Multi-Agent Langraph RAG Chatbot

@PINESCRIPTERR

Hello, I can help you with this project. I’ve worked on similar solutions before and can share live examples directly in chat. Scope & Delivery: • Implementation of the requested features as discussed • Clean, well-structured code • Final tested delivery Timeline: Estimated delivery within X days, with regular progress updates. Payment: Payment can be handled via milestones or escrow to ensure safety for both sides. Bug Fixing & Support: I include a free bug-fix period of X days after delivery. Any bugs related to the delivered work will be fixed at no extra cost. I’m available to discuss all details — including scope, pricing, and timeline — and make sure everything is clear before we start. Best regards, Houssame

@NEHABHAT92

As an experienced developer in both web and mobile systems, I understand the importance of a seamless, efficient user experience. With over 9 years under our belt, my team and I have tackled projects similar to yours before with resounding success. We've built complex, intelligent systems utilizing important technologies like AJAX, HTML and JavaScript that have dramatically improved UX for our clients. But it's not just about solving a one-time problem; we focus on building long-term versatile solutions that are maintainable in the long run. This means we not only provide the skills you require for this particular project but also ensure it is flexible enough to accommodate any future needs such as handling images or PDFs. Moreover, efficiency and accuracy are crucial for a project of this nature. I am proud to assure you that my teams have routinely delivered projects conforming to rigorous test standards with a minimum score requirement, like your 90% answer accuracy target. Our commitment to excellence is matched by our dedication to communication and collaboration so that we can find the best solution for your unique needs. With my proven skills and technical acumen, I'm confident we can bring this innovative vision to reality - let's chat!

@chiragardeshna

Hi there, I help teams ship production-grade multi-agent RAG systems that route questions to the right depth of response. I have 7+ years of experience building RAG pipelines and agent orchestrations, including LangGraph-style routing, vector stores, ingestion, chunking, embeddings, and citation-backed responses. I regularly work in Python with LangChain, OpenAI-compatible endpoints, and web-embedded chat widgets, and I design abstractions that are ready for PDFs and images. I reduce risk by separating retrieval from agent logic, enforcing testable routing rules, and providing simple CLI-based data refreshes with no code changes. I am available to start immediately. Regards Chirag

@Pulkit8369

I am writing to express my strong interest in building your multi-agent RAG architecture. As an AWS Certified Machine Learning Engineer with specialized experience in Generative AI and Python , I am well-equipped to implement your vision using LangGraph. My recent work includes engineering a YouTube Video Summarizer pipeline where I implemented sliding window chunking and a context-aware Q&A module using Gemini, which directly aligns with your need for specialized retrieval agents . Additionally, I developed an AI-powered presentation generator using Gemma 2, demonstrating my ability to create efficient, user-facing AI tools that reduce manual workflows . I understand the importance of clean abstractions for future multi-modal capabilities and can leverage my DevOps background (Docker, Git) to ensure the solution is modular and production-ready . I am confident in my ability to build the router logic, ingestion scripts, and web widget to meet your 90% accuracy criteria and am available to start immediately.

@Inshad10

I can build the multi-agent RAG experience you described using Langraph as the orchestration layer. The system will route user queries between a fast FAQ agent and a detailed, citation-aware agent, backed by a clean RAG pipeline with embeddings, chunking, and retrieval from your supplied data. I’ll deliver: Langraph-based multi-agent backend (Python) Embeddable web widget (single JS include) Vector store + ingestion pipeline Simple CLI/script to update source data without code changes Clear README and commented code The architecture will be modular and future-proofed for additional modalities (PDFs, images). I’m comfortable with LangChain and OpenAI stacks and can iterate quickly to meet your accuracy and routing requirements.

@harshu384

I am an AI Engineer with 1.5 years of hands-on experience building production-grade RAG systems and multi-agent architectures, handling everything from design and development to cloud deployment on AWS. I can implement your LangGraph-based multi-agent RAG system with a central retrieval pipeline, intelligent routing, FAQ and deep-research agents, and a clean, embeddable web widget. I will handle data ingestion, chunking, embeddings, vector stores, and retrieval, with support for structured and unstructured sources and a future-ready design for PDFs and multimodal inputs. I have experience deploying solutions using both server-based and serverless (Lambda + API Gateway) approaches, and I can integrate Langfuse and LangSmith for full LLM observability, tracing, latency monitoring, and prompt/version tracking. The deliverable will include a CLI-based data update workflow, clean README, and well-documented code to ensure repeatable deployments. I move fast, iterate collaboratively, and focus on building systems that are reliable, observable, and production-ready. Happy to discuss milestones and timelines.

