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How to Build an AI Astrology Chatbot with RoxyAPI and LLMs

•
10 min read
•By Aisha Okonkwo
astrologyAI ChatbotMCPLLM IntegrationTutorial

Build an AI-powered astrology chatbot using RoxyAPI with Claude, GPT, or any LLM. MCP integration, tool calling, and architecture guide.

How to Build an AI Astrology Chatbot with RoxyAPI and LLMs

The most successful astrology apps in 2026 are conversational. Users do not want to navigate dropdown menus and form fields to get a reading. They want to type "What does my birth chart say about my career?" and get a personalized, intelligent response.

Building this used to require a team of astrologers, NLP engineers, and backend developers. Now it requires an LLM and a good astrology API.

This guide shows you how to build an AI astrology chatbot that combines the conversational intelligence of large language models (Claude, GPT, Gemini) with the precise astrological calculations of RoxyAPI. The LLM handles the conversation. The API handles the math. The result is a chatbot that actually knows what it is talking about.

Architecture Overview

The architecture is straightforward:

  1. User asks a question in natural language ("What is my Moon sign?")
  2. LLM interprets the question and determines which API calls are needed
  3. RoxyAPI returns precise astrological calculations (planetary positions, birth chart data, tarot cards, numerology)
  4. LLM receives the structured data and generates a conversational, personalized response
  5. User sees an intelligent answer grounded in real calculations

This is called the "tool use" or "function calling" pattern. The LLM does not hallucinate astrology data. It calls an API for the facts and uses its language ability to present them naturally.

Why LLMs Alone Are Not Enough

You might wonder: why not just ask ChatGPT or Claude about astrology directly? Two reasons.

Accuracy. LLMs do not calculate planetary positions. They cannot tell you where Mars was at 3:47 AM on March 15, 1992. They will guess, and they will be wrong. Astrology requires precise astronomical calculations based on Swiss Ephemeris data. An API provides this. An LLM cannot.

Personalization. Generic astrology advice is worthless. "Scorpios are intense" tells the user nothing they do not already know. A real reading requires the user's exact birth date, time, and location to calculate their unique chart. The API takes these inputs and returns personalized data. The LLM then turns that data into a meaningful conversation.

The combination is powerful: API precision plus LLM communication.

Two Integration Approaches

Approach 1: MCP (Model Context Protocol)

MCP is the emerging standard for connecting AI agents to external tools. RoxyAPI ships with an MCP server, which means AI agents can discover and call astrology endpoints automatically.

How it works:

  1. Configure the RoxyAPI MCP server in your AI agent framework
  2. The agent automatically sees available astrology, tarot, numerology, I-Ching, and dream interpretation tools
  3. When a user asks a question, the agent decides which tool to call
  4. The agent calls the RoxyAPI endpoint through MCP
  5. The response flows back to the agent for interpretation

Best for: Claude-powered agents, multi-agent systems, frameworks that support MCP natively.

MCP is the cleanest integration path. The agent handles tool selection automatically. You write minimal glue code.

Approach 2: Function Calling / Tool Use

If your LLM provider supports function calling (OpenAI, Anthropic, Google all do), you can define RoxyAPI endpoints as callable tools.

How it works:

  1. Define each API endpoint as a function the LLM can call (name, parameters, description)
  2. When the user asks a question, the LLM decides which function to call
  3. Your backend executes the actual API call to RoxyAPI
  4. The response is passed back to the LLM as context
  5. The LLM generates a conversational response based on the data

Best for: OpenAI API users, custom agent frameworks, applications where you want explicit control over which tools are available.

Building the Chatbot Step by Step

Step 1: Define Your Tool Schema

Map RoxyAPI endpoints to tool definitions your LLM understands. Here are the core tools:

Birth Chart Tool

  • Name: get_birth_chart
  • Description: "Calculate a Western astrology birth chart showing planetary positions, signs, and houses for a given birth date, time, and location"
  • Parameters: date (YYYY-MM-DD), time (HH:MM), latitude (number), longitude (number), tz_offset (number)

Vedic Birth Chart Tool

  • Name: get_vedic_chart
  • Description: "Calculate a Vedic (Jyotish) birth chart showing planetary positions in sidereal zodiac with nakshatras, dashas, and house placements"
  • Parameters: date (YYYY-MM-DD), time (HH:MM), latitude (number), longitude (number), tz_offset (number)

Tarot Reading Tool

  • Name: get_tarot_reading
  • Description: "Draw tarot cards for a reading. Returns card names, positions, and interpretations"
  • Parameters: spread (string, e.g. "three-card", "celtic-cross")

Numerology Tool

  • Name: get_life_path
  • Description: "Calculate the numerology life path number from a birth date, revealing personality traits and life purpose"
  • Parameters: date (YYYY-MM-DD)

I-Ching Tool

  • Name: get_iching_reading
  • Description: "Consult the I-Ching (Book of Changes) for guidance. Returns a hexagram with judgment and interpretation"
  • Parameters: none (or optional question text)

Dream Interpretation Tool

  • Name: interpret_dream
  • Description: "Interpret dream symbols and themes. Returns psychological and symbolic analysis"
  • Parameters: symbols (array of strings)

Step 2: Build the Conversation Flow

A typical conversation looks like this:

User: "I was born on March 15, 1992 at 3:47 AM in Mumbai. What does my chart say about my career?"

