@x1pay/langchain
LangChain/LangGraph tools for AI agent x402 payments on X1
Crawler Summary
Model Context Protocol (MCP) Bootcamp offers a deep dive into MCP architecture and its role in the Agentic AI ecosystem. Learn to build real-world, production-ready AI workflows using MCP with LangChain, LangGraph, and CrewAI through fully practical, project-based implementations. <div align="center"> </div> <img width="1025" height="240" alt="image" src="https://github.com/user-attachments/assets/5ecbd43e-74e4-488c-9970-02b4f00f6794" /> --- <div align="center"> <img src="https://capsule-render.vercel.app/api?type=waving&color=gradient&height=260&text=AGENTIC%20AND%20GEN%20AI%20AGENTS%20WITH%20MCP&fontSize=40&fontColor=ffffff&animation=fadeIn" width="100%"/> </div> --- 10 Python AI/ML librarie Capability contract not published. No trust telemetry is available yet. 3 GitHub stars reported by the source. Last updated 5/31/2026.
Freshness
Last checked 5/31/2026
Best For
Build-Agentic-AI-and-Gen-AI-Agents-with-MCP is best for crewai, multi-agent workflows where OpenClaw compatibility matters.
Not Ideal For
Contract metadata is missing or unavailable for deterministic execution.
Evidence Sources Checked
editorial-content, GITHUB OPENCLEW, runtime-metrics, public facts pack
Model Context Protocol (MCP) Bootcamp offers a deep dive into MCP architecture and its role in the Agentic AI ecosystem. Learn to build real-world, production-ready AI workflows using MCP with LangChain, LangGraph, and CrewAI through fully practical, project-based implementations. <div align="center"> </div> <img width="1025" height="240" alt="image" src="https://github.com/user-attachments/assets/5ecbd43e-74e4-488c-9970-02b4f00f6794" /> --- <div align="center"> <img src="https://capsule-render.vercel.app/api?type=waving&color=gradient&height=260&text=AGENTIC%20AND%20GEN%20AI%20AGENTS%20WITH%20MCP&fontSize=40&fontColor=ffffff&animation=fadeIn" width="100%"/> </div> --- 10 Python AI/ML librarie
Public facts
4
Change events
0
Artifacts
0
Freshness
May 31, 2026
Capability contract not published. No trust telemetry is available yet. 3 GitHub stars reported by the source. Last updated 5/31/2026.
Trust score
Unknown
Compatibility
OpenClaw
Freshness
May 31, 2026
Vendor
Ratnesh 181998
Artifacts
0
Benchmarks
0
Last release
Unpublished
Key links, install path, and a quick operational read before the deeper crawl record.
Summary
Capability contract not published. No trust telemetry is available yet. 3 GitHub stars reported by the source. Last updated 5/31/2026.
Setup snapshot
git clone https://github.com/Ratnesh-181998/Build-Agentic-AI-and-Gen-AI-Agents-with-MCP.gitSetup 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.
Everything public we have scraped or crawled about this agent, grouped by evidence type with provenance.
Vendor
Ratnesh 181998
Protocol compatibility
OpenClaw
Adoption signal
3 GitHub stars
Handshake status
UNKNOWN
Merged public release, docs, artifact, benchmark, pricing, and trust refresh events.
Extracted files, examples, snippets, parameters, dependencies, permissions, and artifact metadata.
Extracted files
0
Examples
0
Snippets
0
Languages
python
Full documentation captured from public sources, including the complete README when available.
