{"id":"98df170c-474a-4afa-b53d-a8c1eafae92b","entityType":"skill","slug":"crewai-bkbilal009-ai-olympics-agent","name":"AI_Olympics_Agent","canonicalUrl":"https://www.xpersona.co/skill/crewai-bkbilal009-ai-olympics-agent","canonicalPath":"/skill/crewai-bkbilal009-ai-olympics-agent","generatedAt":"2026-10-10T02:19:59.350Z","source":"GITHUB_OPENCLEW","claimStatus":"UNCLAIMED","verificationTier":"NONE","summary":{"evidence":{"source":"editorial-content","verified":true,"confidence":"high","updatedAt":"2026-10-09T05:12:16.350Z","emptyReason":null},"description":"An advanced multi-agent framework built with CrewAI, Llama 3.3, and Groq LPU for autonomous Olympic sports research. The system coordinates specialized AI agents to scrape live internet data and synthesize complex athletic metrics into professional analytical markdown reports. --- title: \"AI Olympics Agent: Autonomous Sports Research & Intelligence Ecosystem\" emoji: \"🏅\" colorFrom: \"gold\" colorTo: \"yellow\" sdk: \"docker\" pinned: true license: \"mit\" short_description: \"An advanced CrewAI multi-agent orchestrator utilizing Llama 3.3 and Groq LPU for real-time Olympic analytical reports.\" --- 🏅 AI Olympics Agent: Autonomous Sports Research & Intelligence Ecosystem **\"Synthesizing Sports Intel","descriptionLabel":"Technical summary","evidenceSummary":"Capability contract not published. No trust telemetry is available yet. Last updated 10/9/2026.","installCommand":"git clone https://github.com/bkbilal009/AI_Olympics_Agent.git","sourceUrl":"https://github.com/bkbilal009/AI_Olympics_Agent","homepage":null,"primaryLinks":[{"label":"View Source","url":"https://github.com/bkbilal009/AI_Olympics_Agent","kind":"source"}],"safetyScore":66,"overallRank":18.2,"popularityScore":0,"trustScore":null,"claimedByName":null,"isOwner":false,"seoDescription":"An advanced multi-agent framework built with CrewAI, Llama 3.3, and Groq LPU for autonomous Olympic sports research. The system coordinates specialized AI agent"},"coverage":{"evidence":{"source":"public-profile","verified":false,"confidence":"medium","updatedAt":"2026-10-09T05:12:16.350Z","emptyReason":null},"protocols":[{"protocol":"OPENCLEW","label":"OpenClaw","status":"self-declared","notes":"Declared in the public agent profile."}],"capabilities":[{"label":"crewai","status":"self-declared"},{"label":"multi-agent","status":"self-declared"}],"verifiedCount":0,"selfDeclaredCount":3,"capabilityMatrix":{"rows":[{"key":"OPENCLEW","type":"protocol","support":"unknown","confidenceSource":"profile","notes":"Listed on profile"},{"key":"crewai","type":"capability","support":"supported","confidenceSource":"profile","notes":"Declared in agent profile metadata"},{"key":"multi-agent","type":"capability","support":"supported","confidenceSource":"profile","notes":"Declared in agent profile metadata"}],"flattenedTokens":"protocol:OPENCLEW|unknown|profile capability:crewai|supported|profile capability:multi-agent|supported|profile"}},"adoption":{"evidence":{"source":"no-adoption-signals","verified":false,"confidence":"low","updatedAt":"2026-10-09T05:12:16.350Z","emptyReason":"No source adoption metrics were available."},"stars":0,"forks":0,"downloads":null,"packageName":null,"latestVersion":null,"tractionLabel":null},"release":{"evidence":{"source":"agent-index","verified":false,"confidence":"medium","updatedAt":"2026-10-09T05:12:16.349Z","emptyReason":null},"lastUpdatedAt":"2026-10-09T05:12:16.350Z","lastCrawledAt":"2026-10-09T05:12:16.349Z","lastIndexedAt":null,"nextCrawlAt":"2026-10-16T05:12:16.349Z","lastVerifiedAt":null,"highlights":[]},"execution":{"evidence":{"source":"GITHUB OPENCLEW","verified":false,"confidence":"low","updatedAt":null,"emptyReason":"No published capability contract is available yet."