Autonomous Python AI Agent Build

I’m putting together a production-ready AI agent that can think for itself and carry out practical, real-world tasks. At its core the agent will rely on both OpenAI and open-source LLMs, giving me the freedom to choose the right model for a given workload.

Scope of work
The agent must accept a plain-language instruction from the chat UI, break it into a basic sequence of sub-tasks, and then execute each step without additional prompts. Typical actions include: opening pages through Playwright or Selenium, reading or writing local files, running terminal commands, and calling external REST APIs. Throughout the run it should store and retrieve context via a vector database (Chroma, Pinecone, or FAISS) so conversations remain coherent and previously gathered knowledge is reusable.

Architecture guidelines
• Language: Python
• Framework: FastAPI or Flask (whichever you’re faster with)
• Orchestration: LangChain or LangGraph for tool routing and memory
• Containerisation: Docker, with an easy “docker compose up” to launch every service
• Persistence & logging: simple log files plus stdout, and a lightweight monitoring hook so I can inspect agent decisions in real time

Optional add-ons such as voice I/O, local LLM hosting, or a RAG knowledge base are nice-to-haves; please flag if you’ve done them before and we can prioritise later milestones.

Deliverables
• Clean, well-commented source code in a private repo
• Docker files & compose script for one-shot deployment
• Step-by-step deployment guide (markdown)
• Minimal API reference showing how to trigger the agent programmatically
• Short recorded demo or screenshots proving autonomous task execution

When you respond, link me to any related automation or agent work you’ve shipped—especially projects that balanced multiple tools or required reliable task sequencing. I’m ready to start as soon as I see a solid plan and timeline.