Agent Templates
CRM AI Agent

Post-call CRM agents that update records on-behalf-of your users

Connect four real services, delegate OAuth to your users, and ship a working agent in minutes. Clone the sample, swap in your tools, and go from zero to multi-connector in a single afternoon.

CRM AI Agent
Sample Agent for Acme
May 22 · 10:00 AM ·
47s
Update CRM from today's Acme Corp call
J
Reading Granola transcript
Fetch call transcript
granola_get_transcript
Update HubSpot deal
hubspot_update_deal
Draft follow-up email
gmail_create_draft
Post-call update: Acme Corp
HubSpot deal updated (as rep, not bot)
"Stage: Proposal → Negotiation"
Action: Send security review doc by Friday
Result
HubSpot updated with rep as author, follow-up draft saved
Slack summary posted to #deal-desk
Message Claude...
Trusted by teams shipping agents to production
CRM AI Agent
Sample Agent for Acme
May 22 · 10:00 AM ·
47s
Update CRM from today's Acme Corp call
J
Reading Granola transcript
Fetch call transcript
granola_get_transcript
Update HubSpot deal
hubspot_update_deal
Draft follow-up email
gmail_create_draft
Post-call update: Acme Corp
HubSpot deal updated (as rep, not bot)
"Stage: Proposal → Negotiation"
Action: Send security review doc by Friday
Result
HubSpot updated with rep as author, follow-up draft saved
Slack summary posted to #deal-desk
Message Claude...

How the agent goes from call transcript to CRM update in five steps

A real working agent you can deploy

This repo uses a single SCALEKIT_USER_ID env var to simulate one user. In production, pass each user's real ID as the identifier on every Scalekit call, and send them an authorization link whenever their connector status is not ACTIVE. HubSpot's 30-minute token expiry is handled automatically.

01
Authorize, then orchestrate
main.py
Every connection is authorized once via a one-time link. Scalekit refreshes tokens for you, including HubSpot's 30-minute expiry. main.py triggers on a completed Granola transcript, verifies all four connectors are ACTIVE, then runs the post-call pipeline in sequence.
main.py
import os
from scalekit import ScalekitClient
from langchain.agents import create_tool_calling_agent

client = ScalekitClient(
    env_url=os.environ["SCALEKIT_ENV_URL"],
    client_id=os.environ["SCALEKIT_CLIENT_ID"],
    client_secret=os.environ["SCALEKIT_CLIENT_SECRET"],
)

# LangChain-compatible tools scoped to this user
tools = client.actions.langchain.get_tools(
    identifier="user@example.com",
    connection_names=["granolamcp", "hubspot", "gmail", "slack"],
)

agent = create_tool_calling_agent(llm, tools, prompt)
02
Read call transcript from Granola
read_transcript.py
03
Extract deal data with LLM
extract_data.py
04
Update HubSpot deal records
update_hubspot.py
05
Draft follow-up email and notify Slack
deliver_outputs.py
Why choose Scalekit

Delegated identity. Not service accounts.

Credentials never touch agent code or LLM context. The agent acts as the user, not as a shared bot.
Delegated OAuth - Agent reads your calendar, your inbox — scoped to the authorizing identity, not org-wide.
Credentials outside agent runtime  -  Tokens never touch agent code or LLM context. Both failure modes covered.
Token lifecycle automatic  -  Refresh, expiry, rotation across all connectors. One SDK call. Zero management code.
200+ prebuilt connectors  -  Google, Slack, HubSpot, GitHub, Jira, Notion, Salesforce — same auth pattern everywhere.

Agents that update your CRM, without the auth plumbing

Four things you'd otherwise build: Granola MCP auth, HubSpot OAuth with 30-minute refresh, Gmail credentials, Slack auth. Handled.

OAuth flow per connector
One SDK call returns a delegated token for any connector. Google, HubSpot, Slack, same pattern across all 200+ connectors
tools = client.actions.langchain.get_tools(
    identifier=user_id,
    connection_names=["granolamcp", "hubspot", "gmail"],
)
agent = create_tool_calling_agent(llm, tools, prompt)
Secure token vault  
Scalekit stores OAuth credentials outside agent code and outside LLM context. Both are separate failure modes. Both covered
client = ScalekitClient(
    env_url=os.environ["SCALEKIT_ENV_URL"],
    client_id=os.environ["SCALEKIT_CLIENT_ID"],
    client_secret=os.environ["SCALEKIT_CLIENT_SECRET"],
)
# Credentials never in agent code or LLM context
Token refresh logic
Token lifecycle handled automatically — expiry, rotation, re-auth — across every connector. Agent runs in 6 months. Same call works
# Day 1 or day 180 — same call works
tools = client.actions.langchain.get_tools(
    identifier=user_id,
    connection_names=["granolamcp", "hubspot", "gmail"],
)
Try other Agent Templates

Prebuilt agents you can ship today

Each one runs on delegated identity, scoped per user.

SALES
Sales call prep agent
Pull Granola notes and Attio contact history to draft a pre-call brief before every sales meeting. Zero rep input.
SALES
Deal intelligence agent
Combine Gong, Attio, and Slack signals to surface deal risks and next-best actions. Updated after every call.
SALES
Outbound prospecting agent
Build targeted prospect lists with Apollo, enrich with firmographic data, and draft personalised outreach. Runs on a schedule.
GTM
HubSpot to Slack updates agent
Watch HubSpot deal stage changes and post structured updates to the right Slack channel. Reps stop checking the CRM all day.
ENGINEERING
Engineering standup agent
Aggregate GitHub and GitLab activity, link to Jira, and post a daily standup digest to Slack. No async updates.
OPS
Email-to-calendar scheduling agent
Parse scheduling intent from Gmail threads and create Google Calendar events with the right attendees and timezone.
Customize the sample

Clone it and own it with connectors you choose

Don't sweat the integration. Point a coding agent at the repo. It clones, swaps in your connectors, and adds new steps for you.

1
Install a coding agent
terminal

claude "Set up a post-call CRM agent using Granola MCP, HubSpot, Gmail, and Slack via Scalekit"

terminal

codex "Set up a post-call CRM agent using Granola MCP, HubSpot, Gmail, and Slack via Scalekit"

terminal

gh copilot suggest "Set up a post-call CRM agent using Granola MCP, HubSpot, Gmail, and Slack via Scalekit"

terminal

Open Cursor Composer (Cmd+Shift+I) Paste the prompt from the Prompt tab

terminal

npx skills add scalekit-inc/skills --skill setup-scalekit

2
Give it this prompt

Clone github.com/scalekit-inc/python-connect-demos/langchain. Set connection_names = ["granolamcp", "hubspot", "gmail", "slack"]. Build a post-call CRM pipeline: read Granola transcript, extract deal signals with the LLM, update HubSpot deal stage and notes as the rep (Scalekit handles 30-minute token refresh), save a follow-up draft to Gmail, post summary to Slack. Set SCALEKIT_ENV_URL, SCALEKIT_CLIENT_ID, SCALEKIT_CLIENT_SECRET in .env.

Build your own
multi-connector agent

Add connectors. Change the LLM. Same delegated auth pattern.