Build a Local AI Orchestrator (Python + OpenAI + Claude Code)

@Microlent

Hi, I read your requirement carefully. What stood out is that you're building an AI engineering orchestrator, not another chatbot. The focus is on coordinating multiple AI tools through a structured Review → Fix → Test workflow while keeping the developer in control. We've built AI-powered applications, LLM integrations, workflow automation, and developer tools using Python, OpenAI APIs, Git, and CLI-based architectures, making this a strong fit. My proposed architecture: • Python 3.12+ with asyncio for orchestration. • OpenAI and Claude Code integrations behind a unified provider layer. • SQLite + Markdown for project memory and task history. • GitPython for diffs, branching, and user-approved commits. • Typer with Rich for a clean CLI experience. • Modular workflow engine for Plan → Implement → Test → Review → Fix cycles. Estimated timeline: 3 to 4 weeks for the MVP. One question: Will Claude Code be invoked through its official CLI, or do you already have a preferred integration method or API? I'd be happy to discuss the architecture and help build a scalable foundation for the next phases. Regards, Rajesh Rolen Microlent Systems

@kabirchy

Hello, I understand you're looking to build a local AI Engineering Orchestrator. This is an intriguing project, and I believe my expertise aligns well with your needs. With a focus on coordinating multiple AI tools, my approach involves leveraging Python to create a robust system that integrates OpenAI with Claude Code. This setup will facilitate the automation of software development tasks through AI agents. By utilizing LLM Prompt Engineering, we'll ensure that interactions with AI are optimized for performance and accuracy. A core component of the system will be a scalable architecture, designed to handle future expansions and modifications. For data management, I recommend using SQLite to maintain a lightweight yet effective database solution. My background in both AI and software development equips me to tackle the complexities of orchestrating these technologies effectively. By developing a cohesive and intuitive application, we can streamline your software development processes and enhance productivity. Looking forward to the opportunity to collaborate and contribute to your innovative project. Best Regards, Khorshed Alam, RS Software

@kamran2012

Hi there, I will build your local AI orchestrator with the review, fix, test loop coordinating OpenAI for planning and review while Claude Code handles implementation. The architecture will use asyncio with a Typer CLI, Pydantic models for task state, and SQLite for project memory alongside your Markdown files. On a similar multi-agent pipeline, enforcing structured output between the planning and execution agents prevented most misrouted tasks. I will apply that same pattern here. Questions: 1) For Claude Code integration, are you using the CLI subprocess approach or their API? 2) Do you have an existing test suite, or should the orchestrator generate tests too? I can map out the full architecture and start this week. Looking forward to discussing further. Best regards, Kamran

@ZohaibRoy

⭐⭐⭐⭐⭐ Build a Local AI Engineering Orchestrator with Python ❇️ Hi My Friend, I hope you're doing well. I reviewed your project requirements and see you are looking for an experienced Python developer for an AI Engineering Orchestrator. You have no need to look any further, as Zohaib is here to help you! My team has successfully completed 50+ similar projects for AI automation. I will create a reliable MVP that integrates various AI tools to streamline software development efficiently. ➡️ Why Me? I can easily build your AI Engineering Orchestrator as I have 5 years of experience in Python development, OpenAI API integration, and Git automation. My expertise includes working with AI agents, executing terminal commands, and developing CLI applications. Not only this, but I have a strong grip on relevant technologies like asyncio, SQLite, and Pydantic, ensuring a robust solution for your project. ➡️ Let's have a quick chat to discuss your project in detail and let me show you samples of my previous work. Looking forward to discussing this with you in chat. ➡️ Skills & Experience: ✅ Python 3.12+ ✅ OpenAI API ✅ AI Agents ✅ Git Automation ✅ CLI Applications ✅ Asyncio ✅ GitPython ✅ Automated Testing ✅ Markdown File Management ✅ Rich ✅ Pydantic ✅ SQLite Waiting for your response! Best Regards, Zohaib

