Twilio Customer Memory

Overview

Conversation Memory gives your application persistent customer memory. Observations (what happened) and traits (who the customer is) are written automatically from conversations flowing through Conversation Orchestrator/Orchestrator — or posted directly if you run your own extraction. Retrieve relevant context via Recall before responding.

Conversation Orchestrator/Orchestrator conversation → auto-extracted observations & summaries → Memory Store
Your App → Recall → relevant context injected into LLM prompt

All Conversation Memory APIs are on memory.twilio.com. Observations, traits, profiles, summaries — everything is on the same host.

Auth: Basic Auth — TWILIO_ACCOUNT_SID and TWILIO_AUTH_TOKEN.

Prerequisites

• Twilio account with Conversation Memory access (requires enablement) — New to Twilio? See twilio-account-setup
• TWILIO_ACCOUNT_SID and TWILIO_AUTH_TOKEN — see twilio-iam-auth-setup
• Memory Store must be created before creating a Conversations Service in Conversation Orchestrator/Orchestrator — the store SID is required in the conversation config
• For conversation orchestration: twilio-conversation-orchestrator

Quickstart

Step 1 — Create a Memory Store

Do this before setting up Conversation Orchestrator/Orchestrator. The Memory Store SID goes into your conversation service config.

Python

import os, requests

account_sid = os.environ["TWILIO_ACCOUNT_SID"]
auth_token = os.environ["TWILIO_AUTH_TOKEN"]

store = requests.post(
    "https://memory.twilio.com/v1/Services",
    auth=(account_sid, auth_token),
    json={
        "uniqueName": "my-app-memory",
        "friendlyName": "My App Memory Store"
    }
).json()

memory_store_sid = store["sid"]
print(memory_store_sid)

Node.js

const accountSid = process.env.TWILIO_ACCOUNT_SID;
const authToken = process.env.TWILIO_AUTH_TOKEN;

const store = await fetch("https://memory.twilio.com/v1/Services", {
    method: "POST",
    headers: {
        "Authorization": "Basic " + btoa(`${accountSid}:${authToken}`),
        "Content-Type": "application/json",
    },
    body: JSON.stringify({
        uniqueName: "my-app-memory",
        friendlyName: "My App Memory Store",
    }),
}).then(r => r.json());

const memoryStoreSid = store.sid;

Use memory_store_sid when creating your Conversations Service in Conversation Orchestrator/Orchestrator. The two must be linked for automatic observation and summary extraction to work.

Step 2 — Profiles

Profiles are created automatically when conversations flow through Conversation Orchestrator/Orchestrator — the conversation config determines how participants are resolved into profiles. You can also create or enrich profiles manually using traits.

Create a profile manually with traits:

Python

profile = requests.post(
    f"https://memory.twilio.com/v1/Services/{memory_store_sid}/Profiles",
    auth=(account_sid, auth_token),
    json={
        "traits": {
            "Contact": {
                "phone": "+15558675310",
                "firstName": "Alyssa",
                "lastName": "Mock",
                "email": "alyssa@example.com"
            }
        }
    }
).json()

profile_id = profile["id"]

Node.js

const profile = await fetch(
    `https://memory.twilio.com/v1/Services/${memoryStoreSid}/Profiles`,
    {
        method: "POST",
        headers: {
            "Authorization": "Basic " + btoa(`${accountSid}:${authToken}`),
            "Content-Type": "application/json",
        },
        body: JSON.stringify({
            traits: {
                Contact: {
                    phone: "+15558675310",
                    firstName: "Alyssa",
                    lastName: "Mock",
                    email: "alyssa@example.com",
                }
            }
        }),
    }
).then(r => r.json());

const profileId = profile.id;

Look up a profile by phone number (for inbound calls where you only have the caller's number):

Python

lookup = requests.post(
    f"https://memory.twilio.com/v1/Services/{memory_store_sid}/Profiles/Lookup",
    auth=(account_sid, auth_token),
    json={"idType": "phone", "value": "+15558675310"}
).json()

profile_id = lookup["profiles"][0]["id"] if lookup.get("profiles") else None

Step 3 — Observations

Observations are extracted automatically from conversations when a conversation becomes inactive or is closed, based on your conversation config. You don't need to write them manually for Conversation Orchestrator-managed conversations.

If you run your own extraction (custom pipeline outside Conversation Orchestrator), post results directly:

Python

requests.post(
    f"https://memory.twilio.com/v1/Services/{memory_store_sid}/Profiles/{profile_id}/Observations",
    auth=(account_sid, auth_token),
    json={
        "observations": [
            {
                "content": "Customer asked about order #4521. Wants expedited shipping. Prefers SMS updates.",
                "source": "custom_extraction",
                "occurredAt": "2026-04-20T14:30:00Z",
                "conversationIds": [conversation_sid]
            }
        ]
    }
)

Node.js

await fetch(
    `https://memory.twilio.com/v1/Services/${memoryStoreSid}/Profiles/${profileId}/Observations`,
    {
        method: "POST",
        headers: {
            "Authorization": "Basic " + btoa(`${accountSid}:${authToken}`),
            "Content-Type": "application/json",
        },
        body: JSON.stringify({
            observations: [{
                content: "Customer asked about order #4521. Wants expedited shipping. Prefers SMS updates.",
                source: "custom_extraction",
                occurredAt: new Date().toISOString(),
                conversationIds: [conversationSid],
            }]
        }),
    }
);

Batch up to 10 observations in one request.

Step 4 — Recall Context Before Responding

Recall runs hybrid lexical + semantic search and returns the most relevant observations and summaries for an LLM prompt.

