FAQ & Troubleshooting

Python dependency commands on this page use a PM-prepared source checkout. After a dependency change, reactivate the checkout and restart Tutou.

Quick answers and fixes for the most common questions and issues.

Frequently Asked Questions

What LLM providers work with Tutou?

Tutou Agent works with any OpenAI-compatible API. Supported providers include:

OpenRouter — access hundreds of models through one API key (recommended for flexibility)
Nous Portal — Nous Research's subscription gateway — 300+ models plus web/image/TTS/browser through one OAuth login (recommended for newcomers)
OpenAI — GPT-5.4, GPT-5-codex, GPT-4.1, GPT-4o, etc.
Anthropic — Claude models (direct API, OAuth via tutou auth add anthropic, OpenRouter, or any compatible proxy)
Google — Gemini models (direct API via gemini provider, OpenRouter, or compatible proxy)
z.ai / ZhipuAI — GLM models
Kimi / Moonshot AI — Kimi models
MiniMax — global and China endpoints
Local models — via Ollama, vLLM, llama.cpp, SGLang, or any OpenAI-compatible server

Set your provider with tutou model or by editing ~/.tutou/.env. See the Environment Variables reference for all provider keys.

Does it work on Windows/Android/my platform??

See Platform Support for the full platform availability matrix.

I run Tutou in WSL2. What's the best way to control my normal Windows Chrome?

Prefer an MCP bridge over /browser connect.

Recommended pattern:

run Tutou inside WSL2
keep using your normal signed-in Chrome on Windows
add chrome-devtools-mcp as an MCP server through cmd.exe or powershell.exe
let Tutou use the resulting MCP browser tools

This is more reliable than trying to force Tutou core browser transport to attach directly across the WSL2/Windows boundary.

See:

Is my data sent anywhere?

API calls go only to the LLM provider you configure (e.g., OpenRouter, your local Ollama instance). Tutou Agent does not collect telemetry, usage data, or analytics. Your conversations, memory, and skills are stored locally in ~/.tutou/.

Can I use it offline / with local models?

Yes. Run tutou model, select Custom endpoint, and enter your server's URL:

tutou model
# Select: Custom endpoint (enter URL manually)
# API base URL: http://localhost:11434/v1
# API key: ollama
# Model name: qwen3.5:27b
# Context length: 64000   ← Tutou minimum; set this to match your server's actual context window

Or configure it directly in config.yaml:

model:
  default: qwen3.5:27b
  provider: custom
  base_url: http://localhost:11434/v1

Tutou persists the endpoint, provider, and base URL in config.yaml so it survives restarts. If your local server has exactly one model loaded, /model custom auto-detects it. You can also set provider: custom in config.yaml — it's a first-class provider, not an alias for anything else.

This works with Ollama, vLLM, llama.cpp server, SGLang, LocalAI, and others. See the Configuration guide for details.

Ollama users

If you set a custom num_ctx in Ollama (e.g., ollama run --num_ctx 64000), make sure to set the matching context length in Tutou — Ollama's /api/show reports the model's maximum context, not the effective num_ctx you configured.

Timeouts with local models

Tutou auto-detects local endpoints and relaxes streaming timeouts (read timeout raised from 120s to 1800s, stale stream detection disabled). If you still hit timeouts on very large contexts, set TUTOU_STREAM_READ_TIMEOUT=1800 in your .env. See the Local LLM guide for details.

How much does it cost?

Tutou Agent itself is free and open-source (MIT license). You pay only for the LLM API usage from your chosen provider. Local models are completely free to run.

Can multiple people use one instance?

Yes. The messaging gateway lets multiple users interact with the same Tutou Agent instance via Telegram, Discord, Slack, WhatsApp, or Home Assistant. Access is controlled through allowlists (specific user IDs) and DM pairing (first user to message claims access).

What's the difference between memory and skills?

Memory stores facts — things the agent knows about you, your projects, and preferences. Memories are retrieved automatically based on relevance.
Skills store procedures — step-by-step instructions for how to do things. Skills are recalled when the agent encounters a similar task.

Both persist across sessions. See Memory and Skills for details.

Can I use it in my own Python project?

Yes. Import the AIAgent class and use Tutou programmatically:

from run_agent import AIAgent

agent = AIAgent(model="anthropic/claude-opus-4.7")
response = agent.chat("Explain quantum computing briefly")

See the Python Library guide for full API usage.

Troubleshooting

Installation Issues

`tutou: command not found` after installation

Cause: Your shell hasn't reloaded the updated PATH.

Solution:

# Reload your shell profile
source ~/.bashrc    # bash
source ~/.zshrc     # zsh

# Or start a new terminal session

If it still doesn't work, verify the install location:

which tutou
ls ~/.local/bin/tutou

tip

The installer adds ~/.local/bin to your PATH. If you use a non-standard shell config, add export PATH="$HOME/.local/bin:$PATH" manually.