@C0RETECHIES

Hi there, I understand you're building a local AI orchestrator. It functions as an autonomous agent, using GPT for planning and Claude for code implementation. The core is a 'review-fix-test' loop: it runs tests, uses GPT to analyze failures, and feeds actionable feedback back to Claude. This process repeats until success, then presents a Git diff for a final, human-approved commit, using Markdown files for persistent memory. Technical approach: A modular Python CLI app using Typer for the interface and asyncio to manage the agent workflow. We'll use Pydantic for data structures, subprocess to execute tests, and GitPython for version control. A central orchestrator class will manage state transitions. SQLite will store run history. Core modules: Planner Agent (GPT for strategy), Coder Agent (Claude for implementation/fixes), Test Executor (runs shell commands), Reviewer Agent (GPT for analyzing test failures), and Git Manager (handles diffs and commits post-approval). Relevant systems: We recently built an 8-agent AI pipeline. It orchestrates multiple specialized agents to achieve a complex goal, which is architecturally analogous to your plan-code-test-review agent swarm. Implementation strategy: We'll build the state machine for the plan-code-test-fix cycle first. Then, implement and unit-test each agent's integration (OpenAI, Claude, Git). The Typer CLI will be the final layer. Regards, Rohit

@ambientinfotech

Hi, We not only just write code—I build reliable solutions that solve real business problems. Whether it's automation, APIs, web applications, AI, or backend development, I can deliver exactly what you're looking for. Looking forward to working with you. Thanks, Deva

@corpmember29

Hello, I have carefully reviewed your requirements for building a local AI Engineering Orchestrator and understand the complete MVP workflow. I have 10+ years of experience in Python, AI Agents, OpenAI API, LLM integrations, Git automation, CLI applications, and workflow orchestration, I can develop a robust Windows-based solution using Python 3.12+, asyncio, GitPython, Pydantic, SQLite, Rich, and Typer/Click. The MVP will automate the complete Plan → Implement → Test → Review → Fix → Git Approval cycle with a modular architecture, making it easy to extend in future phases while ensuring clean, maintainable, and well-documented code. I WILL PROVIDE 2 YEARS FREE ONGOING SUPPORT AND COMPLETE SOURCE CODE. WE WILL WORK WITH AGILE METHODOLOGY AND WILL GIVE YOU ASSISTANCE FROM ARCHITECTURE DESIGN, DEVELOPMENT, TESTING, DEPLOYMENT, AND FUTURE ENHANCEMENTS. I am available on desk as per your convenient time zone and will work on your project until you satisfied with my work. Thanks Christina

@DeveloperStation

Hi, We’ve developed a similar product called Descripio, where we integrated multiple LLMs to automate Amazon product listing optimization. We also built a Chrome extension to extract product reviews and used GitHub Actions for CI/CD to run tests and deploy code. We can use GitHub’s REST API to create a GitHub app that allows developers to connect their repositories and run CI/CD pipelines. We can also implement a feature where developers can manually trigger a pipeline for specific branches. With 15 years of experience, I’ve worked extensively with Python and JavaScript, and I’ve led multiple startups as a co-founder. I’m equally comfortable with front-end and back-end development, and I’ve worked with modern frameworks like React, Vue, Nuxt, and Next. Let’s schedule a 10-minute introductory call to discuss your project in more detail and see if I’m the right fit. Feel free to message me anytime—I usually respond within 10 minutes. I’m eager to learn more about your exciting project. Best regards, Adil

@godesignpk

I can build your local AI Engineering Orchestrator MVP to automate software development workflows. My approach involves creating a Python application leveraging asyncio and Pydantic to integrate OpenAI and Claude Code. The system will manage a review-fix-test cycle, execute terminal commands via GitPython, run automated tests, and maintain project memory using Markdown files, ensuring human approval before Git commits. I have developed custom automation tools and AI-driven systems, similar to the task-execution and review workflows you've described. While I don't have a direct public link for this specific type of local orchestrator, my experience in building complex Python applications and integrating AI APIs ensures a robust solution. What is your preferred method for managing project memory and user approval prompts within the CLI?