Docs source
GITHUB OPENCLEW
Editorial quality
ready
Model Context Protocol (MCP) Bootcamp offers a deep dive into MCP architecture and its role in the Agentic AI ecosystem. Learn to build real-world, production-ready AI workflows using MCP with LangChain, LangGraph, and CrewAI through fully practical, project-based implementations. <div align="center"> </div> <img width="1025" height="240" alt="image" src="https://github.com/user-attachments/assets/5ecbd43e-74e4-488c-9970-02b4f00f6794" /> --- <div align="center"> <img src="https://capsule-render.vercel.app/api?type=waving&color=gradient&height=260&text=AGENTIC%20AND%20GEN%20AI%20AGENTS%20WITH%20MCP&fontSize=40&fontColor=ffffff&animation=fadeIn" width="100%"/> </div> --- 10 Python AI/ML librarie
๐ข NumPy ๐ ๐Official Website ๐ Documentation
๐ผ Pandas ๐ ๐Official Website ๐ Documentation
๐ Scikit-Learn ๐
๐Official Website ๐ Documentation
๐ XGBoost ๐
๐Official Website ๐ Documentation
โก LightGBM ๐
๐Official Website ๐ Documentation
๐ง TensorFlow ๐
๐Official Website ๐ Documentation
๐ฏ Keras ๐
๐Official Website ๐ Documentation
๐ฅ PyTorch ๐
๐Official Website ๐ Documentation
๐ค Transformers (Hugging Face) ๐
๐Official Website ๐ Documentation
๐งฉ spaCy ๐
๐Official Website ๐ Documentation
Complete Agentic AI Course In 10 Hours- Langchain, Langgraph, RAG,Vectorless RAG, Guardrails,Evals : https://www.youtube.com/watch?v=rV3HJ4LEZ7k
โ and honestly, it completely reshaped how I think about the future of Agentic AI.
For years, AI systems have been powerful individually, but fragmented when it comes to collaboration, context-sharing, scalability, and orchestration.
MCP changes that.
This book explains how MCP is becoming the foundational communication layer for next-generation AI ecosystems โ enabling AI agents, tools, servers, and workflows to operate with shared context, adaptive intelligence, modularity, and secure multi-agent coordination.
Licensed under the MIT License - Feel free to fork and build upon this innovation! ๐
<img src="https://streak-stats.demolab.com?user=Ratnesh-181998&theme=radical&hide_border=true&background=0D1117&stroke=4ECDC4&ring=F38181&fire=FF6B6B&currStreakLabel=4ECDC4" alt="GitHub Streak Stats" width="48%"/>
<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%" />Machine endpoints, protocol fit, contract coverage, invocation examples, and guardrails for agent-to-agent use.
Contract coverage
Status
missing
Auth
None
Streaming
No
Data region
Unspecified
Protocol support
Requires: none
Forbidden: none
Guardrails
Operational confidence: low
curl -s "https://www.xpersona.co/api/v1/agents/crewai-ratnesh-181998-build-agentic-ai-and-gen-ai-agents-with-m/snapshot"
curl -s "https://www.xpersona.co/api/v1/agents/crewai-ratnesh-181998-build-agentic-ai-and-gen-ai-agents-with-m/contract"
curl -s "https://www.xpersona.co/api/v1/agents/crewai-ratnesh-181998-build-agentic-ai-and-gen-ai-agents-with-m/trust"
Trust and runtime signals, benchmark suites, failure patterns, and practical risk constraints.
Trust signals
Handshake
UNKNOWN
Confidence
unknown
Attempts 30d
unknown
Fallback rate
unknown
Runtime metrics
Observed P50
unknown
Observed P95
unknown
Rate limit
unknown
Estimated cost
unknown
Do not use if
Every public screenshot, visual asset, demo link, and owner-provided destination tied to this agent.
Neighboring agents from the same protocol and source ecosystem for comparison and shortlist building.
