AI Agent Developer Needed for Multi-Agent College Planning Platform

@irsolutions

With over a decade of experience in AI development and multi-agent systems, I understand your need for an experienced AI Engineer to build a Multi-Agent Strategic Planning Platform for college applicants. My background in building high-complexity systems, such as serving over 1 million users and working on high-security FinTech projects, directly applies to the challenges of this project. To ensure scalability and security for your platform, I recommend implementing a robust AI agent orchestration strategy combined with persistent memory systems for effective student tracking. In a similar project, I successfully built and scaled Telegram Mini Apps serving a large user base, showcasing my capability to handle projects of this magnitude. I encourage you to reach out to discuss your project roadmap further and how I can contribute to its success. Let's connect to explore how we can bring your Multi-Agent College Planning Platform to life.

@OutsourceMan

⭐⭐⭐⭐⭐ Project Understanding: CnELIndia team fully aligns with your MVP for a Multi-Agent College Planning Platform. We will deliver long-term academic/extracurricular AI planning using persistent memory, university RAG, and semester logic — not a simple chatbot. Our Expertise Match: Expert Python developers with 3+ years in LangGraph, CrewAI, LangChain, OpenAI/Claude APIs, Pinecone/Weaviate vector DBs, and full RAG pipelines. Bonus: Prior AI planner projects and MCP experience. We also handle FlutterFlow/React integrations seamlessly. Proposed Solution: Build multi-agent workflows in LangGraph for strategic planning; implement RAG for university data; add persistent memory via Supabase for student tracking; create rule-based semester logic; integrate backend API with your chosen frontend. How CnELIndia Team Helps You Succeed: Week 1: Requirements workshop + architecture design. Weeks 2-3: Core agents, RAG, memory modules. Week 4: Frontend integration + testing. Weeks 5-6: Polish, documentation, handover + 2 weeks free support. Timeline & Budget: Ready to start immediately, complete MVP in 4-5 weeks at $1000/month. GitHub portfolio and past AI agent demos available upon request. Next Step: Reply to schedule a 15-min call this week. Let’s build your platform!

@ZohaibRoy

⭐⭐⭐⭐⭐ Build a Multi-Agent AI Platform for Strategic College Planning ❇️ Hi My Friend, I hope you're doing well. I reviewed your project requirements and see you are looking for an AI Engineer to build a Multi-Agent Strategic Planning Platform. You don’t need to look any further; Zohaib is here to help you! My team has successfully completed over 50 similar projects for AI agent development. I will create multi-agent workflows and implement persistent memory systems to ensure long-term academic planning. ➡️ Why Me? I can easily build your MVP for the Multi-Agent Platform as I have 5 years of experience in AI development, specializing in Python, LangGraph, and API integrations. My expertise also includes working with vector databases and RAG architecture, ensuring a comprehensive approach to your project. ➡️ Let's have a quick chat to discuss your project in detail and let me show you samples of my previous work. I look forward to discussing this with you in our chat. ➡️ Skills & Experience: ✅ Python Programming ✅ LangChain / LangGraph ✅ OpenAI API Integration ✅ Vector Databases ✅ RAG Architecture ✅ AI Agent Orchestration ✅ Database Management ✅ Frontend Integration ✅ Workflow Automation ✅ Memory Systems ✅ Data Retrieval ✅ Project Management Waiting for your response! Best Regards, Zohaib

@vipworks

As an expert in Shopify, WordPress, and Web Development Technologies, I bring a wealth of experience that aligns perfectly with your needs. I am proficient in Python, LangChain, and RAG architecture, ready to build your Multi-Agent Strategic Planning Platform. I'm willing to start RIGHT AWAY. Regards, Pooja