Unsupported Python version

Current first-party installations require Python 3.14, not an arbitrary newer version. The >=3.11,<3.15 range in pyproject.toml allows older installations to run the updater before switching to 3.14; it does not mean the current runtime supports 3.11–3.13. The installer and packaged distributions provide their pinned interpreter.

For a manual source environment, use the development setup. Do not replace the interpreter inside an installed app or container. For a managed-install error, run tutou doctor and use that installation's update method.

Terminal commands say `node: command not found` (or `nvm`, `pyenv`, `asdf`, …)

Cause: Tutou builds a per-session environment snapshot by running bash -l once at startup. A bash login shell reads /etc/profile, ~/.bash_profile, and ~/.profile, but does not source ~/.bashrc — so tools that install themselves there (nvm, asdf, pyenv, cargo, custom PATH exports) stay invisible to the snapshot. This most commonly happens when Tutou runs under systemd or in a minimal shell where nothing has pre-loaded the interactive shell profile.

Solution: Tutou auto-sources ~/.bashrc by default. If that's not enough — e.g. you're a zsh user whose PATH lives in ~/.zshrc, or you init nvm from a standalone file — list the extra files to source in ~/.tutou/config.yaml:

terminal:
  shell_init_files:
    - ~/.zshrc                     # zsh users: pulls zsh-managed PATH into the bash snapshot
    - ~/.nvm/nvm.sh                # direct nvm init (works regardless of shell)
    - /etc/profile.d/cargo.sh      # system-wide rc files
  # When this list is set, the default ~/.bashrc auto-source is NOT added —
  # include it explicitly if you want both:
  #   - ~/.bashrc
  #   - ~/.zshrc

Missing files are skipped silently. Sourcing happens in bash, so files that rely on zsh-only syntax may error — if that's a concern, source just the PATH-setting portion (e.g. nvm's nvm.sh directly) rather than the whole rc file.

Independently of the init files, every terminal command's PATH is completed with the standard system directories (/usr/local/bin, /opt/homebrew/bin, …), the Tutou-managed runtime dirs, and ~/.local/bin when it exists (the pip --user / pipx / uv tool install target) — appended after your own entries, so precedence is unchanged. This covers backends started with a thin non-interactive PATH (systemd, GUI launchers, the Desktop SSH remote backend) without any configuration.

To disable the auto-source behaviour (strict login-shell semantics only):

terminal:
  auto_source_bashrc: false

`uv: command not found`

Cause: The uv package manager isn't installed or not in PATH.

Solution:

curl -LsSf https://astral.sh/uv/install.sh | sh
source ~/.bashrc

Permission denied errors during install

Cause: Insufficient permissions to write to the install directory.

Solution:

# Don't use sudo with the installer — it installs to ~/.local/bin
# If you previously installed with sudo, clean up:
sudo rm /usr/local/bin/tutou
# Then re-run the standard installer
curl -fsSL https://docs.tutoukeji.com/install.sh | bash

Provider & Model Issues

The agent says "Tutou policy" or "Tutou guardrails" refused my request

A model cannot reliably identify why it refused a request. If the refusal appears only in the assistant's prose, its claim that a hidden Tutou runtime policy caused it may be a hallucinated explanation or a restriction applied by the selected model or provider.

Tutou enforcement is explicit: a blocked tool action returns a tool error naming the denied command or path, and an approval-required action shows an approval prompt. Tutou does not silently turn those execution controls into a general content-refusal layer. Provider-level controls can still apply when configured, such as Amazon Bedrock Guardrails.

To isolate the source:

Run /status to confirm the active model and provider.
Check whether the refusal includes an actual Tutou tool error or approval prompt. If it is prose only, do not treat the model's attribution as runtime evidence.
Retry in a fresh session with another configured model or provider. A refusal that changes with the model is model/provider behavior, not a Tutou execution control.
If an explicit tool error appears, use its exact text when reporting the problem.

See Security for Tutou' documented execution controls and Providers for provider configuration.

"…refused this request because of a policy on your account"

Meaning: the provider rejected the request for an account-level reason that retrying cannot change — an aggregator's data/privacy settings excluded every endpoint for the model, or the model's upstream provider has blocked the account (for example this user has been blocked for a previous policy violation, which OpenRouter can relay inside an otherwise successful HTTP 200 stream). Tutou sends the request once, does not retry it or rotate credentials, and moves to your fallback chain if one is configured.

Solution: check the account's status and data/privacy settings with the provider named in the reply, or switch to another model or provider with /model. tutou fallback add routes future blocks to a backup automatically.

"Could not open a stream to `<host>` after N attempts (request X KB)"

Meaning: every connect attempt to that endpoint failed before a single stream event arrived, so nothing was billed; the normal retry/fallback chain still runs afterwards. The line names the host actually contacted, how many attempts were made, and the serialized request size — the three things that separate an outage from a request-size limit.

