Customer Support Bot

Build a customer support bot that remembers customer history, past interactions, and preferences, so customers don’t repeat themselves and agents inherit full context on handoff.

Architecture

Per-customer memory: every message is attributed to the customer via sender_id; search by user_id spans all of that customer’s history.
Session per ticket: each ticket uses its own session_id, so a ticket extracts as a coherent unit while still rolling up under the customer.
Cross-ticket recall: search a customer’s memory to surface similar past issues, regardless of which ticket they happened in.

Setup

pip install everos-cloud
export EVEROS_API_KEY="your_api_key"

from everos_cloud import EverOS

client = EverOS()  # reads EVEROS_API_KEY

Store ticket messages

The customer’s messages carry their customer_id as sender_id; the bot’s replies use a bot sender id with role="assistant". A ticket maps to a session.

BOT_SENDER = "support_bot"

def store_message(customer_id: str, ticket_id: str, role: str, content: str):
    client.add(
        session_id=f"ticket_{ticket_id}",
        messages=[
            {
                "sender_id": customer_id if role == "user" else BOT_SENDER,
                "role": role,
                "content": content,
            }
        ],
    )

Gather context for a reply

Search the customer’s memory for relevant past episodes. Episodes are narrative summaries of prior interactions; include_profile=True also returns the consolidated customer profile once it has built up.

def gather_context(customer_id: str, query: str) -> str:
    results = client.search(
        query, user_id=customer_id, method="hybrid",
        top_k=5, include_profile=True,
    )
    parts = [f"[Profile] {p.profile_data}" for p in (results.profiles or [])]
    parts += [f"[Past issue] {e.episode}" for e in (results.episodes or [])]
    return "\n".join(parts) if parts else "No prior history for this customer."

Complete support bot

from everos_cloud import EverOS

client = EverOS()
BOT_SENDER = "support_bot"


class CustomerSupportBot:
    def _store(self, customer_id: str, ticket_id: str, role: str, content: str):
        client.add(
            session_id=f"ticket_{ticket_id}",
            messages=[{
                "sender_id": customer_id if role == "user" else BOT_SENDER,
                "role": role,
                "content": content,
            }],
        )

    def _context(self, customer_id: str, query: str) -> str:
        results = client.search(
            query, user_id=customer_id, method="hybrid",
            top_k=5, include_profile=True,
        )
        parts = [f"[Profile] {p.profile_data}" for p in (results.profiles or [])]
        parts += [f"[Past issue] {e.episode}" for e in (results.episodes or [])]
        return "\n".join(parts) if parts else "No prior history for this customer."

    def _generate(self, message: str, context: str) -> str:
        prompt = f"""You are a helpful support agent. Use the customer's history
to avoid asking them to repeat themselves. Reference past issues when relevant.

CUSTOMER CONTEXT:
{context}

CUSTOMER MESSAGE:
{message}"""
        # Replace with your LLM call.
        return "[LLM support reply grounded in the context above]"

    def handle_message(self, customer_id: str, ticket_id: str, message: str) -> str:
        # Recall context BEFORE storing, so we don't retrieve the current message
        context = self._context(customer_id, message)
        reply = self._generate(message, context)
        self._store(customer_id, ticket_id, "user", message)
        self._store(customer_id, ticket_id, "assistant", reply)
        return reply

    def close_ticket(self, ticket_id: str):
        # Force extraction of the ticket when it's resolved
        client.flush(f"ticket_{ticket_id}")

    def handoff_to_agent(self, customer_id: str, ticket_id: str, agent: str) -> dict:
        results = client.search(
            "issue summary and resolution attempts",
            user_id=customer_id, method="hybrid", top_k=10,
        )
        return {
            "ticket_id": ticket_id,
            "history": [e.summary for e in (results.episodes or [])],
            "handoff_note": f"Escalated to {agent}. Full context above.",
        }


bot = CustomerSupportBot()

print(bot.handle_message("customer_john", "T-2024-001",
    "I've been trying to reset my password but the email never arrives."))
print(bot.handle_message("customer_john", "T-2024-001",
    "I checked spam too. My email is john@example.com."))

bot.close_ticket("T-2024-001")
print(bot.handoff_to_agent("customer_john", "T-2024-001", "Sarah"))

Cross-ticket intelligence

Because search is scoped to the customer (not a single ticket), you can surface recurring issues across their entire history.

def find_recurring_issues(customer_id: str) -> list:
    results = client.search(
        "recurring issue, problem, error, unable to",
        user_id=customer_id, method="hybrid", top_k=20,
    )
    return results.episodes or []

history = find_recurring_issues("customer_john")
print(f"Found {len(history)} relevant past interactions")

Best practices

One session per ticket

Use a unique session_id per ticket so each extracts as a coherent unit, while user_id search still unifies the customer across tickets.

session_id = f"ticket_{ticket_id}"   # good: isolates each ticket

Recall before you store

Search for context before storing the incoming message, so retrieval reflects prior history rather than echoing the message you just received.

Privacy

Filter sensitive content out of episodes before putting them in an LLM prompt.

safe = [e for e in episodes
        if not any(t in (e.episode or "").lower()
                   for t in ("ssn", "credit card", "password"))]

Getting Started

Use Cases

Integration & Patterns

Customer Support Bot

Architecture

Setup

Store ticket messages

Gather context for a reply

Complete support bot

Cross-ticket intelligence

Best practices

Next steps

AI Tutor

Agentic retrieval

​Architecture

​Setup

​Store ticket messages

​Gather context for a reply

​Complete support bot

​Cross-ticket intelligence

​Best practices

​Next steps

AI Tutor

Agentic retrieval

Architecture

Setup

Store ticket messages

Gather context for a reply

Complete support bot

Cross-ticket intelligence

Best practices

Next steps