I built an open-source coding agent that makes context visible and editable — you curate exactly what the LLM sees

I've been frustrated with terminal coding agents where context assembly is invisible — the model decides what to read, context bloat creeps in, and output quality degrades without you knowing why. So I built **Nice Coding Agent**, a human-in-the-loop coding workbench with a web UI. It's open source and I'd love feedback. 📸 Screenshot of the UI in the comments 👇 The core idea: instead of an opaque context window, you get a **visible, editable context stack**. Every file, search result, plan, or note is a card you can pin, minimize, edit in-place, summarize, or remove before it's fed to the model. A real-time token meter shows you exactly how close you are to bloating the context. You curate what the LLM sees rather than hoping it reads the right things. A few other things that make it different from a fire-and-forget agent: * **Separated workflows instead of one generalist loop** — Build Context → Plan → Implement → Research → Inspect/Browse. Plan produces a reviewable plan; Implement turns the approved plan into proposed diffs you accept per-file, not all-or-nothing. * **Hybrid code search that doesn't rely on the model guessing** — code is indexed into Postgres (ParadeDB BM25 + pgvector + cross-encoder reranking) with tree-sitter chunking. Searching "where do we validate JWT tokens" finds the right function even if those exact words aren't there. * **Local-first retrieval** — embeddings and reranking run locally via sentence-transformers. Your code never leaves for a third-party indexing service. * **Sandboxed execution** — agent-generated code runs in an OS-level sandbox (macOS seatbelt, Linux bubblewrap) so it can verify assumptions before writing a diff. * **It doubles as an MCP server** — exposes `search_code` / `search_documents` / `build_comprehensive_context` over SSE, so Claude Code or any MCP client can plug into your code index. Complementary, not just competitive. * **Multi-provider** with a one-click Standard/High tier toggle (cheap model for exploration, frontier model for final implementation). Defaults to a free NVIDIA tier so the barrier to entry is low. **Honest framing:** this is *not* an autonomous agent. If you want to type a goal and walk away, terminal agents win. This wins when you want control, visibility, and curation — reviewing every plan and change, hand-tuning context, and keeping retrieval local. Built in Python with NiceGUI for the frontend and LangChain/LangGraph for orchestration. Would especially love feedback on the context-stack approach — is visible curation something you'd actually use, or do you prefer the model just handling it?