Solution: if the request is large (hundreds of KB — long coding sessions reach this once the context grows) and short new chats work, the endpoint or a proxy in front of it is likely rejecting bodies that size: raise its body limit, or run /compress to shrink the context. If the request is small, the endpoint is unreachable — check the base_url, then retry with /retry. logs/agent.log records the exception chain for each attempt.

Messaging replies: "interrupted mid-request" vs "not running or is unreachable" vs "could not reach"

Chat surfaces (Telegram, Discord, Slack, …) never show the raw transport exception; the gateway maps it to one of three short replies, and the difference tells you where to look:

Reply	What happened	What to do
"The connection to the AI model service was interrupted mid-request — usually transient."	An established connection was cut (`Connection reset by peer`, EOF, `RemoteProtocolError`). The endpoint answered the connect, so it is running.	`/retry`. If it recurs on large requests, see the "stream" entry above.
"The AI model service isn't reachable right now — the configured model endpoint is not running or is unreachable."	Nothing accepted the connection (`Connection refused`, no route to host, DNS failure).	Start the model server / check `base_url`, then `/retry`; `tutou doctor` on the host.
"Tutou could not reach the AI model service (no further detail from the SDK)."	The SDK reported a generic `APIConnectionError` and kept no cause; neither of the above is certain.	`/retry`; `tutou doctor` if it persists. The raw exception is in `tutou logs`.

`/model` only shows one provider / can't switch providers

Cause: /model (inside a chat session) can only switch between providers you've already configured. If you've only set up OpenRouter, that's all /model will show.

Solution: Exit your session and use tutou model from your terminal to add new providers:

# Exit the Tutou chat session first (Ctrl+C or /quit)

# Run the full provider setup wizard
tutou model

# This lets you: add providers, run OAuth, enter API keys, configure endpoints

After adding a new provider via tutou model, start a new chat session — /model will now show all your configured providers.

Quick reference

Want to...	Use
Add a new provider	`tutou model` (from terminal)
Enter/change API keys	`tutou model` (from terminal)
Switch model mid-session	`/model <name>` (inside session)
Switch to different configured provider	`/model provider:model` (inside session)

API key not working

Cause: Key is missing, expired, incorrectly set, or for the wrong provider.

Solution:

# Check your configuration
tutou config show

# Re-configure your provider
tutou model

# Or set directly
tutou config set OPENROUTER_API_KEY sk-or-v1-xxxxxxxxxxxx

warning

Make sure the key matches the provider. An OpenAI key won't work with OpenRouter and vice versa. Check ~/.tutou/.env for conflicting entries.

Model not available / model not found

Cause: The model identifier is incorrect or not available on your provider.

Solution:

# List available models for your provider
tutou model

# Set a valid model
tutou config set model.default anthropic/claude-opus-4.7

# Or specify per-session
tutou chat --model openrouter/meta-llama/llama-3.1-70b-instruct

Rate limiting (429 errors)

Cause: You've exceeded your provider's rate limits.

Solution: Wait a moment and retry. For sustained usage, consider:

Upgrading your provider plan
Switching to a different model or provider
Using tutou chat --provider <alternative> to route to a different backend

Context length exceeded

Cause: The conversation has grown too long for the model's context window, or Tutou detected the wrong context length for your model.

Solution:

# Compress the current session
/compress

# Or start a fresh session
tutou chat

# Use a model with a larger context window
tutou chat --model openrouter/google/gemini-3-flash-preview

If this happens on the first long conversation, Tutou may have the wrong context length for your model. Check what it detected:

Look at the CLI startup line — it shows the detected context length (e.g., 📊 Context limit: 128000 tokens). You can also check with /usage during a session.

Local servers (llama.cpp, Ollama) that go silent instead of erroring: when a provider rejects a request as too large, Tutou compacts the conversation and rebuilds the request. Tutou re-measures the complete rebuilt request (system prompt + tool schemas + messages) before retrying, and runs further bounded compaction passes if it is still over the threshold. If the request still cannot fit, the turn ends with Context length exceeded: compression could not reduce the rebuilt request below the safe threshold rather than sending an oversized request that llama.cpp would silently truncate (stop processing: n_tokens = 65535, truncated = 1 in the server log). If you hit that message, the fix is almost always the configured context_length above: make it match the server's actual -c / --ctx-size.

"The model server rejected this request as too large, but this conversation is only about N tokens…": a local server (localhost, LAN, Tailscale) said "context exceeded" without quoting any measurement, while Tutou's own estimate of the request is far below the window it knows for the model — so it does not compress or blame the conversation, and the turn stays retryable. On single-slot local servers (LM Studio, Ollama) this is almost always another request holding the server's context at that moment — typically a background memory review from an earlier session (thread=bg-review in logs/agent.log). Wait a moment and /retry. If it recurs with no other Tutou process running, the server is loading the model with a smaller window than Tutou assumes: raise the server's context setting or lower model.context_length to match it. Hosted providers never get this message: they have no shared slot to wait out, so the same rejection there means the route's real window is smaller than Tutou assumes, and Tutou compresses and retries instead.

