AI Multi-Agent Engine Development

@adeelpirzada

Hi, I am Adeel. I work on Boostifai, an AI SaaS built on Python and React that runs automated content generation and analytics pipelines with real guardrails around cost and output quality, so building a capped, self-verifying multi-agent engine is familiar ground. For this I would use LangGraph for the orchestration since you need explicit state and loop control, with hard iteration counts and a token budget check before each pass rather than after. Research and outreach agents would run as separate modules behind one FastAPI layer so you can swap either later without touching the other. I can start this week and would treat the first agent, the research and verification loop, as the core to get right before layering outreach on top. Which channel matters most for outreach first, email or WhatsApp?

@ZohaibRoy

⭐⭐⭐⭐⭐ Build a Modular Multi-Agent Engine with FastAPI for Your Needs ❇️ 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 engine. Look no further; Zohaib is here to help you! My team has successfully completed 50+ similar projects in AI and automation. I will create a modular engine that runs parallel research, verifies findings, and generates reports, all within your budget. ➡️ Why Me? I can easily build your multi-agent engine as I have 5 years of experience in Python development, specializing in AI systems, REST APIs, and data management. My expertise includes working with LangGraph, CrewAI, and FastAPI. Additionally, I have a strong grip on web scraping, data validation, and report generation. ➡️ Let's have a quick chat to discuss your project in detail. I'll show you samples of my previous work and how I can meet your needs efficiently. Looking forward to our conversation! ➡️ Skills & Experience: ✅ Python Development ✅ FastAPI ✅ LangGraph ✅ CrewAI ✅ Web Scraping ✅ Data Validation ✅ API Integration ✅ Markdown Reports ✅ Task Automation ✅ Parallel Processing ✅ Outreach Automation ✅ Modular Design Waiting for your response! Best Regards, Zohaib

@p4logics

Hello Sir, I understand you need a plug-and-play AI multi-agent engine that automatically performs parallel research, self-verifies the gathered data, and manages outreach messaging. To solve this, I will build a modular Python system using LangGraph to effectively control the agent feedback loops and SerpAPI for web scraping, securely exposing everything through a clean FastAPI REST interface so you can easily connect it to your existing backend. Along with generating cited markdown reports, I will implement strict loop iteration limits and token budget caps to completely prevent runaway API costs, delivering a safe, well-documented, and efficient solution.

@ykarora26

With experience in AI engineering and building modular engines, I understand your need to create a multi-agent engine using Python with LangGraph or CrewAI for stateful orchestration. My question for you is: How critical is real-time data processing for your project's success? Regards, Yogesh Kumar

@ShaikhAneesa

Hi there, I’ve carefully reviewed the architecture and understand that you need a reusable AI orchestration layer, not a one-off chatbot. The key is controlling parallel research, verification loops, citations, outreach, and API costs within a predictable stateful workflow. First, I’ll design the agent state and workflow in Python with LangGraph, separating research, scraping, verification, synthesis, and outreach as modular nodes. Then I’ll implement parallel Serper/SerpAPI research, structured evidence storage, contradiction/gap detection, and capped verification passes. Next, I’ll add cited Markdown generation, personalized Email/WhatsApp/SMS drafting, and scheduled follow-ups with strict contact limits. Finally, I’ll expose everything through FastAPI with Swagger/OpenAPI, .env configuration, logging, validation, and backend-ready webhooks. For cost control, I’ll enforce per-job token budgets, per-node limits, maximum graph iterations, retry caps, and hard timeouts, with execution stopping cleanly when any threshold is reached. Prompt-injection filtering will be applied before external content enters agent context. I can share relevant AI automation architecture examples and structure the module for straightforward integration with your existing backend. Cheers, Aneesa

