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Roles / Technical Category
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Multi-agent Orchestration
Frameworks and tools for coordinating multiple agents, teams, roles, swarms, or AI workforces.
4955total
4955filtered
263page
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
deepeval skill by sammcj/agentic-coding
This skill helps you evaluate LLM applications using Deepeval's pytest-based metrics and tracing to ensure quality and safety.
MCPHTTPCLI
- Fit
- 61 / 100
- Trust
- Needs review
- Install
- MCP ready
Review links
- Fit
- 61 / 100
- Trust
- Needs review
- Install
- MCP ready
Review links
explainx / agent
Deepgram
Deepgram's voice AI platform provides APIs for speech-to-text, text-to-speech, and language understanding.
MCPHTTPCLI
- Fit
- 61 / 100
- Trust
- Needs review
- Install
- MCP ready
Review links
playbooks / skill
deepinit skill by yeachan-heo/oh-my-claudecode
This skill generates comprehensive, hierarchical AGENTS.md documentation across the codebase to guide AI agents through structure and dependencies.
MCPHTTPCLI
- Fit
- 61 / 100
- Trust
- Needs review
- Install
- MCP ready
Review links
explainx / agent
Deepnote
Deepnote's AI Copilot, with its efficient and contextual code suggestions, is paving the way for a future of AI-powered data exploration in notebooks.
MCPHTTPCLI
- Fit
- 61 / 100
- Trust
- Needs review
- Install
- MCP ready
Review links
- Fit
- 61 / 100
- Trust
- Needs review
- Install
- MCP ready
Review links
playbooks / skill
deeprecall skill by openclaw/skills
This skill enables persistent AI agents to recall and correlate large memory stores using recursive language models for deep historical insight.
MCPHTTPCLI
- Fit
- 61 / 100
- Trust
- Needs review
- Install
- MCP ready
Review links
playbooks / skill
deepresearch skill by openclaw/skills
This skill enables deep research by planning, decomposing, and coordinating subagents to produce synthesized, data-driven insights across large contexts.
MCPHTTPCLI
- Fit
- 61 / 100
- Trust
- Needs review
- Install
- MCP ready
Review links
- Fit
- 61 / 100
- Trust
- Needs review
- Install
- MCP ready
Review links
playbooks / skill
defragmenting-memory skill by letta-ai/letta-code
This skill decomposes and reorganizes memory blocks into focused, single-purpose components to improve clarity and reduce redundancy in memory-based AI
MCPHTTPCLI
- Fit
- 61 / 100
- Trust
- Needs review
- Install
- MCP ready
Review links
playbooks / skill
delegate skill by jcastillotx/vibe-skeleton-app
This skill automatically delegates tasks to specialized subagents, optimizing workload and accelerating delivery with clear context and parallel execution
MCPHTTPCLI
- Fit
- 61 / 100
- Trust
- Needs review
- Install
- MCP ready
Review links
playbooks / skill
delegation-first skill by laurigates/claude-plugins
This skill delegates implementation tasks to specialized sub-agents while the main Claude focuses on architecture and coordination.
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.