Telegram Fitness Bot with Custom Workout Plans
Sends a photo or text to the bot and receives a personalized 7-day workout plan formatted for Telegram.
What it does
Deliver personalized 7-day workout plans directly in Telegram by analyzing user photos or text inputs. The bot identifies body type and goals to generate tailored fitness routines, handling message formatting automatically to ensure seamless delivery without manual intervention.
Step by step
- Set up a Telegram Bot via BotFather to obtain the authentication token.
- Configure the AI Agent to route incoming messages from the Telegram trigger to the Google Gemini Chat Model.
- Implement simple memory storage using a key-value store like Redis or a local JSON file to track user session data across turns.
Where the LLM does the work
- Prompt engineering for the fitness coach persona to ensure tone is encouraging and advice is safe.
- Designing the system prompt to parse natural language goals into structured workout parameters.
- Generating specific exercise descriptions that adapt based on the inferred body type or user constraints.
Watch out for
Tools that fit
The agent brief
You are helping me build the following AI agent workflow. ## Goal Telegram Fitness Bot with Custom Workout Plans: Sends a photo or text to the bot and receives a personalized 7-day workout plan formatted for Telegram. ## Specification - What it does: Deliver personalized 7-day workout plans directly in Telegram by analyzing user photos or text inputs. The bot identifies body type and goals to generate tailored fitness routines, handling message formatting automatically to ensure seamless delivery without manual intervention. - Trigger: Triggered by an event (Manual · on user message or photo) - Autonomy: Fully hands-off - Expected setup effort: about an afternoon - Tools/services involved: - Telegram: User interface and messaging delivery - Google Gemini Chat Model: Image analysis and workout plan generation - AI Agent: Orchestrating the conversational flow and logic - Simple Memory: Maintaining context during user interactions ## Known pitfalls, handle each one explicitly in your implementation 1. Image analysis via text-only models will fail, so ensure you use a multimodal model like Gemini Pro Vision for photo inputs. 2. Liability risks arise from giving medical advice, add a disclaimer in every response stating the bot is not a doctor. 3. Context window limits can cause memory loss during long conversations, implement a sliding window or summary mechanism to keep history relevant. 4. Telegram message length limits are 4096 characters, so truncate long workout plans into multiple messages if necessary. ## Reference implementation https://n8n.io/workflows/6697 (workflow template) Fetch and inspect this before building. If it matches my stack, adapt it; if not, rebuild the pattern with my tools. ## Process requirements 1. Before building: ask me which of the listed tools I actually use and what my platform is (n8n / Make / code / other). Do not assume. 2. Adapt the pattern to my answers; do not force the reference stack. 3. Address every pitfall above; tell me how you handled each. 4. Provide a test plan I can run before letting this touch real data. 5. Ask before any step that sends messages, modifies data, or spends money. Source: https://usecasesforagents.com/use-case/telegram-fitness-bot-with-custom-workout-plans/ via usecasesforagents.com
Frequently asked questions
Can I use a different Messaging than Telegram?
Yes. Telegram is only the example messaging in this recipe. The same flow works with ClickSend API, Discord, Google Chat and LINE Messaging API. Swap the messaging connection and keep the rest of the setup as written.
Want this running in your business?
Who it's for
Built for freelancers and consultants juggling this across every client, not just one.
Developers can adapt this to their own repo or ticket queue with the same trigger and tools.