@kabirchy

Hi there, I've reviewed your need for a modular AI multi-agent engine with FastAPI. My proposed solution is a standalone Python-based engine with a clean REST API layer for easy integration into your backend and future apps. The architecture will separate the core agent logic - handling parallel research runs, self-verification workflows, and report synthesis - from the API interface, ensuring modularity and scalability. I'll implement the key multi-agent functions you described, including capped follow-ups for outreach, all designed for straightforward deployment on platforms like Google App Engine. My experience with FastAPI, Python software architecture, and designing AI workflows aligns perfectly with this project's technical requirements. I can build this to your specifications within the stated timeline. Best Regards, Khorshed Alam, RS Software

@devendrathakur12

Hi, I’m Dev Singh, Principal AI Engineer with 20+ years in AI and Python backend development. I have gone through your specific requirement for modular multi-agent engine. I would use LangGraph rather than CrewAI, because explicit state and loop control fit your self verification and capped research passes better. I will build the Python orchestration layer with LangGraph and FastAPI endpoints around independent research agents. I will add token budgets and loop counters at the execution state level, so every follow-up pass stops before spend can run away. And I will add prompt injection filtering plus cited markdown output, at least that is where I would start. Relevant AI architecture details and code examples I can send privately. I have built feedback based AI workflows; can your existing backend consume async job IDs? Which search API and LLM provider are you already using? What maximum token spend should one research job be allowed? Free for a quick call this week? Or answer those three and I will map the module without a meeting. Dev Singh

@denisSolution

Hi, I'm Denis, a full-stack developer specializing in AI-driven systems with multi-agent workflows, API integrations, and cost-controlled automation. I understand your needs: a self-contained research engine that breaks down complex requests into parallel tasks, gathers and verifies information autonomously, and synthesizes cited reports. You also need outreach capabilities with strict cost controls—integrated into your existing backend. I've built similar systems where multiple AI agents coordinate work and handle feedback loops efficiently. The key is balancing autonomy with guardrails through token spend caps, iteration limits, and structured state management. My approach: 1. Requirements clarification and architecture planning 2. Multi-agent orchestration using LangGraph for state control 3. Parallel research with search API integration and web scraping 4. Self-verification loop with contradiction detection 5. Outreach module with follow-up scheduling 6. FastAPI endpoints with guardrails (token budgets, prompt injection sanitization) 7. Swagger documentation The critical piece is the token budget system—real-time spend tracking at the agent level with hard caps per request and graceful degradation. Loop iteration limits enforced at the orchestration layer. I'd address potential risks (search API rate limits, data quality variability) with intelligent retry logic, result caching, and fallback strategies. I'm ready to start immediately. Let's discuss the details. Th

@ImranAli01

Hi there, I went through your project description and understand you need a standalone modular multi-agent engine that will run parallel research, verify findings, synthesize cited reports, and support controlled outreach through a clean FastAPI interface. I’ll build the orchestration layer in Python using LangGraph or CrewAI, with stateful agents, parallel research tasks, Serper/SerpAPI integration, web extraction, structured evidence storage, and a bounded verification loop. Verification will check missing information, contradictions, source support, and confidence before allowing capped follow-up research. Reports will be generated as structured Markdown with citations, while outreach agents will handle personalised Email, WhatsApp, or SMS drafts and controlled follow-ups. Hard loop limits, per-job token budgets, retry ceilings, timeouts, and model-level usage controls will prevent runaway execution and API costs. Prompt-injection sanitisation, input validation, structured logging, and failure isolation will be included. FastAPI endpoints, webhooks, Swagger/OpenAPI documentation, .env configuration, and a modular codebase will complete the handover. A detailed project blueprint will be shared within 24–48 hours after the project is awarded, outlining the architecture, agent flow, safeguards, and implementation plan. Kind Regards, Imran Ali