To fix context detection, set it explicitly:

# In ~/.tutou/config.yaml
model:
  default: your-model-name
  context_length: 131072  # your model's actual context window

Or for custom endpoints, add it per-model on the provider entry:

providers:
  my-server:
    api: "http://localhost:11434/v1"
    models:
      qwen3.5:27b:
        context_length: 64000

(Older configs use the legacy custom_providers: list — still supported and auto-migrated to providers:.)

See Context Length Detection for how auto-detection works and all override options.

Terminal Issues

Command blocked as dangerous

Cause: Tutou detected a potentially destructive command (e.g., rm -rf, DROP TABLE). This is a safety feature.

Solution: When prompted, review the command and type y to approve it. You can also:

Ask the agent to use a safer alternative
See the full list of dangerous patterns in the Security docs

tip

This is working as intended — Tutou never silently runs destructive commands. The approval prompt shows you exactly what will execute.

`sudo` not working via messaging gateway

Cause: The messaging gateway runs without an interactive terminal, so sudo cannot prompt for a password.

Solution:

Avoid sudo in messaging — ask the agent to find alternatives
If you must use sudo, configure passwordless sudo for specific commands in /etc/sudoers
Or switch to the terminal interface for administrative tasks: tutou chat

Docker backend not connecting

Cause: Docker daemon isn't running or the user lacks permissions.

Solution:

# Check Docker is running
docker info

# Add your user to the docker group
sudo usermod -aG docker $USER
newgrp docker

# Verify
docker run hello-world

Messaging Issues

Bot not responding to messages

Cause: The bot isn't running, isn't authorized, or your user isn't in the allowlist.

Solution:

# Check if the gateway is running
tutou gateway status

# Start the gateway
tutou gateway start

# Check logs for errors
cat ~/.tutou/logs/gateway.log | tail -50

Messages not delivering

Cause: Network issues, bot token expired, or platform webhook misconfiguration.

Solution:

Verify your bot token is valid with tutou gateway setup
Check gateway logs: cat ~/.tutou/logs/gateway.log | tail -50
For webhook-based platforms (Slack, WhatsApp), ensure your server is publicly accessible

Allowlist confusion — who can talk to the bot?

Cause: Authorization mode determines who gets access.

Solution:

Mode	How it works
Allowlist	Only user IDs listed in config can interact
DM pairing	First user to message in DM claims exclusive access
Open	Anyone can interact (not recommended for production)

Configure in ~/.tutou/config.yaml under your gateway's settings. See the Messaging docs.

Gateway won't start

Cause: Missing dependencies, port conflicts, or misconfigured tokens.

Solution:

# Install core messaging gateway dependencies
cd ~/.tutou/tutou-agent && python -c "import pm; pm.sync_venv(['messaging'], explicit=True)"  # Telegram, Discord, Slack, and shared gateway deps

# Check for port conflicts
lsof -i :8080

# Verify configuration
tutou config show

WSL: Gateway keeps disconnecting or `tutou gateway start` fails

Cause: WSL's systemd support is unreliable. Many WSL2 installations don't have systemd enabled, and even when enabled, services may not survive WSL restarts or Windows idle shutdowns.

Solution: Use foreground mode instead of the systemd service:

# Option 1: Direct foreground (simplest)
tutou gateway run

# Option 2: Persistent via tmux (survives terminal close)
tmux new -s tutou 'tutou gateway run'
# Reattach later: tmux attach -t tutou

# Option 3: Background via nohup
nohup tutou gateway run > ~/.tutou/logs/gateway.log 2>&1 &

If you want to try systemd anyway, make sure it's enabled:

Open /etc/wsl.conf (create it if it doesn't exist)
Add:
```
[boot]
systemd=true
```
From PowerShell: wsl --shutdown
Reopen your WSL terminal
Verify: systemctl is-system-running should say "running" or "degraded"

Auto-start on Windows boot

For reliable auto-start, use Windows Task Scheduler to launch WSL + the gateway on login:

Create a task that runs wsl -d Ubuntu -- bash -lc 'tutou gateway run'
Set it to trigger on user logon

macOS: Node.js / ffmpeg / other tools not found by gateway

Cause: launchd services inherit a minimal PATH (/usr/bin:/bin:/usr/sbin:/sbin) that doesn't include Homebrew, nvm, cargo, or other user-installed tool directories. This commonly breaks the WhatsApp bridge (node not found) or voice transcription (ffmpeg not found).