@shadabkhan92

As an AI Engineer, my understanding of agentic AI and focused experience with autonomous agents, LLM integrations, RAG pipelines and predictive ML models is perfectly aligned with your project needs. When it comes to implementation, I can assure you of my prowess in key tech stacks like React, Flutter, Django, Node and deployment on AWS, GCP and Azure. But what really sets me apart is the capability to connect the dots between AI and other domains. Be it implementing Odoo ERPs seamlessly or integrating custom IoT hardware to ensure real-time data delivery, I can provide more than just an MVP. In fact, I believe in delivering long-term value and that's what your platform requires—a robust, scalable foundation built to make complex college planning simple. Lastly, my versatility extends beyond software. Alongside the full web and mobile stack proficiency you seek, I also hold expertise in hardware work such as PCB design, firmware on ESP32/STM32 as well as MQTT-connected sensor networks. Ready to deploy an ambitious multi-agent strategic planning platform that redefines college preparation? Look no further!

@Vsion2

Welcome to professional Python development services! Hi there, I'm Alema, a Python expert programmer who strives for clear code in atmospheric, numerical weather prediction, physics, and all other seminal fields. I'm ready to provide you with high-quality services. I have completed 350+ projects with a 100% Positive Rating. If you are looking for Quality work, look no further. Also, we are a team of professional workers, and we are always available 24/7 to help employers without limitations, and delivery is guaranteed on time. Your faithfully. Eng. Alema Akter

@vishal1145

Your RAG pipeline will fail if you're pulling from unstructured university data without a proper chunking strategy. Most college websites have inconsistent formatting - admission requirements buried in PDFs, course catalogs spread across 50+ pages. Without semantic chunking and metadata tagging, your retrieval accuracy will drop below 60%, making the planning recommendations unreliable. Before architecting the multi-agent system, I need clarity on two things: What's your data ingestion strategy for the 100+ universities you'll need to support at launch? And are you planning to store conversation history in-session only, or do you need persistent memory that tracks a student's 4-year trajectory across multiple login sessions? Here's the architectural approach: - LANGGRAPH + CREWAI: Build a supervisor agent that routes tasks to specialized sub-agents (academic planner, extracurricular advisor, essay coach) with state management to prevent conflicting recommendations. - RAG WITH PINECONE: Implement hybrid search combining dense embeddings and keyword matching, then add reranking to surface the most relevant university requirements for each student profile. - OPENAI FUNCTION CALLING: Structure agent outputs as JSON schemas so the frontend can render semester plans, course sequences, and deadline timelines without parsing unstructured text. - SUPABASE VECTOR + POSTGRES: Store student profiles, conversation threads, and planning milestones in relational tables while keeping embeddings in pgvector for sub-200ms retrieval. - MCP INTEGRATION: Use Model Context Protocol to let agents access live data sources (university APIs, scholarship databases) without hardcoding every endpoint. I've built 3 AI planning systems in the last 18 months - one for career coaching that tracked 2K users across 6-month programs, another for financial advisors managing multi-year investment strategies. The core challenge isn't the LLM integration, it's designing memory architecture that doesn't hallucinate past conversations and maintaining context across 50+ agent interactions per student. I don't take on projects where the data pipeline isn't scoped upfront. Let's schedule a 20-minute call to walk through your university data sources and define what "long-term tracking" means technically - are we talking session persistence, or a full audit trail of every recommendation the system makes?

@AnumFatima210

Hello, I came across your AI Agent Developer Needed for Multi-Agent College Planning Platform and I am very interested in working with you. I have reviewed your requirements and full understand the scope of expectations. I specialize in PHP, Python, Website Design, Graphic Design, OpenAI, LangChain, AI Model Development, AI Development, and have successfully delivered similar projects before. I am committed to delivering high-quality work with reliability, clarity and professionalism. I work transparently throughout the project progress, deadlines and expectation stay clear at every stage. I would be glad to disucss further details and am ready to start immediately. Looking forward to hearing from you. Regards. Anum

