PhantomBuster

Connect to PhantomBuster to automate web scraping and data extraction workflows. Launch, monitor, and manage automation agents that extract data from LinkedIn, Instagram, Twitter, and hundreds of other platforms.

Supports authentication: API Key

Set up the agent connector

Register your PhantomBuster API key with Scalekit so it can authenticate and proxy automation requests on behalf of your users. PhantomBuster uses API key authentication — there is no redirect URI or OAuth flow.

1. Get your PhantomBuster API key

   • Sign in to phantombuster.com and go to Settings → API in the left sidebar.

   • Your API key is displayed on this page. Click the copy icon to copy it. If you need a new key, click Regenerate.

   

   Keep your API key secret

   Your PhantomBuster API key grants full access to your organization — including launching agents, reading lead data, and managing billing. Never expose it in client-side code or commit it to source control.

   Plan requirements

   PhantomBuster tools require different subscription tiers. Review the table below before choosing a plan at phantombuster.com/pricing:

   Plan	Included resources	Notes
   Trial	2 hrs execution time/month, 1 agent	Core agent and container tools only
   Starter	20 hrs/month, up to 5 agents	Leads, lists, basic org tools
   Pro	80 hrs/month, up to 15 agents	CRM integration, AI credits, SERP credits
   Team	300 hrs/month, unlimited agents	Custom scripts, branches, full org export
   Business	Custom	SSO, dedicated support, custom limits

   Specific tool requirements:

   • phantombuster_ai_completions — requires AI credits (included in Pro+, or purchasable add-on)
   • phantombuster_org_save_crm_contact — requires a HubSpot CRM integration configured in org settings (Pro+)
   • phantombuster_branch_* — requires custom script access (Team+)
   • phantombuster_org_export_* — requires Pro+ for full date ranges (up to 6 months)

2. Create a connection in Scalekit

   • In Scalekit dashboard, go to Agent Auth → Create Connection. Find PhantomBuster and click Create.

   • Note the Connection name — you will use this as connection_name in your code (e.g., phantombuster).

   

3. Add a connected account

   Connected accounts link a specific user identifier in your system to a PhantomBuster API key. Add accounts via the dashboard for testing, or via the Scalekit API in production.

   Via dashboard (for testing)

   • Open the connection you created and click the Connected Accounts tab → Add account.

   • Fill in:

     • Your User’s ID — a unique identifier for this user in your system (e.g., user_123)
     • API Key — the PhantomBuster API key you copied in step 1

   • Click Save.

   

   Via API (for production)

   Node.js

   await scalekit.actions.upsertConnectedAccount({
     connectionName: 'phantombuster',
     identifier: 'user_123',
     credentials: { api_key: 'your-phantombuster-api-key' },
   });

   Python

   scalekit_client.actions.upsert_connected_account(
       connection_name="phantombuster",
       identifier="user_123",
       credentials={"api_key": "your-phantombuster-api-key"}
   )

   Production usage tip

   In production, call upsertConnectedAccount when a user connects their PhantomBuster account — for example, after they paste their API key into a settings page in your app.

Rate limits

PhantomBuster enforces per-plan API rate limits. Exceeding them returns 429 Too Many Requests. Monitor your execution time and resource usage at phantombuster.com → Dashboard → Usage.

Usage

Once a connected account is set up, call the PhantomBuster API through the Scalekit proxy. Scalekit injects the API key as the X-Phantombuster-Key header automatically — you never handle credentials in your application code.

Proxy API calls

Node.js

import { ScalekitClient } from '@scalekit-sdk/node';
import 'dotenv/config';

const connectionName = 'phantombuster'; // connection name from your Scalekit dashboard
const identifier = 'user_123';          // your user's unique identifier

// Get your credentials from app.scalekit.com → Developers → Settings → API Credentials
const scalekit = new ScalekitClient(
  process.env.SCALEKIT_ENV_URL,
  process.env.SCALEKIT_CLIENT_ID,
  process.env.SCALEKIT_CLIENT_SECRET
);
const actions = scalekit.actions;

// List all agents via Scalekit proxy — no API key needed here
const result = await actions.request({
  connectionName,
  identifier,
  path: '/api/v2/agents',
  method: 'GET',
});
console.log(result.data);

Python

import scalekit.client, os
from dotenv import load_dotenv
load_dotenv()

connection_name = "phantombuster"  # connection name from your Scalekit dashboard
identifier = "user_123"            # your user's unique identifier

# Get your credentials from app.scalekit.com → Developers → Settings → API Credentials
scalekit_client = scalekit.client.ScalekitClient(
    client_id=os.getenv("SCALEKIT_CLIENT_ID"),
    client_secret=os.getenv("SCALEKIT_CLIENT_SECRET"),
    env_url=os.getenv("SCALEKIT_ENV_URL"),
)
actions = scalekit_client.actions

# List all agents via Scalekit proxy — no API key needed here
result = actions.request(
    connection_name=connection_name,
    identifier=identifier,
    path="/api/v2/agents",
    method="GET"
)
print(result)

No OAuth flow needed

PhantomBuster uses API key auth — unlike OAuth connectors, there is no authorization link or redirect flow. Once you call upsertConnectedAccount (or add an account via the dashboard), your users can make requests immediately.