},"installCommand":"git clone https://github.com/bkbilal009/AI_Olympics_Agent.git","setupComplexity":"low","setupSteps":["Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.","Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data."],"contract":{"contractStatus":"missing","authModes":[],"requires":[],"forbidden":[],"supportsMcp":false,"supportsA2a":false,"supportsStreaming":false,"inputSchemaRef":null,"outputSchemaRef":null,"dataRegion":null,"contractUpdatedAt":null,"sourceUpdatedAt":null,"freshnessSeconds":null},"invocationGuide":{"preferredApi":{"snapshotUrl":"https://www.xpersona.co/api/v1/agents/crewai-bkbilal009-ai-olympics-agent/snapshot","contractUrl":"https://www.xpersona.co/api/v1/agents/crewai-bkbilal009-ai-olympics-agent/contract","trustUrl":"https://www.xpersona.co/api/v1/agents/crewai-bkbilal009-ai-olympics-agent/trust"},"curlExamples":["curl -s \"https://www.xpersona.co/api/v1/agents/crewai-bkbilal009-ai-olympics-agent/snapshot\"","curl -s \"https://www.xpersona.co/api/v1/agents/crewai-bkbilal009-ai-olympics-agent/contract\"","curl -s \"https://www.xpersona.co/api/v1/agents/crewai-bkbilal009-ai-olympics-agent/trust\""],"jsonRequestTemplate":{"query":"summarize this repo","constraints":{"maxLatencyMs":2000,"protocolPreference":["OPENCLEW"]}},"jsonResponseTemplate":{"ok":true,"result":{"summary":"...","confidence":0.9},"meta":{"source":"GITHUB_OPENCLEW","generatedAt":"2026-10-10T02:19:59.350Z"}},"retryPolicy":{"maxAttempts":3,"backoffMs":[500,1500,3500],"retryableConditions":["HTTP_429","HTTP_503","NETWORK_TIMEOUT"]}},"endpoints":{"dossierUrl":"https://www.xpersona.co/skill/crewai-bkbilal009-ai-olympics-agent/api/v1/agents/crewai-bkbilal009-ai-olympics-agent/dossier","snapshotUrl":"https://www.xpersona.co/api/v1/agents/crewai-bkbilal009-ai-olympics-agent/snapshot","contractUrl":"https://www.xpersona.co/api/v1/agents/crewai-bkbilal009-ai-olympics-agent/contract","trustUrl":"https://www.xpersona.co/api/v1/agents/crewai-bkbilal009-ai-olympics-agent/trust"}},"reliability":{"evidence":{"source":"runtime-metrics","verified":false,"confidence":"low","updatedAt":null,"emptyReason":"No trust, reliability, or runtime telemetry is available."},"trust":{"status":"unavailable","handshakeStatus":"UNKNOWN","verificationFreshnessHours":null,"reputationScore":null,"p95LatencyMs":null,"successRate30d":null,"fallbackRate":null,"attempts30d":null,"trustUpdatedAt":null,"trustConfidence":"unknown","sourceUpdatedAt":null,"freshnessSeconds":null},"decisionGuardrails":{"doNotUseIf":["Contract metadata is missing or unavailable for deterministic execution."],"safeUseWhen":[],"riskFlags":["missing_or_unavailable_contract","trust_data_unavailable","schema_references_missing"],"operationalConfidence":"low"},"executionMetrics":{"observedLatencyMsP50":null,"observedLatencyMsP95":null,"estimatedCostUsd":null,"uptime30d":null,"rateLimitRpm":null,"rateLimitBurst":null,"lastVerifiedAt":null,"verificationSource":null},"runtimeMetrics":{"successRate":null,"avgLatencyMs":null,"avgCostUsd":null,"hallucinationRate":null,"retryRate":null,"disputeRate":null,"p50Latency":null,"p95Latency":null,"lastUpdated":null}},"benchmarks":{"evidence":{"source":"no-benchmark-data","verified":false,"confidence":"low","updatedAt":null,"emptyReason":"No benchmark suites or observed failure patterns are available."},"suites":[],"failurePatterns":[]},"artifacts":{"evidence":{"source":"GITHUB OPENCLEW","verified":false,"confidence":"high","updatedAt":"2026-10-09T05:12:16.350Z","emptyReason":null},"readme":"---\ntitle: \"AI Olympics Agent: Autonomous Sports Research & Intelligence Ecosystem\"\nemoji: \"🏅\"\ncolorFrom: \"gold\"\ncolorTo: \"yellow\"\nsdk: \"docker\"\npinned: true\nlicense: \"mit\"\nshort_description: \"An advanced CrewAI multi-agent orchestrator utilizing Llama 3.3 and Groq LPU for real-time Olympic analytical reports.\"\n---\n\n# 🏅 AI Olympics Agent: Autonomous Sports Research & Intelligence Ecosystem\n\n> **\"Synthesizing Sports Intelligence, Synchronizing Multi-Agent Workflows, Documenting Athletic History.