Solution: The gateway captures your shell PATH when you run tutou gateway install. If you installed tools after setting up the gateway, re-run the install to capture the updated PATH:

tutou gateway install    # Re-snapshots your current PATH
tutou gateway start      # Detects the updated plist and reloads

You can verify the plist has the correct PATH:

/usr/libexec/PlistBuddy -c "Print :EnvironmentVariables:PATH" \
  ~/Library/LaunchAgents/ai.tutou.gateway.plist

Performance Issues

Slow responses

Cause: Large model, distant API server, or heavy system prompt with many tools.

Solution:

Try a faster/smaller model: tutou chat --model openrouter/meta-llama/llama-3.1-8b-instruct
Reduce active toolsets: tutou chat -t "terminal"
Check your network latency to the provider
For local models, ensure you have enough GPU VRAM

High token usage

Cause: Long conversations, verbose system prompts, or many tool calls accumulating context.

Solution:

# See exactly what the fixed prompt costs — breakdown by block
# (system prompt, skills index, memory, tool schemas). Runs offline.
tutou prompt-size

# Compress the conversation to reduce tokens
/compress

# Check session token usage
/usage

If the baseline looks high before you've typed anything, that's the fixed prompt budget — the system prompt plus tool schemas sent on every call. Run tutou prompt-size to measure it, then trim: disable toolsets you don't use (tutou tools) and uninstall or disable skills you don't need (tutou skills).

tip

Use /compress regularly during long sessions. It summarizes the conversation history and reduces token usage significantly while preserving context.

Session getting too long

Cause: Extended conversations accumulate messages and tool outputs, approaching context limits.

Solution:

# Compress current session (preserves key context)
/compress

# Start a new session with a reference to the old one
tutou chat

# Resume a specific session later if needed
tutou chat --continue

MCP Issues

MCP server not connecting

Cause: Server binary not found, wrong command path, or missing runtime.

Solution:

# Ensure MCP dependencies are installed (already included in standard install)
cd ~/.tutou/tutou-agent && python -c "import pm; pm.sync_venv(['mcp'], explicit=True)"

# For npm-based servers, ensure Node.js is available
node --version
npx --version

# Test the server manually
npx -y @modelcontextprotocol/server-filesystem /path/to/allowed/dir

Verify your ~/.tutou/config.yaml MCP configuration:

mcp_servers:
  filesystem:
    command: "npx"
    args: ["-y", "@modelcontextprotocol/server-filesystem", "/home/user/docs"]

Tools not showing up from MCP server

Cause: Server started but tool discovery failed, tools were filtered out by config, or the server does not support the MCP capability you expected.

Solution:

Check gateway/agent logs for MCP connection errors
Ensure the server responds to the tools/list RPC method
Review any tools.include, tools.exclude, tools.resources, tools.prompts, or enabled settings under that server
Remember that resource/prompt utility tools are only registered when the session actually supports those capabilities
Use /reload-mcp after changing config

# Verify MCP servers are configured
tutou config show | grep -A 12 mcp_servers

# Restart Tutou or reload MCP after config changes
tutou chat

MCP timeout errors

Cause: The MCP server is taking too long to respond, or it crashed during execution.

Solution:

Increase the timeout in your MCP server config if supported
Check if the MCP server process is still running
For remote HTTP MCP servers, check network connectivity

warning

If an MCP server crashes mid-request, Tutou will report a timeout. Check the server's own logs (not just Tutou logs) to diagnose the root cause.

Skills Issues

The Skills Hub page won't load in the desktop app (403 / blocked)

Cause: The docs site (docs.tutoukeji.com) is served through Vercel, whose WAF denies some residential IP ranges it considers flagged. If your network is on such a range, every request to the domain returns a 403 block page.

Solution: The Skills Hub picker probes the primary domain and automatically falls back to the equivalent GitHub Pages deployment (docs.tutoukeji.com), which serves the same catalog. If the page still fails on both origins, check whether a proxy, DNS filter, or firewall is blocking both hosts — and report the affected range to the maintainers so it can be reviewed on the deployment side.

Profiles

How do profiles differ from just setting TUTOU_HOME?

Profiles are a managed layer on top of TUTOU_HOME. You could manually set TUTOU_HOME=/some/path before every command, but profiles handle all the plumbing for you: creating the directory structure, generating shell aliases (tutou-work), tracking the active profile in ~/.tutou/active_profile, and syncing skill updates across all profiles automatically. They also integrate with tab completion so you don't have to remember paths.

No. Each messaging platform (Telegram, Discord, etc.) requires exclusive access to a bot token. If two profiles try to use the same token simultaneously, the second gateway will fail to connect. Create a separate bot per profile — for Telegram, talk to @BotFather to make additional bots.

No. Each profile has its own memory store, session database, and skills directory. They are completely isolated. If you want to start a new profile with existing memories and sessions, use tutou profile create newname --clone-all to copy everything from the current profile, or add --clone-from <profile> to copy from a specific source profile.