Scalekit tools

Use execute_tool to call PhantomBuster tools directly from your code. Scalekit resolves the connected account, injects the API key, and returns a structured response — no raw HTTP or credential management needed.

Launch an agent and retrieve results

The most common PhantomBuster workflow: launch an automation agent, stream its console output while it runs, then read the final result.

examples/phantombuster_launch.py

import scalekit.client, os, time
from dotenv import load_dotenv
load_dotenv()

scalekit_client = scalekit.client.ScalekitClient(
    client_id=os.getenv("SCALEKIT_CLIENT_ID"),
    client_secret=os.getenv("SCALEKIT_CLIENT_SECRET"),
    env_url=os.getenv("SCALEKIT_ENV_URL"),
)
actions = scalekit_client.actions

connection_name = "phantombuster"
identifier = "user_123"

# Step 1: Resolve the connected account for this user
response = actions.get_or_create_connected_account(
    connection_name=connection_name,
    identifier=identifier
)
connected_account = response.connected_account

# Step 2: Find the agent you want to run
agents = actions.execute_tool(
    tool_name="phantombuster_agents_fetch_all",
    connected_account_id=connected_account.id,
    tool_input={}
)
agent_id = agents.result[0]["id"]  # pick the first agent, or filter by name

# Step 3: Launch the agent
launch_result = actions.execute_tool(
    tool_name="phantombuster_agent_launch",
    connected_account_id=connected_account.id,
    tool_input={
        "id": agent_id,
        "output": "result-object",
    }
)
container_id = launch_result.result["containerId"]
print(f"Agent launched. Container ID: {container_id}")

# Step 4: Poll for output until the agent finishes
output_pos = 0
while True:
    output = actions.execute_tool(
        tool_name="phantombuster_container_fetch_output",
        connected_account_id=connected_account.id,
        tool_input={"id": container_id, "fromOutputPos": output_pos}
    )
    print(output.result.get("output", ""), end="", flush=True)
    output_pos = output.result.get("nextOutputPos", output_pos)
    if output.result.get("status") in ("finished", "error"):
        break
    time.sleep(3)  # poll every 3 seconds

# Step 5: Fetch the structured result
final_result = actions.execute_tool(
    tool_name="phantombuster_container_fetch_result",
    connected_account_id=connected_account.id,
    tool_input={"id": container_id}
)
print("Scraped profiles:", final_result.result)

Save scraped profiles as leads

After a scraping run, bulk-save extracted profiles to a PhantomBuster lead list for downstream CRM enrichment or outreach.

examples/phantombuster_save_leads.py

# First: fetch available lead lists (or create one in the PhantomBuster dashboard)
lists = actions.execute_tool(
    tool_name="phantombuster_lists_fetch_all",
    connected_account_id=connected_account.id,
    tool_input={}
)
list_id = lists.result[0]["id"]  # use the first list, or filter by name

# Bulk-save up to 1,000 profiles in one call — more efficient than looping
actions.execute_tool(
    tool_name="phantombuster_leads_save_many",
    connected_account_id=connected_account.id,
    tool_input={
        "listId": list_id,
        "leads": [
            {
                "firstName": p.get("firstName"),
                "lastName": p.get("lastName"),
                "email": p.get("email"),
                "linkedinUrl": p.get("linkedinUrl"),
                "company": p.get("company"),
                "jobTitle": p.get("title"),
                "additionalFields": {
                    "source": "phantombuster-scraper",
                    "agentId": agent_id,
                    "containerId": container_id,
                },
            }
            for p in final_result.result
        ],
    }
)
print(f"{len(final_result.result)} leads saved to list {list_id}.")

