{"id":"5ba2d813-5d33-4b39-914f-d8af0db58ba3","entityType":"agent","slug":"crewai-ratnesh-181998-agentic-gen-ai-frameworks-mcp-vectordb","name":"Agentic-Gen-AI-Frameworks-MCP-VectorDB","canonicalUrl":"https://www.xpersona.co/agent/crewai-ratnesh-181998-agentic-gen-ai-frameworks-mcp-vectordb","canonicalPath":"/agent/crewai-ratnesh-181998-agentic-gen-ai-frameworks-mcp-vectordb","generatedAt":"2026-10-10T07:15:10.406Z","source":"GITHUB_REPOS","claimStatus":"UNCLAIMED","verificationTier":"NONE","summary":{"evidence":{"source":"editorial-content","verified":true,"confidence":"high","updatedAt":"2026-10-09T18:04:02.399Z","emptyReason":null},"description":"A comprehensive collection of modern Agentic AI and Generative AI frameworks used to build autonomous, multi-agent, and production-ready AI systems. Covers LangChain, LangGraph, CrewAI, AutoGen, MCP, RAG pipelines, LLM orchestration, observability, APIs, and cloud deployment across AWS, GCP, and Azure. --- <div align=\"center\"> <img src=\"https://capsule-render.vercel.app/api?type=waving&color=gradient&height=240&text=Agentic%20AI%20Gen%20AI%20Frameworks&fontSize=42&fontColor=ffffff&animation=fadeIn\" /> </div> --- Frameworks together enable end-to-end Agentic AI systems — from LLM orchestration, memory, multi-agent collaboration, RAG, observability, automation, APIs, to cloud-scale deployment. --- 🧠 Agentic AI & GeN","descriptionLabel":"Technical summary","evidenceSummary":"Capability contract not published. No trust telemetry is available yet. 2 GitHub stars reported by the source. Last updated 10/9/2026.","installCommand":null,"sourceUrl":"https://github.com/Ratnesh-181998/Agentic-Gen-AI-Frameworks-MCP-VectorDB","homepage":null,"primaryLinks":[{"label":"View Source","url":"https://github.com/Ratnesh-181998/Agentic-Gen-AI-Frameworks-MCP-VectorDB","kind":"source"}],"safetyScore":66,"overallRank":21.2,"popularityScore":12,"trustScore":null,"claimedByName":null,"isOwner":false,"seoDescription":"A comprehensive collection of modern Agentic AI and Generative AI frameworks used to build autonomous, multi-agent, and production-ready AI systems. Covers Lang"},"coverage":{"evidence":{"source":"public-profile","verified":false,"confidence":"medium","updatedAt":"2026-10-09T18:04:02.399Z","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":"GITHUB REPOS","verified":false,"confidence":"medium","updatedAt":"2026-10-09T18:04:02.399Z","emptyReason":null},"stars":2,"forks":0,"downloads":null,"packageName":null,"latestVersion":null,"tractionLabel":"2 GitHub stars"},"release":{"evidence":{"source":"agent-index","verified":false,"confidence":"medium","updatedAt":"2026-10-09T18:04:02.393Z","emptyReason":null},"lastUpdatedAt":"2026-10-09T18:04:02.399Z","lastCrawledAt":"2026-10-09T18:04:02.393Z","lastIndexedAt":null,"nextCrawlAt":"2026-10-16T18:04:02.393Z","lastVerifiedAt":null,"highlights":[]},"execution":{"evidence":{"source":"GITHUB REPOS","verified":false,"confidence":"low","updatedAt":null,"emptyReason":"No published capability contract is available yet."