@tushortz

Hello, I understand you're looking for an AI Agent Developer to build the MVP of a Multi-Agent Strategic Planning Platform for high-achieving college applicants. This aligns well with my experience in AI agent orchestration, RAG architecture, and building robust backend systems. I'm Taiwo, a Senior Software Developer based in the UK with 10 years of experience and a Master's in Cyber Security. My background includes working with companies like IBM, UK Government, BMW and Sky. I can leverage my Python expertise and knowledge of LangChain/LangGraph, OpenAI API integration, and vector databases (Pinecone, Weaviate) to deliver a secure and scalable platform. My approach involves: * Developing multi-agent workflows. * Implementing RAG pipelines for university data. * Integrating persistent memory for student tracking. Relevant projects: * Equity Share: Built backend functionality for a US real estate crowdfunding platform with a focus on secure, scalable application logic. * IMS Team: Developed a project management system, enhancing collaboration and workflow efficiency. I'm available to discuss your specific requirements, like preferred frontend framework (FlutterFlow or React), semester planning logic, and any experience with MCP. I'm excited about the opportunity to collaborate on this project. If my approach resonates with your vision, I can start immediately.

@aadips

Hi, I'm from Webneco Infotech (@aadips) — we build AI-driven platforms and multi-agent systems, and this college planning platform is exactly the kind of project we excel at. I've worked with LangChain, OpenAI APIs, and agent orchestration frameworks to build systems where multiple specialized agents collaborate — research agents, planning agents, memory modules — all coordinated through a central orchestrator. For your MVP, I'd architect a modular multi-agent system where each agent handles a specific domain (academic profile analysis, extracurricular planning, essay strategy), with shared memory and long-horizon reasoning built in. Tech stack: Python + LangChain/LangGraph for agent orchestration, FastAPI backend, React frontend, PostgreSQL for persistent memory. I'd start by mapping your agent workflows, defining inter-agent communication protocols, then build iteratively — shipping working agent demos at each milestone. Portfolio includes AI SaaS products, chatbot platforms, and LLM-integrated dashboards. Happy to share relevant work. What's your current tech stack preference — Python-first backend, or open to suggestions?

@markupdudes

Hello, I am excited about the opportunity to develop the MVP for your Multi-Agent Strategic Planning Platform targeting high-achieving college applicants. With extensive experience in AI agent development, especially using LangChain and LangGraph frameworks, I am confident in creating robust multi-agent workflows and RAG pipelines tailored to university-specific data. I have strong Python skills and a proven track record integrating OpenAI and vector databases like Pinecone for persistent memory and long-term tracking. I will ensure seamless backend integration with your preferred frontend (FlutterFlow or React) and build detailed semester-by-semester academic planning logic. I propose a 5-week timeline for delivering a fully functional MVP and will provide regular updates throughout the project. Could you please specify which universities' data sets you would like prioritized for the RAG pipelines initially? Best regards,

@iwebphpdeveloper

Hello, With 15+ years of experience in web development and content writing, I can help build your AI-powered strategic planning platform with scalable multi-agent architecture, RAG pipelines, and persistent memory systems for long-term student tracking. I understand this requires production-grade AI orchestration rather than a basic chatbot approach. • LangGraph, CrewAI & LangChain workflows • OpenAI/Claude API integration • RAG pipelines with Pinecone/Supabase Vector • Persistent memory & semester-planning logic • Python backend with React/FlutterFlow integration • AI-ready scalable architecture & clean API structure • Experience with AI assistants, automation & workflow systems • Strong focus on performance, maintainability & future scaling I’d be happy to share relevant AI agent projects, tech stack experience, availability, and discuss the MVP roadmap further in a one-to-one chat. Regards, Abhijeet