Recommended: pass a conversationId from Conversation Orchestrator/Orchestrator. Recall builds a contextually relevant query from the active conversation automatically — no need to craft one yourself.

Python

recall = requests.post(
    f"https://memory.twilio.com/v1/Services/{memory_store_sid}/Profiles/{profile_id}/Recall",
    auth=(account_sid, auth_token),
    json={
        "conversationId": orchestrator_conversation_sid,
        "observationsLimit": 10,
        "summariesLimit": 3,
    }
).json()

observations = "\n".join(o["content"] for o in recall.get("observations", []))
summaries = "\n".join(s["content"] for s in recall.get("summaries", []))

system_prompt = f"""You are a helpful support agent.

Customer history:
{observations}

Recent summaries:
{summaries}"""

Node.js

const recall = await fetch(
    `https://memory.twilio.com/v1/Services/${memoryStoreSid}/Profiles/${profileId}/Recall`,
    {
        method: "POST",
        headers: {
            "Authorization": "Basic " + btoa(`${accountSid}:${authToken}`),
            "Content-Type": "application/json",
        },
        body: JSON.stringify({
            conversationId: orchestratorConversationSid,
            observationsLimit: 10,
            summariesLimit: 3,
        }),
    }
).then(r => r.json());

const context = [
    ...recall.observations.map(o => o.content),
    ...recall.summaries.map(s => s.content),
].join("\n");

Other Recall modes:

Key Patterns

Trait Groups

Traits are organized into named groups. The Contact group is the standard identity anchor — its fields are promoted to profile identifiers for lookup.

Define your own groups for domain-specific data.

Summaries

Summaries are written automatically at conversation close or when a conversation goes inactive, based on your conversation config — the same trigger as observations. You can also write them manually:

Python

requests.post(
    f"https://memory.twilio.com/v1/Services/{memory_store_sid}/Profiles/{profile_id}/ConversationSummaries",
    auth=(account_sid, auth_token),
    json={
        "conversationId": conversation_sid,
        "content": "Customer called about order #4521. Resolved: approved expedited upgrade.",
        "source": "manual"
    }
)

Summaries are returned in the summaries array of Recall results.

Voice Agent Integration

Retrieve memory at call start, store observations at call end. For voice AI agents on ConversationRelay.

Python (WebSocket handler)

async def handle_call(websocket):
    setup = json.loads(await websocket.recv())
    caller = setup.get("from", "unknown")

    # Look up profile by caller phone
    lookup = requests.post(
        f"https://memory.twilio.com/v1/Services/{MEMORY_STORE_SID}/Profiles/Lookup",
        auth=(ACCOUNT_SID, AUTH_TOKEN),
        json={"idType": "phone", "value": caller}
    ).json()
    profiles = lookup.get("profiles", [])
    profile_id = profiles[0]["id"] if profiles else None

    context = ""
    if profile_id:
        recall = requests.post(
            f"https://memory.twilio.com/v1/Services/{MEMORY_STORE_SID}/Profiles/{profile_id}/Recall",
            auth=(ACCOUNT_SID, AUTH_TOKEN),
            json={"observationsLimit": 5, "summariesLimit": 2}
        ).json()
        context = "\n".join(o["content"] for o in recall.get("observations", []))

    system_prompt = f"You are a helpful agent.\n\nCustomer history:\n{context}" if context else "You are a helpful agent."

    # ... handle conversation ...

    # Store observation at end if running custom extraction
    if profile_id:
        requests.post(
            f"https://memory.twilio.com/v1/Services/{MEMORY_STORE_SID}/Profiles/{profile_id}/Observations",
            auth=(ACCOUNT_SID, AUTH_TOKEN),
            json={"observations": [{"content": call_summary, "source": "voice_agent", "conversationIds": [orchestrator_conversation_sid]}]}
        )

Multi-Tenant (ISV) Pattern

Use one Memory Store per client. The uniqueName doubles as a namespace.

# At client onboarding
store = requests.post(
    "https://memory.twilio.com/v1/Services",
    auth=(account_sid, auth_token),
    json={"uniqueName": f"client-{client_id}", "friendlyName": client_name}
).json()
# Store store["sid"] in your tenant config — pass it to Conversation Orchestrator conversation service setup

CANNOT

• Cannot create Conversation Orchestrator before Memory Store — Create Memory Store first. Its SID is required when creating the Conversations Service. Reversing this order breaks the linkage.
• Cannot extract observations mid-conversation — Automatic extraction happens on conversation close or inactive. For real-time writing, post directly to the Observations endpoint.
• Cannot read observations immediately after write — Eventual consistency. Allow ~2 seconds after write before querying Recall.
• Cannot exceed 15 Memory Stores per account — ISVs with more than 15 tenants should use sub-accounts
• Cannot detect misconfigured linkages — If Memory Store is not correctly linked in Conversation Orchestrator config, observations are silently not extracted. See twilio-debugging-observability.
• Cannot recover deleted profiles — Profile deletion is irreversible, permanent
• Cannot exceed 20 observations per Recall query — observationsLimit max 20, default 5. summariesLimit and communicationsLimit similar.
• Cannot batch more than 10 observations per request — Hard limit on batch writes

Next Steps

• Set up Conversation Orchestrator conversations: twilio-conversation-orchestrator
• Add real-time intelligence: twilio-conversation-intelligence
• Enterprise knowledge retrieval (scripts, offers, policies): twilio-enterprise-knowledge
• Voice agent setup: twilio-voice-conversation-relay
• Debug integration issues: twilio-debugging-observability