@ahmedtahash

Hello, I can build a clean, production-ready multi-agent RAG system using LangGraph, with a router agent, specialized FAQ and deep-dive agents, and a modular ingestion/retrieval pipeline. I’ll focus on strong abstractions so text works now and PDFs/images slot in later, plus an embeddable web widget, CLI-based data refresh, and well-documented Python code. I’ve worked with LangChain, vector stores, and citation-grounded retrieval and can iterate quickly to hit your accuracy targets. For retrieval quality, do you already have a preferred vector store and embedding model, or should I benchmark a few against your FAQ test set? Regards, Ahmad Al-Ashery.

@MohamedZabady

Experienced AI and Machine Learning Engineer specializing in NLP and conversational AI systems. Proven expertise in building production-ready chatbots, intelligent recommendation engines, and complex NLP pipelines using state-of- the-art frameworks. Skilled in developing scalable AI applications with advanced orchestration tools and deploying robust solutions through modern API architectures and cloud platforms. Passionate about leveraging cutting-edge AI technologies to solve real-world problems and enhance user experiences through intelligent automation.

@jaesupi

Hello, I’m a Fullstack and Generative AI Engineer and I’m starting out on Freelancer, but this project matches very closely what I’ve been working on recently. I have hands-on experience building RAG-based systems, AI agents, and web-integrated chatbots using Python, LangChain, JavaScript, and LLM APIs. For your use case, I can help set up a central RAG pipeline with ingestion, chunking, embeddings, and retrieval, and design multiple agents for FAQs, longer answers with citations, and routing between them. I’m also comfortable embedding the system into a simple web widget and keeping the architecture clean so it can be extended to PDFs or images later. I focus on clear documentation, simple update scripts for data, and building something reliable and easy to iterate on. I’d be happy to discuss scope, milestones, and timing. Best regards, Supipi

@Hetal01

I am a software developer with 10 years of experience working with Python and a year of experience working with Agentic AI. I am well versed with prompt engineering, context Engineering and RAG pipelines. I have worked with Langchain and langgraph and have experience with multi agent systems

@CodeNexus

Hey! I have worked on the similar project, and have deep understanding of developing RAG chatbot. If you want, I can share the repository of that as well, and we can have a chat to discuss further, I would love to do so.

@sameer016ahmed

I’ve built agent-orchestrated RAG systems using LangGraph, LangChain, and OpenAI, including router agents, citation-aware responders, and FAQ-optimized bots. I can design a clean multi-agent architecture where a routing agent selects between fast FAQ responses and deeper, source-grounded answers backed by a shared vector store. I’ll handle ingestion, chunking, embeddings, and retrieval, with a CLI-based data refresh workflow requiring no code changes. The web widget will be lightweight, embeddable via JS, and future-proofed for PDFs and images. You’ll get clear docs, tests, and a repeatable local deployment setup.

@Cassiusred

I have built exactly this kind of system. Multi-agent RAG with Langraph/LangChain, router logic to dispatch queries to specialized agents, and embeddable web widgets. Your architecture makes sense: router agent for intent classification, FAQ agent for quick answers, detail agent for longer citation-backed responses. The key is clean abstractions for the retrieval layer so you can swap vector stores or add modalities later without rewriting agent logic. My approach: 1. Ingestion pipeline: Chunking strategy tuned for your FAQ vs. detailed content types. I would likely use different chunk sizes for different retrieval needs. 2. Vector store: Pinecone, Weaviate, or Chroma depending on your hosting preferences. Clean abstraction layer regardless. 3. Agent orchestration: Langraph for the multi-agent routing with clear state management. Each agent gets its own retriever configuration. 4. Widget: Lightweight JS embed that posts to your backend. Clean REST or WebSocket interface. 5. Update workflow: CLI script for re-ingesting updated docs without touching the agent code. I have questions about your current data volume and whether you need streaming responses in the widget. Happy to discuss scope and timeline.

@miteshs45

Hi, I’m comfortable working with RAG-based systems and agent-style AI workflows using tools like LangChain and LangGraph. This is the kind of problem I enjoy working on, especially when it involves clean retrieval logic and thoughtful response handling. I focus on writing clear, maintainable code and keeping things simple and extensible rather than over-engineered. I’m happy to start with a well-scoped MVP and iterate based on feedback. If it sounds like a fit, I’d be glad to discuss expectations, scope, and next steps. — Mitesh