gradium-ai/gradbot/gradbot-voice-agent

Gradbot Voice Agent Builder

Build working voice agent apps using the gradbot framework. Output is a complete, runnable app with backend, frontend, prompts, and configuration.

Important

• ALWAYS read references/backend-template.md and references/frontend-guidelines.md before generating code
• The generated app MUST follow the exact patterns from the reference files — these are battle-tested
• Use the frontend-design skill to generate the static/index.html — do NOT write a generic HTML file

Two Complexity Levels

Simple chat (no tools)

For apps that are purely conversational with no actions/side-effects:

• No tools, no tool handler, no state dataclass needed
• Do NOT pass on_tool_call to gradbot.websocket.handle_session() (omit it entirely)
• Prompt can come from a file OR from the frontend (editable textarea)
• No prompts/ directory needed if prompt comes from frontend
• Pass with_voices=True to gradbot.routes.setup() to enable voice selection UI
• See the "Minimal (no tools)" template in references/backend-template.md

Tool-using agent (with tools)

For apps with actions (ordering, searching, game mechanics, etc.):

• Define tools via gradbot.ToolDef and implement tool handlers
• Track state in a dataclass, update it in tool handlers
• System prompts loaded from prompts/ files
• Include on_tool_call callback in gradbot.websocket.handle_session()
• See the "With tools" template in references/backend-template.md

Workflow

Step 1: Understand the Use Case

Before writing code, identify:

1. Domain: What is the voice agent about?
2. Persona: Who is the AI character? What personality?
3. Tools needed: What actions can it perform? (If none, use simple chat pattern)
4. State: What data to track across the conversation? (If none, use simple chat)
5. UI needs: What should the frontend show besides transcript?

Step 2: Create the App Directory

Simple chat (no tools, prompt from frontend):

<app-dir>/
  main.py
  static/
    index.html
  pyproject.toml

Tool-using agent (tools, file-based prompts):

<app-dir>/
  main.py
  game.py          # domain module (state, tools, tool handler)
  prompts/
    main.txt       # (or base.txt + phase1.txt, phase2.txt, etc.)
  static/
    index.html
  pyproject.toml

If the app has static data (menus, inventories, etc.), include it as JSON files.

Step 3: Write the Backend (main.py)

Consult references/backend-template.md for exact code patterns. Choose the right template:

• Minimal (no tools): ~30 lines in a single main.py, voice selection, frontend-editable prompt
• With tools: thin main.py (~20 lines) + domain module (game.py, ~100+ lines) with state, tools, prompts, and tool handler

Key setup rules:

• Use gradbot.config.load(Path(__file__).parent) or gradbot.config.from_env() for config loading — it loads config.yaml from the app directory and any shared parent config automatically. Returns a Config object with .client_kwargs and .session_kwargs properties.
• ALWAYS call gradbot.routes.setup(app, config=cfg, static_dir=...) to serve frontend and bundled JS
• Pass with_voices=True to gradbot.routes.setup() if the frontend needs voice selection (registers /api/voices)
• rewrite_rules enables language-specific text rewriting before TTS. Get it from voice.language.rewrite_rules (returns "en", "fr", etc.). Do NOT use .value — Lang is not a Python enum.
• ALWAYS set silence_timeout_s to 0.0 — the default 5s causes the agent to re-prompt itself with its last message when the user is silent
• Pass config=cfg to handle_session() to auto-set run_kwargs, output_format, and debug from the config. Or pass them individually for custom setups.

Critical rules for tool definitions (when using tools):

• parameters_json must be a JSON string — use json.dumps()
• NEVER use "type": "array" in parameters (some LLMs like Gemma fail). Use "type": "string" with "description": "Comma-separated list" instead
• Tool descriptions should say WHEN to call the tool, not just what it does

Critical rules for tool handlers (when using tools):

• on_tool_call receives 3 args: (handle, input_handle, websocket) where handle is a gradbot.ToolHandle
• handle.name gives the tool name, handle.args gives parsed args (already deserialized dict)
• Send results via handle.send_json({...}) (auto-serializes) or handle.send(json.dumps({...})) for raw JSON
• Send errors via handle.send_error("message")
• Send UI updates via websocket.send_json({"type": "custom_event", ...})
• Use input_handle.send_config(new_config) to swap prompts/tools mid-session

Critical rules for system prompts:

• Keep responses SHORT: "1-2 sentences max" for voice (long text = slow TTS)
• If using tools: include "Call tools silently FIRST, then speak"
• Be explicit about what the agent should NOT do

Step 4: Write System Prompts (tool-using agents only)

Skip this step for simple chat apps where the prompt comes from the frontend.

For tool-using agents, create prompts/base.txt and prompts/main.txt:

• base.txt: personality, speaking style, boundaries, response length
• main.txt: conversation flow, when to call tools, error handling

For multi-phase apps, create phase1.txt, phase2.txt, etc. and swap via input_handle.send_config().

Step 5: Build the Frontend

IMPORTANT: Invoke the frontend-design skill to create static/index.html.