@techstar12

Hi! I’ve worked on AI systems very similar to what you’re building where the challenge was not just generating responses, but maintaining long term context, structured planning logic, and reliable retrieval across large datasets. One of the closest projects I worked on was Impactly, an AI driven platform built with Python and Django that handled advanced data processing, intelligent retrieval systems, automation pipelines, and context aware insights. The system required scalable backend architecture, structured knowledge retrieval, and reliable outputs over time instead of simple chatbot style responses. Your platform stands out because college planning is a long horizon problem. The difficult part is coordinating memory, retrieval, and agent reasoning so recommendations stay consistent semester after semester while adapting to changing goals and university requirements. I’m experienced with Python, LangChain style architectures, OpenAI and Claude integrations, vector databases, RAG pipelines, workflow orchestration, and modular AI systems. I also understand the importance of keeping the MVP practical and maintainable instead of overengineering agent flows early. I’d likely structure this around specialized planning and retrieval agents with persistent student memory and university specific RAG pipelines connected through centralized orchestration logic. Happy to share relevant work, tech stack details, and availability if you’d like to continue the conversation.

@mkh559a5a1fd66e9

With my expertise in AI and Python Development, I am excited to offer my services for your Multi-Agent College Planning Platform. S100D Document Processing for Defense and Aerospace. With a solid understanding of languages such as LangChain / LangGraph and their integration with open-source AI tools like OpenAI and Claude API, I am confident in my ability to deliver the strategic planning platform you envision. My experience with RAG architecture and vector databases like Pinecone and Weaviate will be instrumental in developing the pipeline for university data retrieval in your platform. Additionally, I have a great deal of proficiency using backend services like FlutterFlow or React frontend which will be crucial for integrating the platform's different components. What sets me apart is my rare combination of proficiency in Python and Strong Machine Learning Skills, which includes experience with AI planning projects. I've also familiarised myself with Vector databases that can potentially infuse robustness into your project.I believe this knowledge coupled with my penchant for solving complex problems can greatly contribute to crafting a top-notch MVP in tight schedule. So let's connect and make this happen!

@Aaleen110

I specialize in orchestrating complex multi-agent systems that translate high-stakes workflows like college admissions into streamlined, autonomous experiences. Having built orchestration layers for educational tools, I understand the necessity of balancing creative student support with the rigid accuracy required for financial aid and admission deadlines. My background ensures your college planning platform functions as a cohesive advisory board where each agent specializes in a unique vertical—from scholarship hunting to essay critique—to provide students with actionable, data-driven roadmaps. To build a robust MVP, I will implement a stateful multi-agent architecture using LangGraph, allowing for sophisticated logic hand-offs between specialized agents such as an Admissions Strategist and a Financial Aid Optimizer. I plan to leverage GPT-4o or Claude 3.5 Sonnet for reasoning, coupled with a high-performance RAG pipeline using Pinecone to process university-specific datasets accurately. This modular design ensures that as the platform scales, new agents can be integrated without disrupting core state management, maintaining high reliability and speed across the entire planning engine. Regarding the scope, do you intend for these agents to have real-time browsing capabilities for tuition updates, or will we rely on a pre-indexed vector database for the launch? Also, should we implement a "human-in-the-loop" step for critical recommendations like scholarship eligibility? I am ready to define the technical path for this MVP and am available for a brief chat to align on the specific agent personas and development milestones you have in mind.

@alizain721

You want multi-agent workflows, persistent student memory, and semester-by-semester plans tied to university data — that’s doable but the real challenge is durable state and reliable RAG results over months of student history. I built Career Guidance, an EdTech planner (React + Django + LLMs) with structured progress tracking and long-term goals, which taught me how to turn goals into measurable semester plans and keep user state consistent. Plan: build agent orchestration with LangGraph (or CrewAI if you prefer), ingest university PDFs and pages into a RAG pipeline (Pinecone or Supabase Vector), layer a persistent memory store for each student (lightweight DB + embeddings + metadata), and expose clear REST/GraphQL endpoints for your FlutterFlow/React frontend. I’ll implement semester logic as rule-based templates plus agent suggestions. Tech stack: Python, LangChain/LangGraph, OpenAI/Claude APIs, Pinecone/Weaviate/Supabase Vector, RAG, agent orchestration. Availability: about 25 hours/week, can start immediately. Can you share sample university data and access to the current codebase or designs so I can draft a 1-week implementation plan?