This isolation is also the reason to never run two agents against the same profile or Tutou home: both write memory automatically and each loads the other's writes at session start, so their stored state degrades with every session. One agent per profile; for genuinely shared memory across agents, use an external memory provider.

What happens when I run `tutou update`?

tutou update pulls the latest code and reinstalls dependencies once (not per-profile). It then syncs updated skills to all profiles automatically. You only need to run tutou update once — it covers every profile on the machine.

How many profiles can I run?

There is no hard limit. Each profile is a directory under ~/.tutou/profiles/ that carries at least one identity file (config.yaml, .env, SOUL.md, profile.yaml, auth.json or state.db); a bare directory without one (a leftover from a log rotation or cron tick) is not a profile — it is not listed or served, -p <name> reports it as missing, and tutou profile create <name> refuses to overwrite it until you move or remove it. The practical limit depends on your disk space and how many concurrent gateways your system can handle (each gateway is a lightweight Python process). Running dozens of profiles is fine; each idle profile uses no resources.

Workflows & Patterns

Using different models for different tasks (multi-model workflows)

Scenario: You use GPT-5.4 as your daily driver, but Gemini or Grok writes better social media content. Manually switching models every time is tedious.

Solution: Delegation config. Tutou can route subagents to a different model automatically. Set this in ~/.tutou/config.yaml:

delegation:
  model: "google/gemini-3-flash-preview"   # subagents use this model
  provider: "openrouter"                    # provider for subagents

Now when you tell Tutou "write me a Twitter thread about X" and it spawns a delegate_task subagent, that subagent runs on Gemini instead of your main model. Your primary conversation stays on GPT-5.4.

You can also be explicit in your prompt: "Delegate a task to write social media posts about our product launch. Use your subagent for the actual writing." The agent will use delegate_task, which automatically picks up the delegation config.

For one-off model switches without delegation, use /model in the CLI:

/model google/gemini-3-flash-preview    # switch for this session
# ... write your content ...
/model openai/gpt-5.4                   # switch back

warning

Each /model switch resets the prompt cache — the cache key includes the model, so the first message after every switch re-reads the whole conversation at full input price. On long sessions, prefer delegation (subagents get their own fresh context) or a new session over repeated back-and-forth switching.

See Subagent Delegation for more on how delegation works.

Running multiple agents on one WhatsApp number (per-chat binding)

Scenario: In OpenClaw, you had multiple independent agents bound to specific WhatsApp chats — one for a family shopping list group, another for your private chat. Can Tutou do this?

Current limitation: Tutou profiles each require their own WhatsApp number/session. You cannot bind multiple profiles to different chats on the same WhatsApp number — the WhatsApp bridge (Baileys) uses one authenticated session per number.

Workarounds:

Use a single profile with personality switching. Create different AGENTS.md context files or use the /personality command to change behavior per chat. The agent sees which chat it's in and can adapt.
Use cron jobs for specialized tasks. For a shopping list tracker, set up a cron job that monitors a specific chat and manages the list — no separate agent needed.
Use separate numbers. If you need truly independent agents, pair each profile with its own WhatsApp number. Virtual numbers from services like Google Voice work for this.
Use Telegram or Discord instead. These platforms support per-chat binding more naturally — each Telegram group or Discord channel gets its own session, and you can run multiple bot tokens (one per profile) on the same account.

See Profiles and WhatsApp setup for more details.

Controlling what shows up in Telegram (hiding logs and reasoning)

Scenario: You see gateway exec logs, Tutou reasoning, and tool call details in Telegram instead of just the final output.

Solution: The display.tool_progress setting in config.yaml controls how much tool activity is shown:

display:
  tool_progress: "off"   # options: off, new, all, verbose

off — Only the final response. No tool calls, no reasoning, no logs.
new — Shows new tool calls as they happen (brief one-liners).
all — Shows all tool activity including results.
verbose — Full detail including tool arguments and outputs.

For messaging platforms, off or new is usually what you want. After editing config.yaml, restart the gateway for changes to take effect.

You can also toggle this per-session with the /verbose command (if enabled):

display:
  tool_progress_command: true   # enables /verbose in the gateway

Managing skills on Telegram (slash command limit)

Scenario: Telegram has a 100 slash command limit, and your skills are pushing past it. You want to disable skills you don't need on Telegram, but tutou skills config settings don't seem to take effect.

Solution: Use tutou skills config to disable skills per-platform. This writes to config.yaml:

skills:
  disabled: []                    # globally disabled skills
  platform_disabled:
    telegram: [skill-a, skill-b]  # disabled only on telegram

After changing this, restart the gateway (tutou gateway restart or kill and relaunch). The Telegram bot command menu rebuilds on startup.

tip

Skills with very long descriptions are truncated to 40 characters in the Telegram menu to stay within payload size limits. If skills aren't appearing, it may be a total payload size issue rather than the 100 command count limit — disabling unused skills helps with both.

