Frequently Asked Questions¶

General Questions¶

Q: What is llm-expose?¶

A: llm-expose is a CLI tool that exposes LLM-powered assistants through messaging platforms like Telegram and Discord. It provides a channel-first workflow where you configure providers, attach channels, control access through pairings, and optionally integrate MCP servers for tool-aware completions.

Q: What models are supported?¶

A: llm-expose uses LiteLLM as its provider, which supports 100+ models from providers including: - OpenAI (GPT-4, GPT-4o, GPT-3.5) - Anthropic (Claude) - Google (Gemini, PaLM) - Meta (Llama via Together AI) - Open-source models via local endpoints - And many more

Q: Can I use local models?¶

A: Yes! You can connect to any OpenAI-compatible endpoint, which includes: - Ollama (local LLMs) - vLLM - LocalAI - LM Studio - LlamaCPP with OpenAI server mode

Configure them as a provider with the endpoint URL.

Q: Is this production-ready?¶

A: llm-expose is actively maintained and used in production setups. However, it's still evolving. For production deployments, review the Deployment Guide and test thoroughly in your environment.

Installation & Setup¶

Q: Should I use interactive mode or headless mode?¶

A: Use interactive mode by default for manual setup. Most commands prompt for missing values automatically.

Use headless mode for automation/CI:

Add --no-input to disable prompts and fail fast on missing required values.
Add -y / --yes when a command can ask for confirmation (for example overwrite/start flows).

Q: I got "no matches found: .[dev]" error during dev install. How do I fix it?¶

A: On macOS/Linux with zsh, the glob expansion needs quoting:

pip install -e '.[dev]'

If using bash or other shells, this shouldn't be necessary, but it never hurts to quote it.

Q: Where is my configuration stored?¶

A: Configuration is stored in your platform's standard config directory: - macOS/Linux: ~/.config/llm-expose/ - Windows: %APPDATA%\llm-expose\

You can manually edit these files or use the CLI commands.

Q: How do I uninstall or reset everything?¶

A: To reset your configuration, remove the config directory:

rm -rf ~/.config/llm-expose/

To uninstall the package:

pip uninstall llm-expose

Q: Can I run multiple instances simultaneously?¶

A: Yes! Each channel has its own configuration and runtime. You can start multiple channels in separate terminal sessions or use process managers like systemd, supervisord, or Docker containers.

Channel Configuration¶

Q: What's the difference between Telegram and Discord channels?¶

A: Both are messaging platforms with different architectures:

Feature	Telegram	Discord
User ID Type	Chat ID (numeric)	User ID (numeric) or Server ID
Setup	Token-only (simpler)	Token + Server ID (more setup)
Scaling	Good for personal/small teams	Better for communities/servers
Rate Limits	Generous	Per-endpoint rate limits

See Channel Configuration for detailed setup.

Q: How do I find my Telegram Chat ID?¶

A: Multiple ways:

Using a bot URL: Message @userinfobot and it will reply with your Chat ID
Using the API: Send a message to your bot and check the payload
From the web client: Check the URL when you open a chat (contains the ID)

Q: How do I set up a Discord bot?¶

A: Follow these steps:

Go to Discord Developer Portal
Create → "New Application"
Go to "Bot" → "Add Bot"
Copy the token (this is your bot token)
Under "OAuth2" → "URL Generator", select bot and send_messages scopes
Use the generated URL to invite your bot to your server
Configure llm-expose with the token and server ID

Q: Can I use one model for multiple channels?¶

A: Yes! Models are global. Once you add a model, all channels can use it. This lets you balance costs or use different models per channel easily.

Pairing & Access Control¶

Q: How does the pairing model work?¶

A: Pairing is how llm-expose controls who can use the service:

When an unpaired user/chat sends a message, they get: This instance is not paired. Run llm-expose add pair <channel-id>
You explicitly add each allowed ID with: llm-expose add pair <id> --channel <channel-name>
Only paired IDs receive responses

This is useful for: - Private bots (control exactly who uses it) - Preventing abuse/costs - Multi-tenant setups

Q: Can I pair groups/servers vs individual users?¶

A: Yes! You can pair: - Individual users (Telegram Chat ID or Discord User ID) - Group chats (Telegram Group Chat ID or Discord Server/Channel ID)

The service will only serve paired IDs, regardless of type.

Q: What if I want unrestricted access?¶

A: Pair a wildcard or all relevant IDs manually. There's no "allow all" setting by design—pairing is explicit for security and cost management.