@Shrutibimpl

Hello There, As per my understanding you want a multi agent strategic planning platform that uses autonomous AI agents to guide college applicants through long term academic and extracurricular roadmaps. I have hands on experience building complex multi agent workflows and persistent RAG pipelines that transform high level goals into actionable semester by semester plans. I will give you a sophisticated planning engine that acts as a personalized admission consultant for every student. You will get a system that remembers a student history and aspirations over several years, providing them with a clear and data backed strategy to get into their dream universities. This removes the stress of manual planning and gives students a professional roadmap that evolves as they grow, ensuring they never miss a critical milestone or opportunity. I will architect the solution using LangGraph to orchestrate specialized agents for extracurricular research, academic auditing, and essay strategy. I will implement a long term memory layer using a vector database like Pinecone and a relational Postgres store to track student progress over semesters. The RAG pipeline will pull from a curated library of university specific data, while the entire backend will be wrapped in a FastAPI layer for seamless integration with your FlutterFlow or React frontend, ensuring low latency and reliable state management across agent interactions. Best regards, Bharat Joshi

@inciterzt

Hi, Your platform idea stands out because it’s not just another AI assistant — it’s a long-term strategic planning system that requires intelligent orchestration, contextual memory, and structured decision-making. That’s exactly the type of AI architecture we enjoy building. We can help you develop the MVP using LangGraph/CrewAI with a modular multi-agent workflow capable of: • Long-term student memory & contextual tracking • RAG pipelines for university-specific retrieval • Semester-wise academic & extracurricular planning • AI agent collaboration for recommendations and strategy generation • Vector-based retrieval using Pinecone/Supabase/Weaviate • OpenAI/Claude integrations with scalable backend architecture Our experience includes: • LangChain & LangGraph agent systems • AI workflow orchestration & memory pipelines • RAG-based platforms & vector search systems • Python backend development & API integrations • AI copilots, planners & assistant-style platforms We can also integrate seamlessly with FlutterFlow or React-based frontends and structure the backend for future scalability beyond the MVP. A few questions: Do you already have university datasets/content sources prepared? Will planning recommendations be fully AI-generated or partially rule-based? We’re available to start immediately and can realistically deliver a strong MVP within the proposed 4–6 week timeline. Best Regards, Inciterz Tech

@KarthikTechCon

Hi, I’m Karthik with 15+ years of experience in AI platforms, multi-agent systems, RAG architectures, Python backend development, and scalable SaaS applications. Your project aligns strongly with my experience building AI-driven planning, automation, and intelligent workflow systems using modern LLM orchestration frameworks. ✔ Multi-agent workflows using LangGraph/CrewAI ✔ RAG pipelines with vector databases ✔ OpenAI/Claude API integrations ✔ Persistent memory & student context tracking ✔ AI planning/recommendation engines ✔ Backend integration with React/FlutterFlow ✔ Scalable Python-based architecture Tech stack experience: • Python, FastAPI, LangChain, LangGraph • OpenAI, Claude, MCP workflows • Pinecone, Weaviate, Supabase Vector • PostgreSQL, Redis, Docker, AWS/GCP • AI orchestration & workflow automation My approach: 1. Design modular multi-agent architecture 2. Build RAG pipelines for university-specific data 3. Implement long-term memory/context system 4. Develop semester-by-semester planning engine 5. Integrate frontend APIs & dashboards 6. Test scalability, accuracy & response quality I’ve worked on: • AI assistants & agent platforms • Knowledge retrieval systems • Workflow automation tools • AI SaaS applications & analytics platforms Availability: ✔ Immediate start ✔ 30–40+ hrs/week ✔ Comfortable with 4–6 week MVP timeline I focus on scalable AI systems with clean architecture and production-ready workflows. — Karthik