{"id":"cb463d3d-e2f3-42d8-9a9c-c8a2f95edfa3","entityType":"agent","slug":"crewai-now-or-neverr-pipeline-bitcoin-ai-agent","name":"pipeline-bitcoin-ai-agent","canonicalUrl":"https://www.xpersona.co/agent/crewai-now-or-neverr-pipeline-bitcoin-ai-agent","canonicalPath":"/agent/crewai-now-or-neverr-pipeline-bitcoin-ai-agent","generatedAt":"2026-10-09T23:05:32.222Z","source":"GITHUB_REPOS","claimStatus":"UNCLAIMED","verificationTier":"NONE","summary":{"evidence":{"source":"editorial-content","verified":true,"confidence":"high","updatedAt":"2026-04-15T05:21:22.124Z","emptyReason":null},"description":"bitcoin ai agent pipeline based on langchain and crewAI Langchain and CrewAI Pipeline 🤼
This repository contains a full Q&A pipeline using LangChain framework, Qdrant as vector database and CrewAI as Agents. The data used is \"The Attention Mechanism\" research paper, but the RAG pipeline is structure to a","descriptionLabel":"Technical summary","evidenceSummary":"Capability contract not published. No trust telemetry is available yet. Last updated 4/15/2026.","installCommand":null,"sourceUrl":"https://github.com/Now-Or-Neverr/pipeline-bitcoin-ai-agent","homepage":null,"primaryLinks":[{"label":"View Source","url":"https://github.com/Now-Or-Neverr/pipeline-bitcoin-ai-agent","kind":"source"}],"safetyScore":66,"overallRank":18,"popularityScore":0,"trustScore":null,"claimedByName":null,"isOwner":false,"seoDescription":"bitcoin ai agent pipeline based on langchain and crewAI Langchain and CrewAI Pipeline 🤼
\n \n
\n\nThis repository contains a full Q&A pipeline using LangChain framework, Qdrant as vector database and CrewAI as Agents. The data used is \"The Attention Mechanism\" research paper, but the RAG pipeline is structure to analyze research papers and provide an analysis and summary. Therefore it can use any other research paper. Use this **[Link](https://nbviewer.org/github/benitomartin/crewai-rag-langchain-qdrant/blob/main/crewAI_RAG_Qdrant.ipynb)** if the notebook cannot be opened.\n\nThe main steps taken to build the RAG pipeline can be summarize as follows:\n\n* **Data Ingestion**: load data from pdf file\n\n* **Indexing**: RecursiveCharacterTextSplitter for indexing in chunks\n\n* **Vector Store**: Qdrant inserting metadata\n\n* **QA Chain Retrieval**: RetrievalQA\n\n* **CrewAI**: Researcher and Writer Agents to analyze and summarize the paper\n \nFeel free to ⭐ and clone this repo 😉\n\n## 👨💻 **Tech Stack**\n\n\n\n\n\n\n\n\n\n\n## 📐 Set Up\n\nIn the initial project phase, the documents are loaded using **PyPDFLoader** and indexed. Indexing is a fundamental process for storing and organizing data from diverse sources into a vector store, a structure essential for efficient storage and retrieval. This process involves the following steps:\n\n- Select a splitting method and its hyperparameters: we will use the **RecursiveCharacterTextSplitter**.\n\n- Select the embeddings model: in our case the **OpenAI**\n\n- Select a Vector Store: **Qdrant**.\n\nStoring text chunks along with their corresponding embedding representations, capturing the semantic meaning of the text. These embeddings facilitate easy retrieval of chunks based on their semantic similarity. \n\nAfter indexing, a QA Chain Retrieval Pipeline is set up in order to check the Q&A functioning and performance. \n\nThe final step includes CrewAI Agents to generate two documents:\n\n- An analysis of the paper provided by the research agent\n\n- A summary of the key findings provided by the writer\n\n\n## 🌊 QA Chain Retrieval Pipeline\n\nThe pipeline created contains the main llm model, the QA chain and the agents. A custom tool which uses the vector store as search engine has been implemented. The researcher looks for the paper and generates a detailed [analysis](https://github.com/benitomartin/crewai-rag-langchain-qdrant/blob/main/researcher_analysis.md) as md file. Finally the writer generates a short [summary](https://github.com/benitomartin/crewai-rag-langchain-qdrant/blob/main/writer_summary.md) with the key findings of the paper, trying to write it in a way that it is comprehesive for computer science students.\n\n```\n### RESEARCHER ###\n\n# Agent\nreasarcher = Agent(\n role=\"Computer Science Researcher\",\n goal='Understanding the published research paper and extract the main insights',\n backstory=\"\"\"You work at a Computer Science school.