Lang Chat

Chat with local (and remote) LLMs right inside VS Code — LM Studio style. Bring your own models and keys, keep every conversation as a versionable file, and use tools, embedded model management and neural text‑to‑speech without leaving the editor.

Why Lang Chat

• 🔒 Local‑first & private — runs against your own LLM (LM Studio, Ollama…), your keys live in VS Code SecretStorage, the managed server binds to 127.0.0.1, and there is no telemetry.
• 🧩 Five backends, one UI — OpenAI‑compatible, Ollama, OpenRouter, Google Gemini and Anthropic Claude, switchable per conversation.
• 📄 Conversations as files — each chat is a human‑readable .chat (config + history) you can diff, version and share.
• 🦙 Models, batteries included — manage an embedded Ollama and browse/download GGUF models from Hugging Face without installing anything.
• 🔧 Agentic tools — workspace filesystem + MCP servers (function calling) on every backend.
• 🗣️ Read aloud — system voices or self‑contained neural Piper TTS.

Features

Sidebar — Engines · Models · Voices · Dictionary, with a .chat open	Chat with the Reasoning & Tools panels
Per‑conversation settings (⚙) — backend, model, sampling, read‑aloud	Lang Chat settings in VS Code

• 💬 Streaming responses, token by token, with a Stop button and auto‑save after each turn.
• 🧠 Reasoning / thinking panel for models that expose it.
• 🦙 Embedded Ollama + Hugging Face GGUF explorer: capability badges, quantization options and downloads with progress (shows size and free disk space first; retry/cancel).
• 🔧 Tools (function calling): native workspace filesystem + MCP servers — agentic loop.
• 🗣️ Read aloud (TTS): system voices (Web Speech) or neural Piper (local, managed daemon).
• 🔎 Search in chat (Ctrl/Cmd+F), 🔍 zoom (Alt/Option + wheel), 🌳 fork, 🕓 compare versions, ♻️ regenerate / continue / merge / edit / delete messages.
• 🖼️ Attachments (images & documents), 🧾 export to standalone HTML / PDF.
• 🧮 Context management: auto‑summarize when context fills up, or send only the last N messages — both shown visually in the chat.
• 🔤 Spell‑check with a personal dictionary, and internationalization (English / Spanish).

Backends

Configure any of these per conversation (in the ⚙ panel) or as the default in Settings:

Quick start

1. Install Lang Chat from the Marketplace.
2. Command palette (Cmd/Ctrl+Shift+P) → “Lang Chat: New chat” → choose where to save the .chat file.
3. Pick a backend in the ⚙ panel and start chatting.

Have LM Studio (local server enabled) or Ollama running first — or use a hosted backend (OpenRouter / Gemini / Anthropic) with an API key.

API keys are best stored securely: run “Lang Chat: Set API Key (secure)” to keep them in VS Code SecretStorage instead of plain settings.

Local models (embedded Ollama)

Lang Chat can manage its own Ollama server without you installing anything:

• The Lang Chat sidebar groups everything into sections: Engines (Ollama / Piper, with run/stop/install), Models (local models + downloads), Voices and Dictionary.
• The ＋ button opens an LM Studio‑style explorer: searches GGUF models on Hugging Face, shows capability badges and quantization options, and downloads with progress.
• On first use it downloads the Ollama binary (SHA256‑verified, fail‑closed) into your global storage; the server runs only on 127.0.0.1. Configure under Settings → Lang Chat → Ollama.

.chat files

Each conversation is a .chat file (human‑readable JSON) storing the inference config + full history. Opening it shows the chat UI; everything is persisted in the file, so it is git‑versionable. A .chat may reference its system prompt from an external .md file (systemPromptFile, confined to the .chat's directory).

Tools (function calling)

With Tools on (⚙, available on every backend), the model can call tools in an agentic loop:

• Workspace filesystem (native, no setup): fs_list, fs_read, fs_write, scoped to the workspace folder.
• MCP servers: define them in a .mcp/ folder (one *.json per server) or a .mcp.json at the workspace root. Each server's tools are exposed as server__tool.

MCP servers and fs_write only run in a trusted workspace.

Privacy

• Your API keys can be stored in VS Code SecretStorage (not plain settings).
• The managed Ollama server and the Piper TTS daemon bind to 127.0.0.1 only.
• No telemetry — Lang Chat does not phone home. Network traffic goes only to the LLM backend you configure and, on demand, to Hugging Face / PyPI to download models and the TTS engine.

Configuration

Settings under Settings → Lang Chat:

Third‑party components & licenses

Lang Chat is MIT licensed. It bundles or downloads third‑party components under their own terms:

The neural TTS engine (Piper) is GPL and is fetched on demand from PyPI; it is not shipped inside the extension package.

License

Released under the MIT License.