\"**\n> \n> **AI Olympics Agent** is a production-ready, highly decoupled Multi-Agent Autonomous Systems framework architected by **Muhammad Bilal**. Powered by the state-of-the-art **CrewAI** framework and accelerated by **Groq LPU** inference, this cognitive ecosystem coordinates specialized AI agents that dynamically query live internet data, structure raw sports metrics, and compile elite-level markdown analytical intelligence reports regarding the Olympic Games.\n\n![Python](https://img.shields.io/badge/Python-3.10%20%7C%203.11-3776AB?style=for-the-badge&logo=python&logoColor=white)\n![CrewAI](https://img.shields.io/badge/Framework-CrewAI_v0.28+-000000?style=for-the-badge)\n![Groq](https://img.shields.io/badge/Inference-Groq_Cloud_LPU-f3d122?style=for-the-badge)\n![Llama 3.3](https://img.shields.io/badge/Model-Llama_3.3_70B-0467DF?style=for-the-badge&logo=meta&logoColor=white)\n![MIT License](https://img.shields.io/badge/License-MIT-green?style=for-the-badge)\n\n---\n\n## 🏗️ Technical Architecture & Advanced Agentic Workflow\n\nUnlike standard static Large Language Models (LLMs) constrained by training knowledge cutoff dates, this system implements an autonomous execution pipeline. The orchestrator separates concerns into specialized nodes that collaborate via an asymmetric task-execution graph.\n\n\n```\n\n```\n              ┌────────────────────────────────────────┐\n              │          User Prompt/Input             │\n              │   (e.g., \"Paris 2024 Analysis\")        │\n              └───────────────────┬────────────────────┘\n                                  │\n                                  ▼\n              ┌────────────────────────────────────────┐\n              │      src/ai_olympics_agent/main.py     │\n              │   (Initializes Inputs & Triggers Crew) │\n              └───────────────────┬────────────────────┘\n                                  │\n                                  ▼\n              ┌────────────────────────────────────────┐\n              │      src/ai_olympics_agent/crew.py     │\n              │  (Orchestrates Agents, Tasks & Tools)  │\n              └───────────────────┬────────────────────┘\n                                  │\n       ┌──────────────────────────┴──────────────────────────┐\n       ▼                                                     ▼\n\n```\n\n┌─────────────────────────────────────┐               ┌─────────────────────────────────────┐\n│    🔍 OLYMPICS RESEARCHER AGENT     │               │     ✍️ SPORTS CONTENT WRITER AGENT   │\n├─────────────────────────────────────┤               ├─────────────────────────────────────┤\n│ • Role: Senior Sports Statistician  │               │ • Role: Lead Sports Editor          │\n│ • Config: agents.yaml (researcher)  │               │ • Config: agents.yaml (writer)      │\n│ • Tool: SerperDevTool (Web Search)  │               │ • Tool: Pure Semantic Synthesis     │\n│ • Task: Comprehensive Data Scrape   │               │ • Task: Markdown Report Compiling   │\n└──────────────────┬──────────────────┘               └──────────────────▲──────────────────┘\n│                                                     │\n└─────────────── [Context Handshake] ─────────────────┘\n\n```\n\n### Deep-Dive Component Analysis\n\n#### 1. 🔍 The Olympics Researcher Agent (The Analytics Scout)\n* **Role & Backstory:** Operating as a Senior Sports Data Analyst, this agent specializes in scanning distributed data nodes, verifying raw statistical tables, and filtering out misinformation.\n* **Operational Goal:** Extract factual, real-time Olympic metadata, medal tallies, timeline sequences, and record-breaking performance specs.\n* **Tool Integration:** Employs `SerperDevTool` to fire structured search queries directly into Google indices, mapping real-time sports results into memory cache.\n\n#### 2. ✍️ The Sports Content Writer Agent (The Master Storyteller)\n* **Role & Backstory:** A legendary sports journalist with decades of media publication experience. It understands formatting dynamics, cognitive retention hooks, and highly formal analytic reporting styles.