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Documentation Index

Fetch the complete documentation index at: https://docs.animam.ai/llms.txt

Use this file to discover all available pages before exploring further.

Animam provides a conversational interface between your website and any LLM. Instead of scraping HTML, AI agents can query your chatbot directly and get accurate, author-intended responses.

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Why This Matters

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The Scraping Problem

When Perplexity, ChatGPT, or any AI agent wants information about your business:
  1. They crawl your website periodically
  2. Parse HTML structures that change
  3. Extract text without understanding context
  4. Generate responses that may be outdated or wrong
You have no control over what they say about you.

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The Animam Solution

With Animam, AI agents can:
  1. Query your API - POST /chat/:slug with a question
  2. Get your answer - The response you control, in real-time
  3. Use structured context - Your corpus, your persona, your intent
┌──────────────────────────────────────────────────────────────┐
│                                                              │
│   Your Site  ←──── ANIMAM ────→  All LLMs                   │
│                                                              │
│   • Corpus                        • Claude                   │
│   • Segments                      • ChatGPT                  │
│   • Persona                       • Perplexity               │
│   • Intent                        • Custom agents            │
│                                                              │
└──────────────────────────────────────────────────────────────┘

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Two Ways to Connect

REST API

Any LLM can query your chatbot via HTTP. Simple POST request, JSON response.

MCP Server

Claude Desktop and MCP clients connect directly. Native integration.

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LLM Context Discovery

Animam exposes a /context/:slug endpoint that provides structured information about your chatbot: 
curl https://api.animam.ai/context/your-slug

{
  "tenant": {
    "name": "Your Company",
    "description": "What you do",
    "botName": "Alice",
    "botTitle": "Customer Support"
  },
  "capabilities": ["faq", "contact-form", "product-info"],
  "segments": ["default", "pricing", "support"],
  "endpoints": {
    "chat": "POST https://api.animam.ai/chat/your-slug",
    "mcp": "npx @animam/mcp"
  }
}
This allows AI agents to:
  • Discover what your chatbot can do
  • Choose the right segment for their query
  • Understand the persona they’re talking to

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Use Cases

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Perplexity / Search Agents

Instead of scraping your pricing page, Perplexity can query: 
POST /chat/your-slug
{"message": "What are your pricing plans?"}
And get your exact, up-to-date pricing.

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Claude Desktop / AI Assistants

Your team can use Claude Desktop with your business context: 
"Connect to the your-slug chatbot and answer customer questions"
Claude loads your corpus and becomes your support agent.

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Custom Integrations

Build agents that query multiple Animam chatbots: 
import requests

def ask_vendor(slug, question):
    response = requests.post(
        f"https://api.animam.ai/chat/{slug}",
        json={"message": question}
    )
    return response.json()["reply"]

# Compare vendors
acme_price = ask_vendor("acme-corp", "What's your enterprise pricing?")
beta_price = ask_vendor("beta-inc", "What's your enterprise pricing?")

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Next Steps

API Access

How to query chatbots via REST API

MCP Universal

Connect Claude Desktop to any chatbot