Check resource usage before running agents

Avoid quota errors by verifying execution time and credit balances before launching a large scraping run.

examples/phantombuster_check_resources.py

resources = actions.execute_tool(
    tool_name="phantombuster_org_fetch_resources",
    connected_account_id=connected_account.id,
    tool_input={}
)

exec_time = resources.result.get("executionTime", {})
ai_credits = resources.result.get("aiCredits", {})

if exec_time.get("remaining", 0) < 30:
    raise RuntimeError(
        f"Insufficient execution time: {exec_time.get('remaining')} min remaining. "
        "Upgrade at phantombuster.com/pricing or wait for your plan to reset."
    )

print(f"Execution time: {exec_time['remaining']} min remaining ({exec_time.get('used')} used)")
print(f"AI credits: {ai_credits.get('remaining', 'N/A')}")

Run AI completions on scraped data

Use PhantomBuster’s AI service to extract structured data from raw agent output — such as parsing job titles into seniority levels or extracting skills from profile summaries.

Requires AI credits

phantombuster_ai_completions consumes AI credits from your plan. Monitor usage at PhantomBuster dashboard → Usage. AI credits are included in Pro+ plans and available as an add-on.

examples/phantombuster_ai_enrichment.py

# Extract structured data from a raw LinkedIn profile headline
completion = actions.execute_tool(
    tool_name="phantombuster_ai_completions",
    connected_account_id=connected_account.id,
    tool_input={
        "model": "gpt-4o",
        "messages": [
            {
                "role": "system",
                "content": "Extract the seniority level and primary skill from this LinkedIn headline. Return JSON only.",
            },
            {
                "role": "user",
                "content": "Senior Software Engineer at Acme Corp | React, TypeScript, GraphQL",
            },
        ],
        "responseSchema": {
            "type": "object",
            "properties": {
                "seniority": {"type": "string", "enum": ["junior", "mid", "senior", "lead", "exec"]},
                "primarySkill": {"type": "string"},
            },
            "required": ["seniority", "primarySkill"],
        },
    }
)
print("Parsed profile:", completion.result)
# → {'seniority': 'senior', 'primarySkill': 'React'}

LangChain integration

Let an LLM decide which PhantomBuster tool to call based on natural language. This example builds an agent that can manage automation runs and leads in response to user input.

examples/phantombuster_langchain.py

import scalekit.client, os
from dotenv import load_dotenv
from langchain_openai import ChatOpenAI
from langchain.agents import AgentExecutor, create_openai_tools_agent
from langchain_core.prompts import (
    ChatPromptTemplate, SystemMessagePromptTemplate,
    HumanMessagePromptTemplate, MessagesPlaceholder, PromptTemplate
)
load_dotenv()

scalekit_client = scalekit.client.ScalekitClient(
    client_id=os.getenv("SCALEKIT_CLIENT_ID"),
    client_secret=os.getenv("SCALEKIT_CLIENT_SECRET"),
    env_url=os.getenv("SCALEKIT_ENV_URL"),
)
actions = scalekit_client.actions

identifier = "user_123"

# Resolve connected account (API key auth — no OAuth redirect needed)
actions.get_or_create_connected_account(
    connection_name="phantombuster",
    identifier=identifier
)

# Load all PhantomBuster tools in LangChain format
tools = actions.langchain.get_tools(
    identifier=identifier,
    providers=["PHANTOMBUSTER"],
    page_size=100
)

prompt = ChatPromptTemplate.from_messages([
    SystemMessagePromptTemplate(prompt=PromptTemplate(
        input_variables=[],
        template=(
            "You are a PhantomBuster automation assistant. "
            "Use the available tools to manage agents, check resource usage, "
            "manage leads, and analyse automation run results."
        )
    )),
    MessagesPlaceholder(variable_name="chat_history", optional=True),
    HumanMessagePromptTemplate(prompt=PromptTemplate(
        input_variables=["input"], template="{input}"
    )),
    MessagesPlaceholder(variable_name="agent_scratchpad")
])

llm = ChatOpenAI(model="gpt-4o")
agent = create_openai_tools_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

result = agent_executor.invoke({
    "input": "List all my agents, show which ones ran in the last 24 hours, and tell me how many leads are in each list"
})
print(result["output"])

Tool list

phantombuster_agent_delete

Permanently delete a PhantomBuster agent and all its associated data. This action is irreversible.

Name	Type	Required	Description
agentId	string	Yes	The unique identifier of the agent to permanently delete.

phantombuster_agent_fetch

Retrieve details of a specific PhantomBuster agent by its ID. Returns agent name, script, schedule, launch type, argument configuration, and current status.

Name	Type	Required	Description
agentId	string	Yes	The unique identifier of the agent to retrieve.

phantombuster_agent_fetch_output

Get the output of the most recent container of an agent. Designed for incremental data retrieval — use fromOutputPos to fetch only new output since the last call.