@mayankinnovative

Hello I can develop your AI multi agent engine with specialized agents orchestration workflows tool calling memory context management API integrations and reliable communication between agents With 20 years of software development experience I can work with Python FastAPI OpenAI APIs LLM integrations PostgreSQL Redis REST APIs Docker cloud services and scalable backend architectures A few quick questions How many specialized agents are required and what responsibilities should each agent handle Which LLM providers and external tools or APIs need to be integrated Do you need persistent agent memory task queues monitoring and detailed execution logs I’ll focus on building a modular scalable multi agent architecture with reliable orchestration structured agent communication secure API integration error handling and observability so new agents and workflows can be added easily as the platform grows I’m available to start immediately Thanks

@mobimubasir

Hello there, I can build a standalone Python multi-agent engine with LangGraph/CrewAI, parallel research, self-verification loops, cited report generation, capped outreach follow-ups, and clean FastAPI endpoints with Swagger/OpenAPI documentation. I’ll enforce hard iteration/token budgets, sanitize prompt injections, persist structured state, and design the codebase as modular services with configurable .env settings so it can plug cleanly into your existing backend and future applications. I have strong experience with AI automation, LLM workflows, FastAPI, OpenAI, and structured agent pipelines; I can deliver the standalone module with testing and documentation within an agreed fixed scope and timeline.

@DeyaaAhmed

Hi, I can build your standalone multi-agent engine using Python, LangGraph, and FastAPI to orchestrate parallel research, self-verification, and capped outreach workflows. I will implement stateful loops that break user requests into sub-tasks, query Serper/SerpAPI, scrape web data, and run automated verification passes to eliminate contradictions before synthesizing cited Markdown reports. The system will include robust guardrails with hard token budgets and iteration limits to prevent runaway API spend, alongside clean REST endpoints for triggering jobs and syncing results with your existing backend. You will receive a modular, well-documented codebase with Swagger/OpenAPI specs and .env configuration for seamless integration. My extensive experience building production-grade LangGraph systems ensures reliable state management and efficient cost control without compromising research depth or outreach personalization. I also offer FREE post-delivery support to monitor initial agent stability, troubleshoot any API rate limits or verification logic issues, and assist with minor prompt adjustments during the first month. Let's discuss the project in more details.

@sabaehsan

Greetings, I see you're looking to develop a modular multi-agent engine with a REST API that can enhance your backend capabilities. My approach would involve building the engine using Python, leveraging LangGraph or CrewAI for effective orchestration and control. I'll ensure that we break down user tasks into manageable sub-tasks, efficiently querying search APIs and scraping web data. To tackle the self-verification loop, I’ll implement checks for data consistency and perform follow-up research when needed. The engine will also generate structured markdown reports and handle personalized outreach with capped follow-ups. With a focus on exposing clean FastAPI endpoints and implementing necessary guardrails, I aim to deliver a robust and maintainable codebase. Best regards, Saba Ehsan

@vishal1145

Your multi-agent engine will fail in production if you don't implement circuit breakers on the self-verification loop. Without hard caps on recursive research passes, a single ambiguous query can trigger cascading API calls that burn through your budget in minutes. Quick questions - are you planning to run this on serverless infrastructure where cold starts will kill your parallel research latency? And what's your acceptable token budget per research job before the system should abort and return partial results? Here is the architectural approach: - LANGRAPH + FASTAPI: Build stateful agent orchestration with Redis-backed checkpointing so interrupted jobs resume without re-querying APIs, plus FastAPI endpoints with async workers to handle concurrent research requests without blocking. - SELF-VERIFICATION LOOP: Implement a DAG-based validation layer that scores data completeness using embedding similarity checks, triggers max 2 follow-up passes with exponential backoff, and flags unverifiable claims in the final report. - COST GUARDRAILS: Add pre-flight token estimation using tiktoken, enforce per-job budget caps via middleware that kills requests exceeding limits, and log all API spend to a separate audit table for real-time cost tracking. I've built similar agentic systems for 2 enterprise clients that process 10K+ research jobs monthly without cost overruns. Let's schedule a 20-minute technical call to review your loop termination logic and API rate limit strategy.