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Contract JSON
{
"contractStatus": "missing",
"authModes": [],
"requires": [],
"forbidden": [],
"supportsMcp": false,
"supportsA2a": false,
"supportsStreaming": false,
"inputSchemaRef": null,
"outputSchemaRef": null,
"dataRegion": null,
"contractUpdatedAt": null,
"sourceUpdatedAt": null,
"freshnessSeconds": null
}Invocation Guide
{
"preferredApi": {
"snapshotUrl": "https://www.xpersona.co/api/v1/agents/crewai-ratnesh-181998-build-agentic-ai-and-gen-ai-agents-with-m/snapshot",
"contractUrl": "https://www.xpersona.co/api/v1/agents/crewai-ratnesh-181998-build-agentic-ai-and-gen-ai-agents-with-m/contract",
"trustUrl": "https://www.xpersona.co/api/v1/agents/crewai-ratnesh-181998-build-agentic-ai-and-gen-ai-agents-with-m/trust"
},
"curlExamples": [
"curl -s \"https://www.xpersona.co/api/v1/agents/crewai-ratnesh-181998-build-agentic-ai-and-gen-ai-agents-with-m/snapshot\"",
"curl -s \"https://www.xpersona.co/api/v1/agents/crewai-ratnesh-181998-build-agentic-ai-and-gen-ai-agents-with-m/contract\"",
"curl -s \"https://www.xpersona.co/api/v1/agents/crewai-ratnesh-181998-build-agentic-ai-and-gen-ai-agents-with-m/trust\""
],
"jsonRequestTemplate": {
"query": "summarize this repo",
"constraints": {
"maxLatencyMs": 2000,
"protocolPreference": [
"OPENCLEW"
]
}
},
"jsonResponseTemplate": {
"ok": true,
"result": {
"summary": "...",
"confidence": 0.9
},
"meta": {
"source": "GITHUB_OPENCLEW",
"generatedAt": "2026-10-09T00:12:47.600Z"
}
},
"retryPolicy": {
"maxAttempts": 3,
"backoffMs": [
500,
1500,
3500
],
"retryableConditions": [
"HTTP_429",
"HTTP_503",
"NETWORK_TIMEOUT"
]
}
}Trust JSON
{
"status": "unavailable",
"handshakeStatus": "UNKNOWN",
"verificationFreshnessHours": null,
"reputationScore": null,
"p95LatencyMs": null,
"successRate30d": null,
"fallbackRate": null,
"attempts30d": null,
"trustUpdatedAt": null,
"trustConfidence": "unknown",
"sourceUpdatedAt": null,
"freshnessSeconds": null
}Capability Matrix
{
"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"
}Facts JSON
[
{
"factKey": "vendor",
"label": "Vendor",
"value": "Ratnesh 181998",
"category": "vendor",
"href": "https://github.com/Ratnesh-181998/Build-Agentic-AI-and-Gen-AI-Agents-with-MCP",
"sourceUrl": "https://github.com/Ratnesh-181998/Build-Agentic-AI-and-Gen-AI-Agents-with-MCP",
"sourceType": "profile",
"confidence": "medium",
"observedAt": "2026-05-23T06:54:00.528Z",
"isPublic": true,
"metadata": {}
},
{
"factKey": "protocols",
"label": "Protocol compatibility",
"value": "OpenClaw",
"category": "compatibility",
"href": "https://www.xpersona.co/api/v1/agents/crewai-ratnesh-181998-build-agentic-ai-and-gen-ai-agents-with-m/contract",
"sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-ratnesh-181998-build-agentic-ai-and-gen-ai-agents-with-m/contract",
"sourceType": "contract",
"confidence": "medium",
"observedAt": "2026-05-23T06:54:00.528Z",
"isPublic": true,
"metadata": {}
},
{
"factKey": "traction",
"label": "Adoption signal",
"value": "3 GitHub stars",
"category": "adoption",
"href": "https://github.com/Ratnesh-181998/Build-Agentic-AI-and-Gen-AI-Agents-with-MCP",
"sourceUrl": "https://github.com/Ratnesh-181998/Build-Agentic-AI-and-Gen-AI-Agents-with-MCP",
"sourceType": "profile",
"confidence": "medium",
"observedAt": "2026-05-23T06:54:00.528Z",
"isPublic": true,
"metadata": {}
},
{
"factKey": "handshake_status",
"label": "Handshake status",
"value": "UNKNOWN",
"category": "security",
"href": "https://www.xpersona.co/api/v1/agents/crewai-ratnesh-181998-build-agentic-ai-and-gen-ai-agents-with-m/trust",
"sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-ratnesh-181998-build-agentic-ai-and-gen-ai-agents-with-m/trust",
"sourceType": "trust",
"confidence": "medium",
"observedAt": null,
"isPublic": true,
"metadata": {}
}
]Change Events JSON
[]
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