You will get Self-Evolving Agent Framework (EvoAgentX): Auto-Optimize Your AI Workflows
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Project details
Stop settling for static AI performance. Deploy agents that learn, adapt, and improve automatically.
EvoAgentX is a cutting edge framework that turns a single natural language goal into a self optimizing multi agent pipeline. Instead of manual prompt tuning and trial and error, it runs advanced evolutionary loops like TextGrad and AFlow to continuously rewrite and refine its own workflows, delivering measurable performance gains.
I will set up, configure, and benchmark EvoAgentX for your specific task or research use case.
Why EvoAgentX
Autonomous workflow generation: You provide a high level goal and the system automatically builds the full multi agent graph.
Self evolution engine: Each run is evaluated against defined metrics such as accuracy or latency, then improved automatically by evolving prompts and agent connections.
Quantifiable results: Built in benchmarks like HotPotQA, MBPP, and MATH plus support for custom metrics to clearly show performance improvements.
Full reproducibility: All configs, seeds, and prompt versions are saved so every result is auditable and repeatable.
EvoAgentX is a cutting edge framework that turns a single natural language goal into a self optimizing multi agent pipeline. Instead of manual prompt tuning and trial and error, it runs advanced evolutionary loops like TextGrad and AFlow to continuously rewrite and refine its own workflows, delivering measurable performance gains.
I will set up, configure, and benchmark EvoAgentX for your specific task or research use case.
Why EvoAgentX
Autonomous workflow generation: You provide a high level goal and the system automatically builds the full multi agent graph.
Self evolution engine: Each run is evaluated against defined metrics such as accuracy or latency, then improved automatically by evolving prompts and agent connections.
Quantifiable results: Built in benchmarks like HotPotQA, MBPP, and MATH plus support for custom metrics to clearly show performance improvements.
Full reproducibility: All configs, seeds, and prompt versions are saved so every result is auditable and repeatable.
AI Algorithms
Autoencoder, Feedforward Neural Network, Gated Recurrent Unit, Large Language Model, Long Short-Term Memory Network, Multilayer Perceptron, Multimodal Large Language Model, Recurrent Neural Network, Regression Analysis, Transformer ModelAI Applications
AI Chatbot, AI Content Creation, AI-Enhanced Classification, AI-Generated Code, AIOps, Anomaly Detection, Natural Language Generation, Natural Language Understanding, Sentiment Analysis, Sequence Modeling, Time Series AnalysisAI Development Language
PythonAI Tools
Azure OpenAI, GitHub Copilot, Gradio, Hugging Face, NVIDIA AI Platform, PyTorch, Replit, Streamlit, TensorFlow, Word2vecAI Models
AlphaCode, BERT, BLOOM, ChatGPT, GPT-3, GPT-4, GPT-J, GPT-Neo, Jurassic-2, LLaMA, OpenAI Codex, WhisperWhat's included
| Service Tiers |
Starter
$1,500
|
Standard
$4,200
|
Advanced
$7,800
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 21 days |
Number of Revisions | 1 | 3 | 5 |
AI Model Integration | |||
Batch Normalization | - | - | - |
Database Integration | - | ||
Detailed Code Comments | |||
Image Upscaling | - | - | - |
MLOps | - | - | |
Model Deployment | |||
Model Documentation | |||
Model Monitoring | - | - | |
Model Testing & Optimization | |||
Model Tuning | |||
Natural Language Processing | |||
NLP Tokenization | |||
Pre-Training | - | - | - |
Prompt Engineering | |||
Setup File | |||
Source Code |
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About Yasir
AI Full Stack Engineer | Agents, Automation & Integrations
100%
Job Success
Karachi, Pakistan - 8:32 am local time
This gives you one technical owner for the AI workflow, frontend, backend, database, APIs, third party integrations, and deployment.
WHAT I CAN BUILD
AI Agents and LLM Applications
I develop AI agents and assistants that can access private data, search documents, call APIs, use business tools, generate structured outputs and complete multi step tasks.
Common use cases include:
• Internal knowledge assistants and RAG applications
• Customer support and lead qualification agents
• Document, email, and product data extraction
• Resume matching and recommendation systems
• Healthcare assistants and data analysis applications
• Voice AI for calls, appointment booking, and follow ups
• AI features added to existing SaaS platforms
Automation and Integrations
I build automation systems that connect the applications a business already uses.
This includes:
• n8n, Make, and Zapier workflows
• CRM, lead routing and sales automation
• Email classification and priority workflows
• Accounting and operational integrations
• Webhook and API based data synchronization
• WhatsApp, calendar, notification and communication workflows
• Custom Python or Node.js logic when no code tools are not enough
I treat automation as software, with proper validation, logging, error handling, retry logic, and clear data flow between systems.
Full Stack AI and SaaS Development
I can build the complete application around the AI or automation layer, including:
• React, Next.js, and TypeScript frontends
• Python, FastAPI, Node.js, NestJS, and Laravel backends
• PostgreSQL, MongoDB, Redis, and Supabase
• Authentication, roles, dashboards and admin panels
• Payments, subscriptions, APIs and third party services
• Docker, AWS, Vercel and CI/CD deployment
SELECTED UPWORK DELIVERY
My paid Upwork work includes:
• Building an AI powered product data extraction tool that converted unstructured product information into structured business data
• Developing a QuickBooks Online and Workiz integration to automate financial and operational workflows
• Creating inventory synchronization between a Laravel POS system, a Next.js application and the Discogs API
• Delivering SaaS applications, backend APIs, business integrations, ERP features and workflow automation systems
My wider portfolio includes AI voice agents, document and email parsers, RAG knowledge assistants, healthcare applications, resume matching agents, WhatsApp automation, lead enrichment workflows and n8n and Make automation systems.
TECHNOLOGY
AI and LLMs: OpenAI, Claude, Gemini, LangChain, LangGraph, LlamaIndex, embeddings, vector databases, function calling, Whisper, ElevenLabs, Vapi, and Retell AI.
Development: Python, FastAPI, Node.js, NestJS, Laravel, React, Next.js, TypeScript, PostgreSQL, MongoDB, Redis, and Supabase.
Automation and Infrastructure: n8n, Make, Zapier, REST APIs, GraphQL, webhooks, Docker, AWS, Vercel, and GitLab CI/CD.
HOW I APPROACH PROJECTS
Before writing code, I map the users, inputs, decisions, outputs, data sources, integrations, and possible failure points.
I then choose the architecture based on what the system actually needs. Sometimes that means an AI agent or RAG pipeline. In other cases, a rules-based workflow, conventional API integration, or backend service is more reliable.
The goal is not to add AI everywhere. The goal is to build a useful system that performs consistently and can be maintained after launch.
You can expect:
• Realistic estimates and clear milestones
• Early communication about risks and limitations
• Clean, maintainable application architecture
• Proper testing, logging, and error handling
• Clear documentation and handover
• Direct communication throughout development
Send me the workflow you want to improve, the systems involved, sample data, and the result you need. We can define the most practical architecture and first development phase for your project.
Steps for completing your project
After purchasing the project, send requirements so Yasir can start the project.
Delivery time starts when Yasir receives requirements from you.
Yasir works on your project following the steps below.
Revisions may occur after the delivery date.
Project Kickoff & Goal Definition
Client purchases the project and shares the natural language goal, metrics, and API access.
Benchmark or Task Scoping
I map the goal to the appropriate benchmark or custom dataset based on the selected tier.