LLM thinks: The user wants career insights from their birth chart. I need their Vedic chart (since they are from India, Vedic is likely preferred) focusing on the 10th house (career house in astrology).

LLM calls: get_vedic_chart(date="1992-03-15", time="03:47", latitude=19.076, longitude=72.877, tz_offset=5.5)

API returns: Structured JSON with all planetary positions, house placements, nakshatras, and dashas.

LLM generates: A personalized response about the user's career indicators based on their 10th house lord, planets in the 10th house, current dasha period, and relevant yogas.

The key insight: the LLM does not need to know astrology. It receives labeled data ("10th house lord: Saturn in Capricorn", "Current Mahadasha: Jupiter") and uses its language ability to explain what this means in the context of career.

Step 3: Handle Multi-Turn Conversations

Real users do not ask one question and leave. They follow up:

  • "What about my love life?"
  • "When is a good time to change jobs?"
  • "Pull a tarot card for guidance on this"

Your chatbot should:

  1. Remember birth details from the first message (do not ask again)
  2. Reuse the birth chart data (cache it for the session)
  3. Switch domains seamlessly (astrology to tarot to numerology) since RoxyAPI covers all of them with the same API key

This is where the multi-domain advantage matters most. A user who starts with a birth chart question, then asks for a tarot reading, then wants their life path number, gets a seamless experience. One API key handles all three domains.

Step 4: Add System Prompts

Guide your LLM with a system prompt that defines its personality and knowledge boundaries:

You are a knowledgeable astrology assistant. You provide personalized
readings based on real astrological calculations from the user's birth
chart.

Rules:
- Always use the API tools for astrological data. Never make up
  planetary positions or calculations.
- When the user provides birth details, call the birth chart tool
  before answering any astrology questions.
- Be warm and insightful but honest. If a chart shows challenges,
  acknowledge them while being constructive.
- You can provide readings from Western astrology, Vedic astrology,
  tarot, numerology, I-Ching, and dream interpretation.
- Always explain astrological concepts in plain language.
- Never claim to predict specific events. Frame insights as tendencies
  and influences.

Step 5: Deploy and Scale

The chatbot can be deployed as:

  • Web app with a chat interface
  • WhatsApp bot using the WhatsApp Business API
  • Telegram bot for community engagement
  • Discord bot for spiritual communities
  • Mobile app with a native chat UI
  • Embedded widget on astrology websites

Each deployment channel uses the same backend: LLM + RoxyAPI tool calls.

Performance and Cost Considerations

Latency: An API call to RoxyAPI takes milliseconds. The LLM response generation takes 1-3 seconds. Total response time is typically 2-4 seconds, which feels natural in a chat context.

Cost structure:

  • LLM costs: varies by provider (typically $0.01-0.05 per conversation turn)
  • API costs: RoxyAPI starts at $39/month for 5,000 requests
  • A typical conversation uses 2-5 API calls (birth chart + follow-up queries)
  • 5,000 API requests supports roughly 1,000-2,500 conversations per month

Caching: Birth charts never change for the same inputs. Cache them. A user's chart calculated once can be reused for every subsequent question in that session and future sessions, dramatically reducing API calls.

The Business Case

AI astrology chatbots have strong monetization potential:

  • Freemium: Offer basic readings free, charge for detailed analysis
  • Subscription: Monthly subscription for unlimited conversations
  • Per-reading: Charge per deep reading or compatibility analysis
  • Consultation upsell: Use the chatbot as a lead generator for human astrologer consultations

The top-grossing astrology apps generate millions in annual revenue. Co-Star, Nebula, and The Pattern have proven that users pay for personalized astrological insights. An AI chatbot can deliver this at scale without a team of human astrologers.

Frequently Asked Questions

Q: Do I need to know astrology to build an astrology chatbot? A: No. The API provides the astrological calculations and data. The LLM provides the conversational interpretation. You need to understand how to connect them (API integration and LLM tool calling) but not astrology itself.

Q: Which LLM works best for astrology chatbots? A: Claude and GPT-4 both excel at interpreting structured data and generating nuanced, empathetic responses. Claude tends to be more careful about not overpromising, which is good for astrology content. GPT-4 is widely available. Both support tool calling. Test with your specific use case.

Q: How is this different from just asking ChatGPT about astrology? A: ChatGPT cannot calculate your actual birth chart. It does not know where Mars was when you were born. It will make up planetary positions. An API-backed chatbot uses real astronomical calculations for the data and the LLM only for interpretation and conversation. The readings are personalized and accurate.

Q: What is MCP and why does it matter for astrology chatbots? A: MCP (Model Context Protocol) is the standard for connecting AI agents to external tools. RoxyAPI ships with an MCP server, meaning AI agents can automatically discover and call astrology endpoints without custom integration code. This simplifies building multi-tool agents significantly.

Q: Can the chatbot handle multiple spiritual domains in one conversation? A: Yes. Since RoxyAPI covers astrology, tarot, numerology, I-Ching, and dreams under one key, the chatbot can seamlessly switch between domains. A user can ask about their birth chart, request a tarot pull, and check their life path number all in the same conversation.

Q: How many concurrent users can this architecture support? A: The bottleneck is typically the LLM, not the API. RoxyAPI handles concurrent requests efficiently. With proper caching of birth chart data and a scalable LLM provider, this architecture supports thousands of concurrent users.

Start building your AI astrology chatbot today. Get your API key at roxyapi.com/pricing and explore the full API documentation.