\n* **Operational Goal:** Consume raw, unfiltered context snippets emitted by the Researcher Agent and synthesize them into high-performance markdown assets.\n* **Formatting Guardrails:** Enforces clean headers, automated data tables, bulleted takeaway points, and professional structural syntax.\n\n---\n\n## 📂 Production Codebase Directory Map\n\nThe system utilizes a structured python layout leveraging decoupled configuration layers to cleanly separate logic from parameter definitions.\n\n\n```\n\nAI_Olympics_Agent/\n└── project/\n├── src/\n│   └── ai_olympics_agent/\n│       ├── config/\n│       │   ├── agents.yaml      # Declarative definitions of agent backstories and roles\n│       │   └── tasks.yaml       # Definitions of precise task expectations and criteria\n│       ├── tools/\n│       │   └── **init**.py      # Custom modular agent tool hooks\n│       ├── **init**.py          # Marks namespace boundaries\n│       ├── crew.py              # Main Orchestrator (Binds Agents, LLMs, and Tasks together)\n│       └── main.py              # CLI Entrypoint for initialization, training, and execution\n├── pyproject.toml               # Poetry package and project structural definitions\n├── requirements.txt             # Flat list of standard environment dependencies\n└── README.md                    # System documentation front-facing manual\n\n```\n\n---\n\n## 📋 Declarative YAML Configurations\n\n### `src/ai_olympics_agent/config/agents.yaml`\n```yaml\nresearcher:\n  role: >\n    Senior Olympic Sports Researcher\n  goal: >\n    Find, clean, and consolidate highly precise real-time historical and live performance metrics regarding the Olympics.\n  backstory: >\n    You are an elite sports archivist and digital investigator. Your specialty lies in scraping complex timelines, mapping medal configurations, verifying records, and bypassing knowledge cutoffs using real-time search mechanics.\n\nwriter:\n  role: >\n    Lead Sports Content Journalist\n  goal: >\n    Convert raw quantitative sports telemetry into publication-grade analytical reports and high-retention markdown documentation.\n  backstory: >\n    You are a globally acclaimed sports journalist. You excel at taking raw research briefs, extracting human-interest angles, creating highly organized structural data matrices, and ensuring flawless typographical styling.\n\n```\n\n### `src/ai_olympics_agent/config/tasks.yaml`\n\n```yaml\nresearch_task:\n  description: >\n    Conduct an exhaustive search on the following query: {topic}. Target precise timelines, track key athletic standouts, pull comprehensive medal distributions, and outline historical constraints.\n  expected_output: >\n    A fully raw, structured factual inventory containing authenticated data arrays, links, numbers, and categorical breakdowns.\n\nwrite_task:\n  description: >\n    Take the verified material supplied by the research node and assemble a publication-ready analytics brief about {topic}. The document must be educational, include structured markdown grids for numbers, and be formatted for instant production deployment.\n  expected_output: >\n    A professional, highly detailed Markdown document (.md layout) with distinct topical sections, summary highlights, and comprehensive data tables.\n\n```\n\n---\n\n## 🛠️ Complete Tech Stack Specs\n\n* **Orchestration Engine:** `CrewAI (v0.28+)` managing sequential state pipelines, token pooling, and asymmetric memory retention.\n* **Compute Acceleration:** `Groq LPU (Language Processing Unit)` utilizing highly optimized hardware clusters for zero-lag token inference.\n* **Foundation Cognitive Engine:** `Llama 3.3 70B` — optimized for logic, function calling, deep instruction following, and highly descriptive context reasoning.\n* **Web Scrape Pipeline:** `Serper.dev engine` converting google semantic queries into direct JSON payloads.\n* **Environment Management:** Configured for cross-platform compliance using `Python 3.10` up to `Python 3.12`.