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Roles / Technical Category
Review matching candidates
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Multi-agent Orchestration
Frameworks and tools for coordinating multiple agents, teams, roles, swarms, or AI workforces.
4955total
4955filtered
302page
413pages
Category fit
Which technical function should the agent perform?
Filter candidates by capability, protocol, trust, install readiness, and trial status.
- Coordinate several agents around one outcome.
- Assign specialist roles and handoffs.
- Inspect launch packets before running a team.
Recommended trials and bundles
playbooks / skill
langchain4j-rag-implementation-patterns skill by giuseppe-trisciuoglio/developer-kit
This skill helps you implement Retrieval-Augmented Generation patterns with LangChain4j to build knowledge-rich QA and document-aware assistants.
MCPHTTPCLI
- Fit
- 61 / 100
- Trust
- Needs review
- Install
- MCP ready
Review links
playbooks / skill
langchain4j-spring-boot-integration skill by giuseppe-trisciuoglio/developer-kit
This skill helps Spring Boot developers integrate LangChain4j with declarative AI services and auto-configuration for production-grade AI apps.
MCPHTTPCLI
- Fit
- 61 / 100
- Trust
- Needs review
- Install
- MCP ready
Review links
explainx / skill
langchain4j-testing-strategies
Comprehensive testing strategies for LangChain4j applications with mocks, containers, and RAG validation. \n \n Provides unit testing patterns with mock mode…
MCPHTTPCLI
- Fit
- 61 / 100
- Trust
- Needs review
- Install
- MCP ready
Review links
playbooks / skill
langchain4j-testing-strategies skill by giuseppe-trisciuoglio/developer-kit
This skill provides testing strategies for LangChain4J applications, enabling reliable unit, integration, and end-to-end tests with mocks and Testcontainers.
MCPHTTPCLI
- Fit
- 61 / 100
- Trust
- Needs review
- Install
- MCP ready
Review links
explainx / skill
langchain4j-tool-function-calling-patterns
Annotation-based and programmatic tool system for LangChain4j agents to execute external functions, APIs, and services. \n \n Define tools using @Tool annota…
MCPHTTPCLI
- Fit
- 61 / 100
- Trust
- Needs review
- Install
- MCP ready
Review links
playbooks / skill
langchain4j-tool-function-calling-patterns skill by giuseppe-trisciuoglio/developer-kit
This skill enables building agentic applications by defining tools and function calls with LangChain4j, enabling real-time API access and external integrations.
MCPHTTPCLI
- Fit
- 61 / 100
- Trust
- Needs review
- Install
- MCP ready
Review links
explainx / skill
langchain4j-vector-stores-configuration
LangChain4J vector store configuration for RAG applications with multiple database backends. \n \n Supports PostgreSQL/pgvector, Pinecone, MongoDB Atlas, Mil…
MCPHTTPCLI
- Fit
- 61 / 100
- Trust
- Needs review
- Install
- MCP ready
Review links
playbooks / skill
langchain4j-vector-stores-configuration skill by giuseppe-trisciuoglio/developer-kit
This skill helps configure LangChain4J vector stores for RAG applications, enabling efficient embedding storage, retrieval, and production-ready performance.
MCPHTTPCLI
- Fit
- 61 / 100
- Trust
- Needs review
- Install
- MCP ready
Review links
- Fit
- 61 / 100
- Trust
- Needs review
- Install
- MCP ready
Review links
mcpservers / skill
LangGraph Execution Control
INVOKE THIS SKILL for LangGraph workflows, parallel execution, interrupts, or streaming. Covers Send API for fan-out, interrupt() for human-in-the-loop,…
MCPHTTPCLI
- Fit
- 61 / 100
- Trust
- Needs review
- Install
- MCP ready
Review links
mcpservers / skill
LangGraph Persistence & Memory
langgraph-persistence-&-memory — an installable skill for AI agents, published by langchain-ai/langchain-skills.
MCPHTTPCLI
- Fit
- 61 / 100
- Trust
- Needs review
- Install
- MCP ready
Review links
playbooks / skill
langgraph skill by bobmatnyc/claude-mpm-skills
This skill enables building complex multi-agent workflows with state persistence, human-in-the-loop, and time-travel debugging using LangGraph.
MCPHTTPCLI
- Fit
- 61 / 100
- Trust
- Needs review
- Install
- MCP ready
Review links
Review support
Open Category guide
Review links
Technical category pages stay focused on job browsing; complete review records and finalist profile resources stay linked here.
Supporting review linksLinks, review order, and page-specific notes remain available here.
Category guideTechnical role guideAgent inventoryResource mapReview guideAPI referenceSite contextSitemapProfile resources
4955 matching candidates for Multi-agent Orchestration. Open candidate profiles for fit, setup guidance, trust evidence, and sandbox trial planning.
- 01Site contextShared review rules
- 02Role categoryOpen the focused review record
- 03Agent inventoryBrowse the matching inventory
- 04Profile resourcesEvidence, setup, trust, trial, and history files
Use the links and review path below for role-category candidates, related playbooks, and finalist profile resources before action.
Review route: OpenAI review tools can start with openai.json; otherwise use site context and agent inventory before opening the finalist's complete review materials before install, hire, or promotion.