},"installCommand":null,"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-ratnesh-181998-agentic-gen-ai-frameworks-mcp-vectordb/snapshot","contractUrl":"https://www.xpersona.co/api/v1/agents/crewai-ratnesh-181998-agentic-gen-ai-frameworks-mcp-vectordb/contract","trustUrl":"https://www.xpersona.co/api/v1/agents/crewai-ratnesh-181998-agentic-gen-ai-frameworks-mcp-vectordb/trust"},"curlExamples":["curl -s \"https://www.xpersona.co/api/v1/agents/crewai-ratnesh-181998-agentic-gen-ai-frameworks-mcp-vectordb/snapshot\"","curl -s \"https://www.xpersona.co/api/v1/agents/crewai-ratnesh-181998-agentic-gen-ai-frameworks-mcp-vectordb/contract\"","curl -s \"https://www.xpersona.co/api/v1/agents/crewai-ratnesh-181998-agentic-gen-ai-frameworks-mcp-vectordb/trust\""],"jsonRequestTemplate":{"query":"summarize this repo","constraints":{"maxLatencyMs":2000,"protocolPreference":["OPENCLEW"]}},"jsonResponseTemplate":{"ok":true,"result":{"summary":"...","confidence":0.9},"meta":{"source":"GITHUB_REPOS","generatedAt":"2026-10-10T07:15:10.406Z"}},"retryPolicy":{"maxAttempts":3,"backoffMs":[500,1500,3500],"retryableConditions":["HTTP_429","HTTP_503","NETWORK_TIMEOUT"]}},"endpoints":{"dossierUrl":"https://www.xpersona.co/api/v1/agents/crewai-ratnesh-181998-agentic-gen-ai-frameworks-mcp-vectordb/dossier","snapshotUrl":"https://www.xpersona.co/api/v1/agents/crewai-ratnesh-181998-agentic-gen-ai-frameworks-mcp-vectordb/snapshot","contractUrl":"https://www.xpersona.co/api/v1/agents/crewai-ratnesh-181998-agentic-gen-ai-frameworks-mcp-vectordb/contract","trustUrl":"https://www.xpersona.co/api/v1/agents/crewai-ratnesh-181998-agentic-gen-ai-frameworks-mcp-vectordb/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 REPOS","verified":false,"confidence":"high","updatedAt":"2026-10-09T18:04:02.399Z","emptyReason":null},"readme":"\n---\n\n<div align=\"center\">\n  <img src=\"https://capsule-render.vercel.app/api?type=waving&color=gradient&height=240&text=Agentic%20AI%20Gen%20AI%20Frameworks&fontSize=42&fontColor=ffffff&animation=fadeIn\" />\n</div>\n\n---\n\n# Frameworks together enable end-to-end Agentic AI systems — from LLM orchestration, memory, multi-agent collaboration, RAG, observability, automation, APIs, to cloud-scale deployment.\n\n---\n\n# 🧠 Agentic AI & GeN AI Frameworks & MCP & VectorDB\n\n## 1. LangChain\n- Purpose: Core framework for building LLM-powered applications, chains, tools, and RAG systems\n- 🌐 Website: https://www.langchain.com\n- 📘 Docs: https://python.langchain.com/docs\n\n## 2. LCEL (LangChain Expression Language)\n- Purpose: Declarative way to build composable LLM pipelines\n- 🌐 Website: https://www.langchain.com\n- 📘 Docs: https://python.langchain.com/docs/expression_language\n\n## 3. LangGraph\n- Purpose: Graph-based agent workflows, state & memory management, multi-agent systems\n- 🌐 Website: https://langchain-ai.github.io/langgraph\n- 📘 Docs: https://langchain-ai.github.io/langgraph/concepts\n\n## 4. LangSmith\n- Purpose: Tracing, debugging, evaluation, and monitoring of LLM & agent workflows\n- 🌐 Website: https://smith.langchain.com\n- 📘 Docs: https://docs.smith.langchain.com\n\n## 5. CrewAI\n- Purpose: Multi-agent collaboration framework with role-based agents\n- 🌐 Website: https://www.crewai.com\n- 📘 Docs: https://docs.crewai.com\n\n## 6. AutoGen\n- Purpose: Autonomous multi-agent conversations and task execution\n- 🌐 Website: https://microsoft.github.io/autogen\n- 📘 Docs: https://microsoft.github.io/autogen/docs\n\n## 7.1 Phidata & Agno\n- Purpose: Lightweight agent framework with RAG, observability, and agent UIs\n- 🌐 Website: https://www.agno.com/\n- 📘 Docs: https://docs.agno.com/\n- https://docs.phidata.com/introduction\n- https://docs.phidata.com/reference/agents/python\n- https://github.com/agno-agi/phidata\n\n## 7.2  LlamaIndex\n- Purpose: LlamaIndex delivers industry-leading document parsing and AI agent frameworks. Transform complex documents into automated workflows\n- 🌐 Website: https://www.llamaindex.ai/\n- 📘 Docs: https://developers.llamaindex.ai/python/framework/\n- https://github.com/run-llama/llama_index\n  \n## 7.3 Haystack (deepset)\n- Production-grade framework for RAG + agentic pipeline building with search and reasoning components.\n- 🌐 Official Website: https://www.deepset.ai/\n- 📘 Documentation: https://docs.deepset.ai/\n\n## 7.4 ReAct\n- A methodology/framework for chaining reasoning and actions in language models.\n- 🌐 ReAct Paper & Community Examples:\n- https://arxiv.org/abs/2210.03629\n- 📘 Implementations: Provided in LangChain / community repos\n  \n## 8. LangFlow\n- Purpose: Low-code / no-code UI for building LangChain-based LLM apps\n- 🌐 Website: https://www.langflow.org\n- 📘 Docs: https://docs.langflow.org\n\n## 9. n8n\n- Purpose: Workflow automation + AI agents + RAG + tool integrations\n- 🌐 Website: https://n8n.io\n- 📘 Docs: https://docs.n8n.io\n\n## 10. Model Context Protocol (MCP)\n- Purpose: Standardized protocol for connecting LLMs with tools, resources, and data\n- 🌐 Website: https://modelcontextprotocol.io\n- 📘 Docs: https://modelcontextprotocol.io/docs\n\n## 11.1 FastAPI\n- Purpose: High-performance APIs for serving agents and LLM pipelines\n- 🌐 Website: https://fastapi.tiangolo.com\n- 📘 Docs: https://fastapi.tiangolo.com/tutorial\n\n## 11.2 Flask API\n- Purpose: Flask is a lightweight WSGI web application framework. It is designed to make getting started quick and easy, with the ability to scale up to complex applications.\n- 🌐 Website: https://flask.palletsprojects.com/en/stable/api/\n- 📘 Docs: https://flask.palletsprojects.com/en/stable/api/\n- https://github.com/flask-api/flask-api\n\n## 11.3 Apache HTTP & HTTPS & HTTPD Web Server\n- The Apache HTTP Server Project is an effort to develop and maintain an open-source HTTP server for modern operating systems including UNIX and Windows.\n- https://github.com/apache/httpd\n- https://httpd.apache.org/\n- https://httpd.apache.org/docs/\n- https://www.youtube.com/watch?v=NACRKwkXKO0\n  \n## 12. BentoML\n- Purpose: Model serving & deployment for LLMs and AI agents\n- 🌐 Website: https://www.bentoml.com\n- 📘 Docs: https://docs.bentoml.com\n\n## 13. Gradio\n- Purpose: UI for LLMs, GenAI demos, and agent interaction\n- 🌐 Website: https://www.gradio.app\n- 📘 Docs: https://www.gradio.app/docs\n\n## 14. Streamlit\n- Purpose: Rapid frontend for AI/ML & GenAI applications\n- 🌐 Website: https://streamlit.io\n- 📘 Docs: https://docs.streamlit.io\n\n## 15. Vector Databases (used across modules)\n- FAISS → https://github.com/facebookresearch/faiss\n- ChromaDB → https://www.trychroma.com\n- Docs: FAISS: https://faiss.ai & Chroma: https://docs.trychroma.com\n- Pinecone https://www.pinecone.io/\n- AstraDb