\n\n---\n\n## 🚀 Execution & Operational Playbook\n\n### Step 1: Clone the Core Artifact\n\n```bash\ngit clone [https://github.com/bkbilal009/AI_Olympics_Agent.git](https://github.com/bkbilal009/AI_Olympics_Agent.git)\ncd AI_Olympics_Agent/project\n\n```\n\n### Step 2: Configure Secret Variable Key Rings\n\nCreate a `.env` file directly inside the `project/` directory to interface with secure nodes:\n\n```env\n# Groq LPU Integration Routing\nOPENAI_API_BASE=\"[https://api.groq.com/openai/v1](https://api.groq.com/openai/v1)\"\nOPENAI_MODEL_NAME=\"llama3-70b-8192\" \nOPENAI_API_KEY=\"gsk_your_actual_production_groq_key_here\"\n\n# Web Crawler Search Matrix Authentication\nSERPER_API_KEY=\"your_serper_api_credential_hash_here\"\n\n```\n\n### Step 3: Install Core Engine Libraries\n\nExecute standard compilation:\n\n```bash\npip install -r requirements.txt\n\n```\n\n*Or, if running an isolated Poetry workspace environment:*\n\n```bash\npoetry lock\npoetry install\n\n```\n\n### Step 4: Execute the Engine\n\nRun the main operational pipeline to prompt the agent sequence:\n\n```bash\npython src/ai_olympics_agent/main.py\n\n```\n\nUpon prompt activation, feed in any analytical query (e.g., `Pakistan's performance history at the Olympic Games or Javelin throw evolution`) and track the runtime execution logs as agents trade memory buffers across the terminal interface.\n\n---\n\n## ⚠️ Troubleshooting & Failure Guardrails\n\n1. **RateLimitError (Groq Token Exhaustion):**\n* *Cause:* Heavy payload bursts hitting the free tier limits of Groq API.\n* *Fix:* Navigate to `src/ai_olympics_agent/crew.py` and implement a throttling cooldown state by appending `max_rpm=10` inside the Agent configurations.\n\n\n2. **Serper Tool Empty Payloads:**\n* *Cause:* Invalid or expired `SERPER_API_KEY` resulting in 403 authorization failures.\n* *Fix:* Verify your environment parameters using `echo $SERPER_API_KEY` on your terminal instance to confirm string registration.\n\n\n\n---\n\n## 👑 Intellectual Heritage & Mentorship\n\nThis advanced production agent workflow was made possible through deep algorithm development training and architecture reviews provided by **Dr. Zafar Shahid** and the technical advisory core at **iCodeGuru**. Their emphasis on mastering abstract Data Structures and Algorithms (DSA) and building modular, production-grade Agentic systems provided the framework required to develop this orchestration ecosystem.\n\n---\n\n## 👨‍💻 Developer Dossier\n\n**Muhammad Bilal** *Aspiring AI Engineer | Agentic Workflow Architect | Competitive Programmer*\n\n### 🌐 Global Routing & Touchpoints:\n\n* **GitHub Repository Hub:** [📂 bkbilal009](https://github.com/bkbilal009)\n* **LinkedIn Professional Interface:** [💼 Muhammad Bilal](https://www.linkedin.com/in/muhammad-bilal-dev/)\n* **Core Engineering Portfolio:** [🚀 AuraPath AI & Vortex Systems](https://github.com/bkbilal009?tab=repositories)\n\n---\n\n*Data. Synchronization. Autonomous Supremacy.*\n--- \n\n```\n","readmeExcerpt":"--- title: \"AI Olympics Agent: Autonomous Sports Research & Intelligence Ecosystem\" emoji: \"🏅\" colorFrom: \"gold\" colorTo: \"yellow\" sdk: \"docker\" pinned: true license: \"mit\" short_description: \"An advanced CrewAI multi-agent orchestrator utilizing Llama 3.3 and Groq LPU for real-time Olympic analytical reports.\" --- 🏅 AI Olympics Agent: Autonomous Sports Research & Intelligence Ecosystem **\"Synthesizing Sports Intel","codeSnippets":[],"executableExamples":[{"language":"text","snippet":"┌─────────────────────────────────────┐               ┌─────────────────────────────────────┐\n│    🔍 OLYMPICS RESEARCHER AGENT     │               │     ✍️ SPORTS CONTENT WRITER AGENT   │\n├─────────────────────────────────────┤               ├─────────────────────────────────────┤\n│ • Role: Senior Sports Statistician  │               │ • Role: Lead Sports Editor          │\n│ • Config: agents.yaml (researcher)  │               │ • Config: agents.yaml (writer)      │\n│ • Tool: SerperDevTool (Web Search)  │               │ • Tool: Pure Semantic Synthesis     │\n│ • Task: Comprehensive Data Scrape   │               │ • Task: Markdown Report Compiling   │\n└──────────────────┬──────────────────┘               └──────────────────▲──────────────────┘\n│                                                     │\n└─────────────── [Context Handshake] ─────────────────┘"},{"language":"text","snippet":"AI_Olympics_Agent/\n└── project/\n├── src/\n│   └── ai_olympics_agent/\n│       ├── config/\n│       │   ├── agents.yaml      # Declarative definitions of agent backstories and roles\n│       │   └── tasks.yaml       # Definitions of precise task expectations and criteria\n│       ├── tools/\n│       │   └── **init**.py      # Custom modular agent tool hooks\n│       ├── **init**.py          # Marks namespace boundaries\n│       ├── crew.py              # Main Orchestrator (Binds Agents, LLMs, and Tasks together)\n│       └── main.py              # CLI Entrypoint for initialization, training, and execution\n├── pyproject.toml               # Poetry package and project structural definitions\n├── requirements.txt             # Flat list of standard environment dependencies\n└── README.md                    # System documentation front-facing manual"},{"language":"yaml","snippet":"researcher:\n  role: >\n    Senior Olympic Sports Researcher\n  goal: >\n    Find, clean, and consolidate highly precise real-time historical and live performance metrics regarding the Olympics.\n  backstory: >\n    You are an elite sports archivist and digital investigator. Your specialty lies in scraping complex timelines, mapping medal configurations, verifying records, and bypassing knowledge cutoffs using real-time search mechanics.\n\nwriter:\n  role: >\n    Lead Sports Content Journalist\n  goal: >\n    Convert raw quantitative sports telemetry into publication-grade analytical reports and high-retention markdown documentation.\n  backstory: >\n    You are a globally acclaimed sports journalist. You excel at taking raw research briefs, extracting human-interest angles, creating highly organized structural data matrices, and ensuring flawless typographical styling."},{"language":"yaml","snippet":"research_task:\n  description: >\n    Conduct an exhaustive search on the following query: {topic}. Target precise timelines, track key athletic standouts, pull comprehensive medal distributions, and outline historical constraints.\n  expected_output: >\n    A fully raw, structured factual inventory containing authenticated data arrays, links, numbers, and categorical breakdowns.\n\nwrite_task:\n  description: >\n    Take the verified material supplied by the research node and assemble a publication-ready analytics brief about {topic}. The document must be educational, include structured markdown grids for numbers, and be formatted for instant production deployment.\n  expected_output: >\n    A professional, highly detailed Markdown document (.md layout) with distinct topical sections, summary highlights, and comprehensive data tables."},{"language":"bash","snippet":"git clone [https://github.com/bkbilal009/AI_Olympics_Agent.git](https://github.com/bkbilal009/AI_Olympics_Agent.git)\ncd AI_Olympics_Agent/project"},{"language":"env","snippet":"# Groq LPU Integration Routing\nOPENAI_API_BASE=\"[https://api.groq.com/openai/v1](https://api.groq.com/openai/v1)\"\nOPENAI_MODEL_NAME=\"llama3-70b-8192\" \nOPENAI_API_KEY=\"gsk_your_actual_production_groq_key_here\"\n\n# Web Crawler Search Matrix Authentication\nSERPER_API_KEY=\"your_serper_api_credential_hash_here\""}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[],"languages":["python"],"docsSourceLabel":"GITHUB OPENCLEW","editorialOverview":"An advanced multi-agent framework built with CrewAI, Llama 3.3, and Groq LPU for autonomous Olympic sports research. The system coordinates specialized AI agents to scrape live internet data and synthesize complex athletic metrics into professional analytical markdown reports. --- title: \"AI Olympics Agent: Autonomous Sports Research & Intelligence Ecosystem\" emoji: \"🏅\" colorFrom: \"gold\" colorTo: \"yellow\" sdk: \"docker\" pinned: true license: \"mit\" short_description: \"An advanced CrewAI multi-agent orchestrator utilizing Llama 3.3 and Groq LPU for real-time Olympic analytical reports.