When invoking frontend-design, provide these requirements:

• It's a voice agent UI — primary interaction is speech, not typing
• Must include a mic/call button to start/stop the session
• Must show a live transcript (user bubbles + agent bubbles)
• CRITICAL: Must load audio via three script tags (NOT ES module imports): opus-encoder.js, audio-processor.js, synced-audio-player.js from /static/js/ — then use SyncedAudioPlayer as a global (it is NOT an ES module)
• Must follow the WebSocket protocol in references/frontend-guidelines.md
• Must include echo cancellation checkbox (checked by default) — without it the agent hears its own TTS
• For simple chat: include voice selector grid, editable prompt textarea, speed slider
• For tool-using agents: include domain-specific content panels (menu, inventory, results, etc.)

Step 6: Create pyproject.toml

[project]
name = "<app-name>"
version = "0.1.0"
description = "<description>"
requires-python = ">=3.12"
dependencies = ["gradbot"]

gradbot includes fastapi, uvicorn with websocket support, pydantic-settings, and pyyaml. Add extra dependencies only if needed.

Step 7: Verify

After generating all files:

• [ ] main.py imports gradbot (uses gradbot.websocket, gradbot.routes, gradbot.config)
• [ ] Uses gradbot.config.load(Path(__file__).parent) or gradbot.config.from_env() for configuration
• [ ] gradbot.routes.setup() called with static_dir and with_voices=True (if voice selection needed)
• [ ] rewrite_rules uses voice.language.rewrite_rules (not .value)
• [ ] silence_timeout_s set to 0.0
• [ ] WebSocket endpoint calls gradbot.websocket.handle_session() with correct callbacks
• [ ] If no tools: on_tool_call is NOT passed to handle_session()
• [ ] If tools: parameters_json is a JSON string via json.dumps(), tool results via handle.send_json() or handle.send(json.dumps())
• [ ] If tools: on_tool_call takes 3 args (handle, input_handle, websocket), uses handle.name and handle.args
• [ ] Frontend loads JS via three <script> tags, NOT ES module imports
• [ ] Frontend onText destructures a single object: ({ text, turnIdx, isUser }) =>
• [ ] Frontend has echo cancellation checkbox wired to SyncedAudioPlayer
• [ ] System prompt enforces short responses for voice

Common Patterns

Simple Voice Chat (no tools)

• No state, no tools, no prompts/ directory
• Voice selection + editable prompt from frontend
• Frontend: transcript + voice grid + speed slider
• ~40 lines of backend code

Voice Game (haggling, trivia, roleplay)

• State tracks game progress, scores, inventory
• Tools: game actions (buy, sell, attack, answer)
• Prompt: character personality + game rules + win/lose conditions
• Frontend: game state display (inventory, score, health bar)

Customer Service Agent (ordering, booking, support)

• State tracks order/booking details
• Tools: CRUD operations (add, remove, modify, confirm)
• Prompt: service persona + menu/catalog knowledge + ordering rules
• Frontend: order summary, menu display, confirmation

Tutoring / Language Learning

• State tracks lesson progress, mistakes, topics covered
• Tools: check answer, advance lesson, provide hint
• Prompt: teacher persona + curriculum + encouragement style
• Frontend: lesson content, progress tracker, exercise display

Search-and-Act Agent (hotel booking, product search)

• State tracks search results and selections
• Tools: search, get details, book/purchase
• Prompt: phases that swap as user progresses through workflow
• Frontend: search results cards, detail view, confirmation

Troubleshooting

Agent gives long responses

Add to system prompt: "CRITICAL: Keep ALL responses to 1-2 SHORT sentences. This is a voice conversation, not a text chat."

Agent repeats itself / re-prompts when user is silent

Set silence_timeout_s = 0.0 in the session config. The default 5s timeout causes the agent to re-send its last message as context and generate a new response.

Tool calls fail with "Invalid JSON"

tool_handle.send() requires a valid JSON string. Always use json.dumps({...}), never pass a raw string.

Tool calls fail silently

Ensure on_tool_call is passed to handle_session(). Check that handle.send() is called with a JSON string, or use handle.send_json() with a dict.

Audio doesn't play

Verify frontend loads JS via three script tags (opus-encoder.js, audio-processor.js, synced-audio-player.js), NOT via ES module import. SyncedAudioPlayer is a global.

Transcript shows [object Object]

The onText callback receives a single object, not separate args. Must destructure: ({ text, turnIdx, isUser }) =>.

Agent hears its own voice (feedback loop)

Add an echo cancellation checkbox and wire it to SyncedAudioPlayer({ echoCancellation: checkbox.checked }).

Voice selection not working

Ensure gradbot.routes.setup(app, ..., with_voices=True) is passed. Without with_voices=True, the /api/voices endpoint is not registered.

WebSocket returns 404 / "Unsupported upgrade request"

Uvicorn needs the websockets library. Ensure you're using gradbot[demos] (which includes uvicorn[standard]). If still failing, add websockets explicitly to your dependencies.

Wrong language TTS pronunciation

Set rewrite_rules to voice.language.rewrite_rules. This enables language-specific text rewriting before synthesis.