\n A new paper has been published and you need to extract\n the main information and provide the main insights to\n the students.You have a knack for dissecting complex\n research papers\"\"\",\n verbose=True,\n tools=[tool_instance],\n allow_delegation=False\n )\n\n\n# Tasks\ntask_research = Task(\n description=\"\"\"Conduct an in-depth analysis of the published research paper.\n Identify the key components and try to understand how each works.\"\"\",\n expected_output=\"\"\"Your final answer must be a full analysis report of the research paper\"\"\",\n agent=reasarcher,\n output_file='researcher_analysis.md' \n\n)\n```\n\n```\n### WRITER ###\n\n# Agent\nwriter = Agent(\n role=\"Computer Science Writer\",\n goal= 'Craft easy to understand content for Computer Science students',\n backstory=\"\"\"You are working on writing a summary of a research paper.\n Use the content of the Computer Science Researcher to develop\n a short comprehensive summary of the research paper\"\"\",\n verbose=True,\n allow_delegation=False,\n)\n\n# Tasks\ntask_writer = Task(\n description=\"\"\"Extract the most important topics and key components of the report\n developed by the Computer Science Researcher.\"\"\",\n expected_output=\"\"\"Your final answer must be a short comprehensive summary \n to understand report for Computer Science students\"\"\",\n agent=writer,\n output_file='writer_summary.md' \n\n)\n```\n\n## 👥 Contact\n- Telegram [@michalstefanow](https://t.me/mylord1_1)\n- Twitter [@michalstefanow](https://x.com/michalstefanow)\n","readmeExcerpt":"Langchain and CrewAI Pipeline 🤼
This repository contains a full Q&A pipeline using LangChain framework, Qdrant as vector database and CrewAI as Agents. The data used is \"The Attention Mechanism\" research paper, but the RAG pipeline is structure to a","codeSnippets":[],"executableExamples":[{"language":"text","snippet":"### RESEARCHER ###\n\n# Agent\nreasarcher = Agent(\n role=\"Computer Science Researcher\",\n goal='Understanding the published research paper and extract the main insights',\n backstory=\"\"\"You work at a Computer Science school.\n A new paper has been published and you need to extract\n the main information and provide the main insights to\n the students.You have a knack for dissecting complex\n research papers\"\"\",\n verbose=True,\n tools=[tool_instance],\n allow_delegation=False\n )\n\n\n# Tasks\ntask_research = Task(\n description=\"\"\"Conduct an in-depth analysis of the published research paper.\n Identify the key components and try to understand how each works.\"\"\",\n expected_output=\"\"\"Your final answer must be a full analysis report of the research paper\"\"\",\n agent=reasarcher,\n output_file='researcher_analysis.md' \n\n)"},{"language":"text","snippet":"### WRITER ###\n\n# Agent\nwriter = Agent(\n role=\"Computer Science Writer\",\n goal= 'Craft easy to understand content for Computer Science students',\n backstory=\"\"\"You are working on writing a summary of a research paper.\n Use the content of the Computer Science Researcher to develop\n a short comprehensive summary of the research paper\"\"\",\n verbose=True,\n allow_delegation=False,\n)\n\n# Tasks\ntask_writer = Task(\n description=\"\"\"Extract the most important topics and key components of the report\n developed by the Computer Science Researcher.\"\"\",\n expected_output=\"\"\"Your final answer must be a short comprehensive summary \n to understand report for Computer Science students\"\"\",\n agent=writer,\n output_file='writer_summary.md' \n\n)"}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[],"languages":["python"],"docsSourceLabel":"GITHUB REPOS","editorialOverview":"bitcoin ai agent pipeline based on langchain and crewAI Langchain and CrewAI Pipeline 🤼
This repository contains a full Q&A pipeline using LangChain framework, Qdrant as vector database and CrewAI as Agents. The data used is \"The Attention Mechanism\" research paper, but the RAG pipeline is structure to a","editorialQuality":{"score":100,"threshold":65,"status":"ready","wordCount":392,"uniquenessScore":65,"reasons":[]}},"media":{"evidence":{"source":"no-media","verified":false,"confidence":"low","updatedAt":"2026-04-15T05:21:22.124Z","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":"2026-04-15T05:21:22.124Z","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":"2026-10-09T23:05:32.222Z","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"}]}}}