@ASirbescu

I’ve built similar systems using Python, FastAPI, OpenAI, Claude, LangGraph/LangChain, Git automation, SQLite, and async workflows to orchestrate multiple AI agents for software engineering tasks rather than simple chatbots. I've developed several AI-powered automation systems that coordinate multiple LLMs and external tools instead of acting as traditional chatbots. For example, AI Software Engineering Assistant using Python, OpenAI, Claude, LangGraph, Git, and FastAPI that generated implementation plans, modified source code, executed tests, reviewed outputs, and iterated until completion. My proposed solution: Modular agent-based architecture (Planner, Implementer, Reviewer, Tester, Git Manager) Python 3.12 with asyncio for concurrent task execution OpenAI API + Claude Code integration with interchangeable providers GitPython for branch management, diffs, and approval-based commits Rich + Typer for an intuitive CLI experience SQLite + Markdown knowledge base for persistent project memory Automatic test execution with configurable retry limits Safe terminal command execution with logging and rollback support Designed from the start so future phases can easily add new AI providers, IDE integration, and additional engineering workflows. I can deliver a clean, well-documented MVP that's easy to extend into the larger platform you have planned. Thanks

@Softeria

With over a decade of experience as an AI Engineer and Software Developer, I'm ready to take on this project and bring your AI Engineering Orchestrator's MVP to life. My skillset is perfectly aligned with your project requirements - Python (including the latest version 3.12+), CLI/Desktop applications, Git automation, LLM prompt engineering, and of course, OpenAI API integration. Additionally, I have profound knowledge in utilising Python for Git operations and terminal commands execution, which will be pivotal for this application. One of the unique aspects that I bring to the table is my experience with Claude Code, which is a strong advantage for your project. I am well-versed in using Claude Code for implementing tasks, fixing issues and running automated tests - skills that directly map onto your MVP feature requirements. To give you an overview of my approach to this project - I will ensure that the workflow synergizes smoothly. Starting from allowing the user to key in a task till the final successful commit through required iterations by GPT reviewing results, Claude fixing any detected issues and conducting Git diff only after user approval. I'll combine all these steps using a streamlined intuitive CLI with support for Markdown files as demanded.

@InterconnectBPO

I specialize in developing cutting-edge AI solutions to streamline software development processes. With our expertise, we can help you achieve your goal of creating a local AI Engineering Orchestrator that effectively coordinates various AI tools. Our approach focuses on leveraging Python, OpenAI API, and Claude Code to automate tasks seamlessly, ensuring efficiency and accuracy in project management. I understand the importance of integrating Git automation, CLI/Desktop applications, and AI agents, as outlined in your project requirements. Our 20+ 5-star reviews on similar projects attest to our proficiency in delivering high-quality results consistently. The worst that can happen is you walk away with a free consultation! Regards, JP

@ShaikhAneesa

Hi there, I understand you want to build the MVP of a local AI Engineering Orchestrator that coordinates multiple AI tools to automate the software development lifecycle. The application will run entirely on Windows, orchestrating OpenAI and Claude Code to plan, implement, test, review, fix, and commit code through a controlled workflow with human approval before every Git commit. My approach would be to develop the application in Python 3.12+ using an asynchronous architecture with asyncio for orchestration, Pydantic for structured data models, SQLite for persistent project memory, GitPython for repository management, Rich for an interactive CLI, and Typer for a clean command-line interface. The workflow will coordinate GPT for planning and code review, Claude Code for implementation and fixes, automated test execution, Markdown-based project memory, Git diff generation, and an approval gate before commits, ensuring a reliable and extensible foundation for future phases. Could you share whether Claude Code will be accessed through its official CLI or API, and what testing framework (pytest, unittest, etc.) your projects primarily use? I'm ready to start immediately. Warm Regards, Aneesa.

@notwillingcurren

Hi, this is close to work I already do daily - I build and run agent-orchestration tooling (LLM agents wired into git, automated test loops, markdown-based project memory) for my own dev workflow, plus a FastAPI/OpenAI automation product in production. For your MVP I'd build: - Python 3.12 CLI (Typer/Click), asyncio, Pydantic models, GitPython for commits - OpenAI API for planning, Claude Code invoked as the implementer step - Terminal/test execution wrapped with a clear pass/fail + fix-retry loop - SQLite for run state/history, markdown files for durable project memory - Human-approval gate before any git commit (explicit confirm, not auto-commit) I've built this exact shape of plan -> implement -> test -> fix loop before, so I already know the failure modes (flaky tests, partial diffs, commit-approval races). Windows-local execution is fine - I'll test in a Windows sandbox to match your environment. Happy to hop on a quick call to align on the MVP cut before I start. Can begin immediately.

