twilio-customer-memory
GitHub提供Twilio Conversation Memory的集成指南，涵盖Memory Store创建、客户档案管理与特质维护、自动观察提取及语义召回。旨在赋予AI或人工客服跨会话的客户持久记忆能力。
Trigger Scenarios
Install
npx skills add fanfan-de/anybox --skill twilio-customer-memory -g -y
SKILL.md
Frontmatter
{
"name": "twilio-customer-memory",
"description": "Store and retrieve customer context using Twilio Conversation Memory. Covers Memory Store provisioning, profile management, traits, observations, conversation summaries, and semantic Recall. Use this skill to give AI agents or human agents persistent memory of customer interactions across sessions and channels."
}
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_SIDandTWILIO_AUTH_TOKEN— seetwilio-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:
| Mode | How | When to use |
|---|---|---|
| Conversation ID (recommended) | "conversationId": orchestrator_sid |
Active Conversation Orchestrator/Orchestrator conversation — query is generated from conversation context |
| Custom query | "query": "your question" |
Custom pipelines outside Conversation Orchestrator, or when you need precise control over relevance |
| No query | Omit both query and conversationId |
Returns most recent observations in chronological order — useful for loading history at session start |
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.
| Group | Fields | Use |
|---|---|---|
Contact |
phone, email, firstName, lastName | Identity anchor — always include |
Account |
accountNumber, tier, region | Business account data |
Support |
disposition, caseId, lastIssueType | Support history |
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 —
observationsLimitmax 20, default 5.summariesLimitandcommunicationsLimitsimilar. - 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
Version History
- 08dc189 Current 2026-07-05 19:08