MCP Integration¶

Q: What is MCP and why would I use it?¶

A: MCP (Model Context Protocol) is a standard for connecting tools to LLMs. Integration lets your assistant: - Call tools (fetch URLs, query APIs, read files) - Have tool-aware responses - Execute multi-step workflows

Example: Your Discord assistant can summarize URLs, query databases, or run server commands.

Q: Which MCP servers are supported?¶

A: llm-expose supports any MCP server that implements the protocol. Popular examples:

filesystem: Read/write files and directories
postgres: Query and modify databases
memory: Persistent conversation memory
git: Repository operations
web: URL fetching and scraping
slack: Interact with Slack workspaces
github: Repository and issue management

See MCP Integration Guide for setup details.

Q: Can I write custom MCP servers?¶

A: Absolutely! You can create custom MCP servers in Python, JavaScript, or any language. The protocol is language-agnostic. See the official MCP documentation.

Q: What happens if an MCP tool fails?¶

A: The LLM receives the error message and can decide how to handle it. Common behaviors: - Retry with different parameters - Inform the user - Fall back to a different approach

Providers & Models¶

Q: How do I add a new model provider?¶

A: Use the CLI command:

llm-expose add model --name my-model --provider openai --model-id gpt-4o

Supported provider options depend on LiteLLM. See Provider Configuration.

Q: Can I use environment variables for API keys?¶

A: Yes! Set your API keys as environment variables (e.g., OPENAI_API_KEY) and llm-expose will discover them automatically via LiteLLM.

Q: What if my API key is invalid?¶

A: You'll see an error when starting the channel. Check:

The key is set correctly in environment or configuration
The provider endpoint is correct
The account has available API quota
Network connectivity to the provider

See Troubleshooting for debug steps.

Q: How do I switch models without restarting?¶

A: Models are configured per-channel. To switch:

Stop the running instance (Ctrl+C)
Update the channel config: llm-expose config set --channel <name> --model-id <new-model>
Restart the channel

You can also maintain multiple channel configs with different models.

Deployment¶

Q: Can I deploy llm-expose to the cloud?¶

A: Yes! Popular options:

Docker: Containerize with Docker (see Deployment Guide)
Heroku/Railway: Deploy as a worker/service
AWS/GCP/Azure: Run on EC2, Cloud Run, App Service, etc.
VPS: Run on any Linux server (DigitalOcean, Linode, etc.)
Raspberry Pi: Great for local deployments

The key is keeping your API keys secure (use environment variables) and ensuring the server can reach Telegram/Discord APIs.

Q: How do I keep llm-expose running 24/7?¶

A: Use a process manager:

systemd (Linux): Native service management
supervisord: Cross-platform process supervision
Docker: Container orchestration (Docker Compose, Kubernetes)
PM2 (Node.js): JavaScript process manager
systemd-timers or cron: For scheduled tasks

See Deployment Guide for examples.

Q: What are typical costs?¶

A: Costs depend on:

LLM API: OpenAI GPT-4o (~$0.01-0.03 per 1K tokens)
Infrastructure: Minimal (cheap VPS or container)
Bandwidth: Usually free

Expected cost for light usage: $10-50/month.

Troubleshooting & Common Issues¶

Q: My bot doesn't respond to messages. What should I do?¶

A: Start with debugging steps:

Check the channel is running: llm-expose start --channel <name>
Verify the user/chat is paired: llm-expose list pairs --channel <name>
Check logs for errors (usually printed to console)
Verify API key is valid

For more details, see the Troubleshooting Guide.

Q: How do I check logs?¶

A: Logs are printed to the terminal by default. For persistent logging, redirect output:

llm-expose start --channel my-channel > bot.log 2>&1 &

For production, use systemd journalctl or your container logs.

Q: Can I run multiple models in parallel?¶

A: Each channel has one model. To run multiple models, create multiple channels (each with different models) and start them independently.

Contributing & Community¶

Q: How can I contribute?¶

A: We welcome contributions! See Contributing Guide for:

How to set up the development environment
Code style and testing requirements
How to submit PRs
Reporting bugs and requesting features

Q: Where can I report bugs?¶

A: Open an issue on GitHub Issues. Include:

What you were trying to do
Error message/logs
Python version and OS
Steps to reproduce

Q: Is there a community or support channel?¶

A: Not yet, but you can:

Open GitHub Discussions for questions
Report issues on GitHub Issues
Email or reach out via GitHub profile

Didn't find your question?¶

If your question isn't here, please:

Check the Troubleshooting Guide for known issues
Review specific guides: Channel Config, MCP Integration, Deployment
Open a GitHub Discussion or Issue

We're here to help!