Shared thread sessions (multiple users, one conversation)

Scenario: You have a Telegram or Discord thread where multiple people mention the bot. You want all mentions in that thread to be part of one shared conversation, not separate per-user sessions.

Current behavior: Tutou creates sessions keyed by user ID on most platforms, so each person gets their own conversation context. This is by design for privacy and context isolation.

Workarounds:

Use Slack. Slack sessions are keyed by thread, not by user. Multiple users in the same thread share one conversation — exactly the behavior you're describing. This is the most natural fit.
Use a group chat with a single user. If one person is the designated "operator" who relays questions, the session stays unified. Others can read along.
Use a Discord channel. Discord sessions are keyed by channel, so all users in the same channel share context. Use a dedicated channel for the shared conversation.

Exporting Tutou to another machine

Scenario: You've built up skills, cron jobs, and memories on one machine and want to move everything to a new dedicated Linux box.

Solution:

Install Tutou Agent on the new machine:

curl -fsSL https://docs.tutoukeji.com/install.sh | bash

On the source machine, create a full backup:
```
tutou backup
```
This saves a zip archive at ~/tutou-backup-<timestamp>.zip. The full backup covers configuration, credentials, memories, skills, sessions, and profiles under the Tutou data root. It is not an application or runtime image.

Copy the zip to the new machine and import it:

# On the source machine
scp ~/tutou-backup-<timestamp>.zip newmachine:~/

# On the new machine
tutou import ~/tutou-backup-<timestamp>.zip

On the new machine, run tutou setup to verify API keys and provider config are working.

Moving a single profile to another machine

Scenario: You want to move or share one specific profile — not your full installation.

# On the source machine
tutou profile export work ./work-backup.tar.gz

# Copy the file to the target machine, then:
tutou profile import ./work-backup.tar.gz work

The imported profile will have all config, memories, sessions, and skills from the export. You may need to update paths or re-authenticate with providers if the new machine has a different setup.

`tutou backup` vs `tutou profile export`

Feature	`tutou backup`	`tutou profile export`
Use Case	Full machine migration	Porting/sharing a specific profile
Scope	Tutou data root, with the exclusions listed below	Single profile directory
Includes	All profiles, global config, API keys, sessions	Single profile: SOUL.md, memories, sessions, skills
Credentials	Included (`.env` and `auth.json`)	Excluded (stripped for safe sharing)
Format	`.zip`	`.tar.gz`

The full backup excludes:

The source checkout, dependency environments, and downloaded tools, models, and runtimes.
Build caches, checkpoints, previous backups, and quick snapshots.
Browser profiles, including copies of real-browser credentials.
Bytecode, SQLite sidecars, gateway.pid, cron.pid, and .backup.lock.

tutou backup --quick saves selected state files instead of a full archive. It is not a replacement for the full backup before a machine migration.

Full backups report files that fail to copy. An archive can therefore exist with missing data. Review the skipped-file report before you remove the source installation. Restored package declarations let PM download dependencies again. Bytecode and SQLite sidecars regenerate locally. The exclusions do not remove .env or auth.json from the full backup.

Manual fallback (rsync): If you prefer to copy files directly, exclude the code repo:

rsync -av --exclude='tutou-agent' ~/.tutou/ newmachine:~/.tutou/

tip

tutou backup produces a consistent snapshot even while Tutou is actively running. The restored archive excludes machine-local runtime files like gateway.pid and cron.pid.

Permission denied when reloading shell after install

Scenario: After running the Tutou installer, source ~/.zshrc gives a permission denied error.

Cause: This usually happens when ~/.zshrc (or ~/.bashrc) has incorrect file permissions, or when the installer couldn't write to it cleanly. It's not a Tutou-specific issue — it's a shell config permissions problem.

Solution:

# Check permissions
ls -la ~/.zshrc

# Fix if needed (should be -rw-r--r-- or 644)
chmod 644 ~/.zshrc

# Then reload
source ~/.zshrc

# Or just open a new terminal window — it picks up PATH changes automatically

If the installer added the PATH line but permissions are wrong, you can add it manually:

echo 'export PATH="$HOME/.local/bin:$PATH"' >> ~/.zshrc

Error 400 on first agent run

Scenario: Setup completes fine, but the first chat attempt fails with HTTP 400.

Cause: Usually a model name mismatch — the configured model doesn't exist on your provider, or the API key doesn't have access to it.

Solution:

# Check what model and provider are configured
tutou config show | head -20

# Re-run model selection
tutou model

# Or test with a known-good model
tutou chat -q "hello" --model anthropic/claude-opus-4.7

If using OpenRouter, make sure your API key has credits. A 400 from OpenRouter often means the model requires a paid plan or the model ID has a typo.

Still Stuck?