\" --- 🏅 AI Olympics Agent: Autonomous Sports Research & Intelligence Ecosystem **\"Synthesizing Sports Intel","editorialQuality":{"score":100,"threshold":65,"status":"ready","wordCount":402,"uniquenessScore":65,"reasons":[]}},"media":{"evidence":{"source":"no-media","verified":false,"confidence":"low","updatedAt":"2026-10-09T05:12:16.350Z","emptyReason":"No screenshots, media assets, or demo links are available."},"primaryImageUrl":null,"mediaAssetCount":0,"assets":[],"demoUrl":null},"ownerResources":{"evidence":{"source":"unclaimed","verified":false,"confidence":"low","updatedAt":"2026-10-09T05:12:16.350Z","emptyReason":"This page has not been claimed by the agent owner."},"hasCustomPage":false,"customPageUpdatedAt":null,"customLinks":[],"structuredLinks":{"docsUrl":null,"demoUrl":null,"supportUrl":null,"pricingUrl":null,"statusUrl":null},"customPage":null},"relatedAgents":{"evidence":{"source":"protocol-neighbors","verified":false,"confidence":"medium","updatedAt":"2026-10-10T02:19:59.350Z","emptyReason":null},"items":[{"id":"8ebccd8e-3863-4187-8355-c3f14e1f9edf","entityType":"agent","canonicalPath":"/agent/iofficeai-aionui","slug":"iofficeai-aionui","name":"AionUi","description":"Free, local, open-source 24/7 Cowork app and OpenClaw for Gemini CLI, Claude Code, Codex, OpenCode, Qwen Code, Goose CLI, Auggie, and more | 🌟 Star if you like it!","url":"https://github.com/iOfficeAI/AionUi","homepage":"https://www.aionui.com","source":"GITHUB_REPOS","protocols":["MCP","OPENCLAW"],"capabilities":[],"safetyScore":100,"overallRank":70,"updatedAt":"2026-10-09T19:11:12.944Z","createdAt":"2026-02-25T03:38:16.584Z","downloads":null},{"id":"b917f68a-ebff-438e-84f8-3f4b2494c0bc","entityType":"agent","canonicalPath":"/agent/activepieces-activepieces","slug":"activepieces-activepieces","name":"activepieces","description":"AI Agents & MCPs & AI Workflow Automation • (~400 MCP servers for AI agents) • AI Automation / AI Agent with MCPs • AI Workflows & AI Agents • MCPs for AI Agents","url":"https://github.com/activepieces/activepieces","homepage":"https://www.activepieces.com","source":"GITHUB_REPOS","protocols":["OPENCLAW"],"capabilities":[],"safetyScore":100,"overallRank":70,"updatedAt":"2026-04-15T02:22:12.426Z","createdAt":"2026-02-25T03:38:12.412Z","downloads":null},{"id":"5cb26759-3a39-483f-94cf-276a98c13bb8","entityType":"agent","canonicalPath":"/agent/cherryhq-cherry-studio","slug":"cherryhq-cherry-studio","name":"cherry-studio","description":"AI productivity studio with smart chat, autonomous agents, and 300+ assistants. Unified access to frontier LLMs","url":"https://github.com/CherryHQ/cherry-studio","homepage":"https://cherry-ai.com","source":"GITHUB_REPOS","protocols":["MCP","OPENCLAW"],"capabilities":[],"safetyScore":100,"overallRank":70,"updatedAt":"2026-04-11T14:38:40.986Z","createdAt":"2026-02-25T03:38:19.379Z","downloads":null},{"id":"6f6582d0-5d76-4f0f-b81d-86520247950b","entityType":"agent","canonicalPath":"/agent/copilotkit-copilotkit","slug":"copilotkit-copilotkit","name":"CopilotKit","description":"The Frontend for Agents & Generative UI. React + Angular","url":"https://github.com/CopilotKit/CopilotKit","homepage":"https://docs.copilotkit.ai","source":"GITHUB_REPOS","protocols":["OPENCLAW"],"capabilities":[],"safetyScore":100,"overallRank":70,"updatedAt":"2026-03-25T09:50:57.846Z","createdAt":"2026-02-25T03:39:14.617Z","downloads":null}],"links":{"hub":"/agent","source":"/agent/source/github_openclew","protocols":[{"label":"OpenClaw","href":"/agent/protocol/openclew"}]}}}