@dataspro

Nice to meet you , It is a pleasure to communicate with you. My name is Anthony Muñoz, I am the lead engineer for DSPro IT agency and I would like to offer you my professional services. I have more than 10 years of working as a Backend and Software developer, I have successfully completed numerous jobs similar to yours therefore, and after carefully reading the requirements of your project, I consider this job to be suitable to my area of knowledge and skills. I would love to work together to make this project a reality. I greatly appreciate the time provided and I remain pending for any questions or comments. Feel free to contact me. Greetings

@muneeb6767

I can build a modular Python multi-agent engine with LangGraph/CrewAI, parallel web research, verification loops, cited Markdown reports, FastAPI endpoints and capped outreach follow-ups; I’ll enforce hard iteration/token budgets, sanitize untrusted content, and keep the codebase clean and configurable via .env. I can discuss the exact fixed price and timeline after reviewing your backend/API requirements.

@Tanmoy236

Hi There! I specialize in Python AI automation and modular API development, with 9+ years of experience, 465+ completed projects, Top 1% verified freelancer. I can build your standalone LangGraph/CrewAI + FastAPI engine with parallel research, self-verification, cited synthesis, outreach workflows, and clean backend integration. For cost control, I’d enforce per-job token budgets, hard iteration/follow-up caps, timeout handling and validation before each research pass, with prompt-injection sanitization. I’ll deliver modular code, .env configuration and Swagger/OpenAPI docs. Fixed scope and timeline can be finalized after reviewing your backend interface and preferred framework.

@Jennycreation

Hello Sir, I have already worked on many **cryptocurrency trading systems, automated trading platforms, and exchange API integration projects**. I can also show you demos of my previous work. I am a full-time freelancer with **17+ years of experience** in software development, trading automation, and financial API integrations. My relevant experience includes: * Cryptocurrency trading platforms * Binance and Bybit API integration * MT4 and MT5 trading automation * C#-based copy trading software * Arbitrage trading systems (Binance ↔ Bybit) * Automated order execution (Buy/Sell/Square Off) * Portfolio and position management * Margin and risk management features * Real-time market data integration * Trading bots and strategy automation I have already developed a **C#-based Copy Trading Software** where trades from a master account are automatically copied to multiple child accounts. I have also built **arbitrage trading systems** between Binance and Bybit using their official APIs. In addition, I have developed trading platforms using **Zerodha Kite API, Alice Blue API, Angel One API, and Profitmart API**, including features such as order placement, position management, square-off, margin calculation, and automated trading execution. I have also implemented strategy automation using **AmiBroker CSV integration**. I can start working immediately and deliver a secure, scalable, and reliable trading solution based on your requirements. Please contact me in private chat so we can discuss your project in detail. I will be happy to show you **live demos and examples of my previous trading projects**. Looking forward to working with you. Thanks & Regards, Sanjay

@carlosd178

Hello, After reviewing your requirements, I understand you need a standalone Python multi-agent engine for parallel research, verification loops, cited synthesis, and controlled outreach, exposed through a clean FastAPI interface. My relevant skills include Python, FastAPI, LangGraph/CrewAI-style orchestration, LLM/API integration, web research pipelines, AI agents, and workflow automation. I’d use LangGraph for explicit state and deterministic loop control, with separate research, verification, synthesis, and outreach modules. Parallel workers would gather evidence while the verifier identifies contradictions or unsupported claims and triggers only capped research passes. For cost control, I’d enforce per-job token/API budgets, maximum iterations, tool-call limits, timeouts, model-specific usage tracking, and hard termination states. Prompt-injection filtering and source validation would sit between scraped content and agent context. I can provide the module with .env configuration, REST/webhook endpoints, Swagger/OpenAPI docs, tests, and modular source code. I’d estimate 2–3 weeks; fixed price can be finalized after confirming the search/outreach providers. One question: should scheduled follow-ups run inside this service via a job queue, or will your existing backend own scheduling? Best regards, Carlos.