https://docs.datastax.com/en/astra-db-serverless/get-started/quickstart.html\n- \n## 16. LLM Providers (Integrated)\n- OpenAI – https://platform.openai.com/docs\n- Anthropic (Claude) – https://docs.anthropic.com\n- Google Gemini – https://ai.google.dev/docs\n- Grok (xAI) – https://x.ai\n- Groq AI - https://console.groq.com/docs/quickstart\n\n## 17. Cloud & LLMOps Stack (Agent Deployment)\n- Docker – https://docs.docker.com\n- AWS (EC2, S3, ECR, Bedrock) – https://docs.aws.amazon.com\n- GitHub Actions – https://docs.github.com/actions\n\n\n\n\n---\n\n<img src=\"https://capsule-render.vercel.app/api?type=rect&color=gradient&customColorList=24,20,12,6&height=3\" width=\"100%\">\n\n---\n\n# 📞 **CONTACT & NETWORKING** 📞\n\n\n## 💼 Professional Networks\n\n[![LinkedIn](https://img.shields.io/badge/💼_LinkedIn-0077B5?style=for-the-badge&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/ratneshkumar1998/)\n[![GitHub](https://img.shields.io/badge/🐙_GitHub-181717?style=for-the-badge&logo=github&logoColor=white)](https://github.com/Ratnesh-181998)\n[![X](https://img.shields.io/badge/X-000000?style=for-the-badge&logo=x&logoColor=white)](https://x.com/RatneshS16497)\n[![Portfolio](https://img.shields.io/badge/🌐_Portfolio-FF6B6B?style=for-the-badge&logo=google-chrome&logoColor=white)](https://share.streamlit.io/user/ratnesh-181998)\n[![Email](https://img.shields.io/badge/✉️_Email-D14836?style=for-the-badge&logo=gmail&logoColor=white)](mailto:rattudacsit2021gate@gmail.com)\n[![Medium](https://img.shields.io/badge/Medium-000000?style=for-the-badge&logo=medium&logoColor=white)](https://medium.com/@rattudacsit2021gate)\n[![Stack Overflow](https://img.shields.io/badge/Stack_Overflow-F58025?style=for-the-badge&logo=stack-overflow&logoColor=white)](https://stackoverflow.com/users/32068937/ratnesh-kumar)\n\n## 🚀 AI/ML & Data Science  [AI/ML 1620+ Problem Solved](https://github.com/Ratnesh-181998/DSML)\n[![Streamlit](https://img.shields.io/badge/Streamlit-FF4B4B?style=for-the-badge&logo=streamlit&logoColor=white)](https://share.streamlit.io/user/ratnesh-181998)\n[![HuggingFace](https://img.shields.io/badge/HuggingFace-FFD21E?style=for-the-badge&logo=huggingface&logoColor=black)](https://huggingface.co/RattuDa98)\n[![Kaggle](https://img.shields.io/badge/Kaggle-20BEFF?style=for-the-badge&logo=kaggle&logoColor=white)](https://www.kaggle.com/rattuda)\n\n## 💻 Competitive Programming [Including all coding plateform's 5000+ Problems/Questions solved](https://github.com/Ratnesh-181998/Algorithms-and-Data-Structures)\n[![LeetCode](https://img.shields.io/badge/LeetCode-FFA116?style=for-the-badge&logo=leetcode&logoColor=black)](https://leetcode.com/u/Ratnesh_1998/)\n[![HackerRank](https://img.shields.io/badge/HackerRank-00EA64?style=for-the-badge&logo=hackerrank&logoColor=black)](https://www.hackerrank.com/profile/rattudacsit20211)\n[![CodeChef](https://img.shields.io/badge/CodeChef-5B4638?style=for-the-badge&logo=codechef&logoColor=white)](https://www.codechef.com/users/ratnesh_181998)\n[![Codeforces](https://img.shields.io/badge/Codeforces-1F8ACB?style=for-the-badge&logo=codeforces&logoColor=white)](https://codeforces.com/profile/Ratnesh_181998)\n[![GeeksforGeeks](https://img.shields.io/badge/GeeksforGeeks-2F8D46?style=for-the-badge&logo=geeksforgeeks&logoColor=white)](https://www.geeksforgeeks.org/profile/ratnesh1998)\n[![HackerEarth](https://img.shields.io/badge/HackerEarth-323754?style=for-the-badge&logo=hackerearth&logoColor=white)](https://www.hackerearth.com/@ratnesh138/)\n[![InterviewBit](https://img.shields.io/badge/InterviewBit-4285F4?style=for-the-badge&logo=google&logoColor=white)](https://www.interviewbit.com/profile/rattudacsit2021gate_d9a25bc44230/)\n\n\n---\n\n## 