If your issue isn't covered here:

Search existing issues: GitHub Issues
Ask the community: Nous Research Discord
File a bug report: Include your OS, Python version (python3 --version), Tutou version (tutou --version), and the full error message

Frequently Asked Questions​

What LLM providers work with Tutou?​

Does it work on Windows/Android/my platform??​

I run Tutou in WSL2. What's the best way to control my normal Windows Chrome?​

Is my data sent anywhere?​

Can I use it offline / with local models?​

How much does it cost?​

Can multiple people use one instance?​

What's the difference between memory and skills?​

Can I use it in my own Python project?​

Troubleshooting​

Installation Issues​

tutou: command not found after installation​

Unsupported Python version​

Terminal commands say node: command not found (or nvm, pyenv, asdf, …)​

uv: command not found​

Permission denied errors during install​

Provider & Model Issues​

The agent says "Tutou policy" or "Tutou guardrails" refused my request​

"…refused this request because of a policy on your account"​

"Could not open a stream to <host> after N attempts (request X KB)"​

Messaging replies: "interrupted mid-request" vs "not running or is unreachable" vs "could not reach"​

/model only shows one provider / can't switch providers​

API key not working​

Model not available / model not found​

Rate limiting (429 errors)​

Context length exceeded​

Terminal Issues​

Command blocked as dangerous​

sudo not working via messaging gateway​

Docker backend not connecting​

Messaging Issues​

Bot not responding to messages​

Messages not delivering​

Allowlist confusion — who can talk to the bot?​

Gateway won't start​

WSL: Gateway keeps disconnecting or tutou gateway start fails​

macOS: Node.js / ffmpeg / other tools not found by gateway​

Performance Issues​

Slow responses​

High token usage​

Session getting too long​

MCP Issues​

MCP server not connecting​

Tools not showing up from MCP server​

MCP timeout errors​

Skills Issues​

The Skills Hub page won't load in the desktop app (403 / blocked)​

Profiles​

How do profiles differ from just setting TUTOU_HOME?​

Can two profiles share the same bot token?​

Do profiles share memory or sessions?​

What happens when I run tutou update?​

How many profiles can I run?​

Workflows & Patterns​

Using different models for different tasks (multi-model workflows)​

Running multiple agents on one WhatsApp number (per-chat binding)​

Controlling what shows up in Telegram (hiding logs and reasoning)​

Managing skills on Telegram (slash command limit)​

Shared thread sessions (multiple users, one conversation)​

Exporting Tutou to another machine​

Moving a single profile to another machine​

tutou backup vs tutou profile export​

Permission denied when reloading shell after install​

Error 400 on first agent run​

Still Stuck?​

Frequently Asked Questions

What LLM providers work with Tutou?

Does it work on Windows/Android/my platform??

I run Tutou in WSL2. What's the best way to control my normal Windows Chrome?

Is my data sent anywhere?

Can I use it offline / with local models?

How much does it cost?

Can multiple people use one instance?

What's the difference between memory and skills?

Can I use it in my own Python project?

Troubleshooting

Installation Issues

`tutou: command not found` after installation

Unsupported Python version

Terminal commands say `node: command not found` (or `nvm`, `pyenv`, `asdf`, …)

`uv: command not found`

Permission denied errors during install

Provider & Model Issues

The agent says "Tutou policy" or "Tutou guardrails" refused my request

"…refused this request because of a policy on your account"

"Could not open a stream to `<host>` after N attempts (request X KB)"

Messaging replies: "interrupted mid-request" vs "not running or is unreachable" vs "could not reach"

`/model` only shows one provider / can't switch providers

API key not working

Model not available / model not found

Rate limiting (429 errors)

Context length exceeded

Terminal Issues

Command blocked as dangerous

`sudo` not working via messaging gateway

Docker backend not connecting

Messaging Issues

Bot not responding to messages

Messages not delivering

Allowlist confusion — who can talk to the bot?

Gateway won't start

WSL: Gateway keeps disconnecting or `tutou gateway start` fails

macOS: Node.js / ffmpeg / other tools not found by gateway

Performance Issues

Slow responses

High token usage

Session getting too long

MCP Issues

MCP server not connecting

Tools not showing up from MCP server

MCP timeout errors

Skills Issues

The Skills Hub page won't load in the desktop app (403 / blocked)

Profiles

How do profiles differ from just setting TUTOU_HOME?

Can two profiles share the same bot token?

Do profiles share memory or sessions?

What happens when I run `tutou update`?

How many profiles can I run?

Workflows & Patterns

Using different models for different tasks (multi-model workflows)

Running multiple agents on one WhatsApp number (per-chat binding)

Controlling what shows up in Telegram (hiding logs and reasoning)

Managing skills on Telegram (slash command limit)

Shared thread sessions (multiple users, one conversation)

Exporting Tutou to another machine

Moving a single profile to another machine

`tutou backup` vs `tutou profile export`

Permission denied when reloading shell after install

Error 400 on first agent run

Still Stuck?