Overview
AI Coding Agent is a local-first, multi-agent coding assistant that plans work, writes and refines code, debugs failures, and uses tools against your filesystem and runtime. It runs on a local LLM via Ollama (e.g. DeepSeek Coder), so prompts and generated code stay on your machine—ideal when you want automation without sending proprietary repos to a hosted API.
The implementation follows a specialized multi-agent pattern: separate agents focus on planning, implementation, and debugging, coordinated through a core orchestrator and a tools layer (file ops, execution helpers, etc.). Long-horizon context is supported with structured memory (Supabase) and semantic recall via Chroma vector memory, so later steps can still “remember” earlier decisions and codebase facts.
Repository: Milanirosefrancis/ai-coding-agent — clone it to run the CLI, configure Ollama, and wire optional memory backends from requirements.txt.
What it can do
- End-to-end project work — describe a feature or greenfield task; the planner breaks it down, the coder generates patches, and the debugger tightens loops when something breaks.
- GitHub-style inputs — point the workflow at repositories or unpacked code so analysis and edits stay grounded in real project layout.
- Links and external context — fetch and reason about content from URLs when you need API docs, specs, or reference material alongside the repo.
- Autonomous tool use — the agent doesn’t only “chat”; it can execute allowed tools (per project safety rules) to read files, run commands, and iterate.
Architecture (mental model)
- CLI — you describe intent, approve or steer plans, and inspect logs.
- Core agent — routes work to specialists and merges their outputs.
- Planner / Coder / Debugger — narrow prompts and responsibilities reduce confusion and help the local model stay on task.
- Memory — Supabase for durable session or project state; Chroma for embeddings-backed retrieval over notes, prior outputs, or indexed fragments.
- Ollama — inference for all LLM turns; swap models by changing your Ollama tag.
Why it’s interesting
Shipping a multi-agent system on a local stack forces clear boundaries between planning, coding, and verification—and makes memory + retrieval first-class instead of an afterthought. This repo is a solid base for experiments in agentic workflows, RAG over dev artifacts, and tool-grounded code generation without cloud lock-in.