@anshumanvermaUK

Hi, I have experience with Python, OpenAI API, Claude, AI agents, FastAPI, asyncio, Git automation, SQLite, Pydantic, and workflow orchestration. My recent work includes AI document-processing platforms, local LLM integrations, automation pipelines, and CLI-based AI tools. I would implement the MVP using Python 3.12, asyncio, Typer, GitPython, Pydantic, SQLite, and a modular agent architecture for maintainability and future expansion. Thanks, Anshuman

@durgesh8527

As an experienced Python developer specialized in AI and Automation systems, I believe I have the skillset required to build a Local AI Orchestrator that fulfills all the envisioned features and functionalities you have described. My proficiency with Python, OpenAI API, Git automation, CLI/Desktop applications and especially Claude Code integration aligns seamlessly with your project's requirements. My extensive portfolio includes various previous projects similar to yours where I've successfully integrated multiple APIs and executed complex development workflows. In terms of tech stack, I propose utilizing Python 3.12+ along with Rich, Pydantic, SQLite, asyncio and either Typer or Click. This comprehensive selection ensures robustness, security and performance. As an added advantage, I have a sound understanding of LLM integrations, Python async programming practices and GitPython which will be invaluable for executing the desired review-fix-test workflow you're seeking.

@krupnovdenis

Hello! We can build an external Python-based solution for this task. 1. Which part of the workflow should we implement first? 2. Do you already have any local commands or repo structure to connect? — About us We are dZENcode – a full-cycle IT company for digital product development: from design and programming to integrations and post-release support. We build projects from scratch and also work on existing solutions that need further development, improvements, or technical support. You can find detailed information about our services and rates on our official website: https://dzencode.com. Please review it – after that, we can discuss the details and agree on the next step. ⚠️ After clarifying all details, we will define the scope, the suitable cooperation format – task-based, outsourcing, or outstaffing – and the final cost. Projects are guaranteed to reach release with us: • 10+ years providing IT services; • 90+ in-house specialists; • 250+ public reviews since 2015; • We support products under SLA after launch; • We work under NDA and a company contract!

@rbtech1984

Hello There!!! ★★★★ (Project GOAL: Build a local Python-based AI engineering orchestrator that coordinates OpenAI, Claude Code, Git, testing, and automated development workflows.) ★★★★ I've carefully read your requirements and understand you're looking for an MVP that runs locally on Windows, orchestrates multiple AI tools, automates the review→fix→test cycle, and keeps the developer in control with Git approval before every commit. The architecture needs to be modular, scalable, and ready for future phases. ⚜ Python 3.12+ application development ⚜ OpenAI & Claude Code integration ⚜ AI agent orchestration ⚜ Git automation & GitPython ⚜ Automated testing workflow ⚜ SQLite & project memory management ⚜ CLI development with Typer/Click I have experience building AI-powered automation tools, Python applications, LLM integrations, and workflow orchestration using asyncio, Pydantic, Git APIs, and SQLite. My approach is to develop a modular architecture where planning, implementation, testing, review, and Git operations are independent services, making future expansion simple. I'll deliver clean, documented code with a structured handover, and I'm excited about contributing to the long-term vision of this project. I can share relevent work and architecture ideas during our discussion. I'd love to discuss Phase 1 in detail and help build a solid foundation for your AI engineering platform. Warm Regards, Farhin B.

@quan9824

As a highly skilled and reliable Python developer, I have successfully executed several projects involving AI, OpenAI API, LLM integrations, Git automation, and CLI/Desktop applications. Such experiences empower my ability to tackle the challenge of building your Local AI Engineering Orchestrator effectively. Moreover, my expertise in Python 3.12+, GitPython, SQLite, Typer/Click makes me an excellent fit for this project as I can flawlessly execute the given tasks. What really distinguishes me is my proficiency in working with Claude Code, Python’s utility module designed for coding AI systems. This puts me ahead in understanding and leveraging the technical requirements of this project to build a software that performs flawless git commit only after reviewing, fixing, testing and gaining user approval making it an EXCELLENT software management tool. I can assure you of delivering top-quality output on-time and within budget estimates - an assurance based on my track record of 100% job completion rate with all positive reviews. This project will embark us on a larger journey which I am not only excited but also adequately prepared for!