Relevance AI
Official@relevanceai · Australia
Offers infrastructure for managing, evaluating, and optimizing multi-agent workforces and knowledge tables through the Model Context Protocol.
Agent Skills by Relevance AI
Showing 15 vetted skills indexed across 2 GitHub repositories.
RelevanceAIdocument-workforce
Generate markdown documentation for AI workforces from Relevance AI platform data.
RelevanceAIagent-optimiser
Analyze Relevance AI agents to identify configuration issues and optimize cost efficiency.
RelevanceAIcapture-learning
Summarize session errors, fixes, and patterns into documentation updates.
RelevanceAItemplate-agent
Provide a structured template and checklist for building basic AI agents.
RelevanceAIsetup
Automate development environment setup for Relevance AI projects.
RelevanceAIagent-build-patterns
Guide architecture pattern selection for scalable agent systems.
RelevanceAIimprove
Validate insights and create reviewable pull requests for shared documentation.
RelevanceAIeval
Generate and execute YAML-configured test cases for Relevance AI agents.
RelevanceAIrelevance-ai
Manage AI agents, tools, and workforces via the Relevance MCP.
RelevanceAImanaging-relevance-tools
Manage the full lifecycle of Relevance AI tools within MCP-enabled projects.
RelevanceAImanaging-relevance-agents
Create, configure, run, and debug Relevance AI agents and workflows.
RelevanceAImanaging-relevance-workforces
Create, configure, trigger, and debug multi-agent workforce graphs via MCP tools.
RelevanceAIrelevance-analytics
Query Relevance AI agent analytics by project, agent, or date range.
RelevanceAIrelevance-evals
Manage agent evaluations in Relevance AI with test cases and eval runs.
RelevanceAImanaging-relevance-knowledge
Create and query knowledge tables with MCP tools for agent memory.
Frequently Asked Questions About Relevance AI
FAQPage SchemaWhat specific tasks can be performed using Relevance AI's capabilities?▼
Users can configure, trigger, and debug multi-agent workforce graphs, manage knowledge tables for memory, and execute performance evaluations. The platform enables lifecycle management of agents and tools via the Model Context Protocol, allowing for granular control over project-specific analytics and configuration optimization.
Which personas benefit most from these technical capabilities?▼
Engineers and architects focused on building, testing, and maintaining scalable agent systems benefit most. These capabilities are designed for technical teams needing to manage complex agent interactions, validate system outputs through structured test cases, and maintain documentation for multi-agent architectures.
What are the prerequisites for integrating these capabilities into a project?▼
Integration requires an environment compatible with the Model Context Protocol to interface with the Relevance ecosystem. Users must have access to the platform to manage agent configurations, define YAML-based test cases for evaluations, and establish connectivity to knowledge tables for persistent memory storage.