, a tool protoc

    Pythonagentagent-architectureai-agent
    View on GitHub↗4,359
    • Compare all 30 related projects→