📊 **GitHub Stats & Metrics** 📊\n\n\n\n![Profile Views](https://komarev.com/ghpvc/?username=Ratnesh-181998&color=blueviolet&style=for-the-badge&label=PROFILE+VIEWS)\n\n\n\n\n<img \n  src=\"https://streak-stats.demolab.com?user=Ratnesh-181998&theme=radical&hide_border=true&background=0D1117&stroke=4ECDC4&ring=F38181&fire=FF6B6B&currStreakLabel=4ECDC4\"\n  alt=\"GitHub Streak Stats\"\nwidth=\"48%\"/>\n\n<img src=\"https://github-readme-activity-graph.vercel.app/graph?username=Ratnesh-181998&theme=react-dark&hide_border=true&bg_color=0D1117&color=4ECDC4&line=F38181&point=FF6B6B\" width=\"48%\" />\n\n---\n\n<img src=\"https://readme-typing-svg.herokuapp.com?font=Fira+Code&size=24&duration=3000&pause=1000&color=4ECDC4&center=true&vCenter=true&width=600&lines=Ratnesh+Kumar+Singh;Data+Scientist+%7C+AI%2FML+Engineer;4%2B+Years+Building+Production+AI+Systems\" alt=\"Typing SVG\" />\n\n","readmeExcerpt":"--- <div align=\"center\"> <img src=\"https://capsule-render.vercel.app/api?type=waving&color=gradient&height=240&text=Agentic%20AI%20Gen%20AI%20Frameworks&fontSize=42&fontColor=ffffff&animation=fadeIn\" /> </div> --- Frameworks together enable end-to-end Agentic AI systems — from LLM orchestration, memory, multi-agent collaboration, RAG, observability, automation, APIs, to cloud-scale deployment. --- 🧠 Agentic AI & GeN","codeSnippets":[],"executableExamples":[],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[],"languages":["python"],"docsSourceLabel":"GITHUB REPOS","editorialOverview":"A comprehensive collection of modern Agentic AI and Generative AI frameworks used to build autonomous, multi-agent, and production-ready AI systems. Covers LangChain, LangGraph, CrewAI, AutoGen, MCP, RAG pipelines, LLM orchestration, observability, APIs, and cloud deployment across AWS, GCP, and Azure. --- <div align=\"center\"> <img src=\"https://capsule-render.vercel.app/api?type=waving&color=gradient&height=240&text=Agentic%20AI%20Gen%20AI%20Frameworks&fontSize=42&fontColor=ffffff&animation=fadeIn\" /> </div> --- Frameworks together enable end-to-end Agentic AI systems — from LLM orchestration, memory, multi-agent collaboration, RAG, observability, automation, APIs, to cloud-scale deployment. --- 🧠 Agentic AI & GeN","editorialQuality":{"score":100,"threshold":65,"status":"ready","wordCount":445,"uniquenessScore":63,"reasons":[]}},"media":{"evidence":{"source":"no-media","verified":false,"confidence":"low","updatedAt":"2026-10-09T18:04:02.399Z","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":"2026-10-09T18:04:02.399Z","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":"2026-10-10T07:15:10.406Z","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_repos","protocols":[{"label":"OpenClaw","href":"/agent/protocol/openclew"}]}}}