Name	Type	Required	Description
fromOutputPos	number	No	Start output from this byte position (for incremental fetching).
id	string	Yes	ID of the agent to fetch output from.
prevContainerId	string	No	Retrieve output from the container after this previous container ID.
prevRuntimeEventIndex	number	No	Return runtime events starting from this index.
prevStatus	string	No	Previously retrieved status from user-side (for delta detection).

phantombuster_agent_launch

Launch a PhantomBuster automation agent asynchronously. Starts the agent execution immediately and returns a container ID to track progress. Use the Get Container Output or Get Container Result tools to retrieve results.

Name	Type	Required	Description
agentId	string	Yes	The unique identifier of the agent to launch.
arguments	object	No	JSON object of input arguments to pass to the agent for this execution.
output	string	No	Output mode for the launch response.
saveArguments	boolean	No	Whether to persist the provided arguments as the agent’s default arguments for future launches.

phantombuster_agent_launch_soon

Schedule a PhantomBuster agent to launch within a specified number of minutes. Useful for delayed execution without setting up a full recurring schedule.

Name	Type	Required	Description
argument	object	No	Input arguments to pass to the agent for this execution (object or JSON string).
id	string	Yes	ID of the agent to schedule.
minutes	integer	Yes	Number of minutes from now after which the agent will launch.
saveArgument	boolean	No	If true, saves the provided argument as the agent’s default for future launches.

phantombuster_agent_save

Create a new PhantomBuster agent or update an existing one. Supports configuring the script, schedule, proxy, notifications, execution limits, and launch arguments. Pass an ID to update; omit to create.

Name	Type	Required	Description
argument	object	No	Default launch argument for the agent (object or JSON string).
branch	string	No	Script branch to use (e.g., main, staging).
executionTimeLimit	number	No	Maximum execution time in seconds before the agent is killed.
id	string	No	ID of the agent to update. Omit to create a new agent.
launchType	string	No	How the agent is launched.
maxParallelism	number	No	Maximum number of concurrent executions allowed for this agent.
maxRetryNumber	number	No	Maximum number of retries before aborting on failure.
name	string	No	Display name for the agent.
proxyAddress	string	No	HTTP proxy address or proxy pool name.
proxyPassword	string	No	Proxy authentication password.
proxyType	string	No	Proxy configuration type.
proxyUsername	string	No	Proxy authentication username.
script	string	No	Script slug or name to assign to this agent.

phantombuster_agent_stop

Stop a currently running PhantomBuster agent execution. Gracefully halts the agent and saves any partial results collected up to that point.

Name	Type	Required	Description
agentId	string	Yes	The unique identifier of the agent to stop.

phantombuster_agents_fetch_all

Retrieve all automation agents in the PhantomBuster organization. Returns agent IDs, names, associated scripts, schedules, and current status.

phantombuster_agents_fetch_deleted

Retrieve all deleted agents in the PhantomBuster organization. Returns agent IDs, names, creation timestamps, deletion timestamps, and who deleted each agent.

phantombuster_agents_unschedule_all

Disable automatic launch for ALL agents in the current PhantomBuster organization. Agents will remain but will only run when launched manually.

phantombuster_ai_completions

Get an AI text completion from PhantomBuster’s AI service. Supports multiple models including GPT-4o and GPT-4.1-mini. Optionally request structured JSON output via a response schema.

Name	Type	Required	Description
messages	array<object>	Yes	Array of conversation messages. Each must have a role (system, assistant, or user) and content string.
model	string	No	AI model to use for the completion.
temperature	number	No	Sampling temperature (0–2). Lower = more deterministic, higher = more creative.

phantombuster_branch_create

Create a new script branch in the current PhantomBuster organization.

Name	Type	Required	Description
name	string	Yes	Name for the new branch. Only letters, numbers, underscores, and hyphens allowed. Max 50 characters.

phantombuster_branch_delete

Permanently delete a branch by ID from the current PhantomBuster organization.

Name	Type	Required	Description
id	string	Yes	ID of the branch to delete.

phantombuster_branch_release

Release (promote to production) specified scripts on a branch in the current PhantomBuster organization.

Name	Type	Required	Description
name	string	Yes	Name of the branch to release.
scriptIds	array<string>	Yes	Array of script IDs to release on this branch.

phantombuster_branches_fetch_all

Retrieve all branches associated with the current PhantomBuster organization.

phantombuster_container_attach

Attach to a running PhantomBuster container and stream its console output in real-time. Returns a live stream of log lines as the agent executes.

Name	Type	Required	Description
id	string	Yes	ID of the running container to attach to.

phantombuster_container_fetch

Retrieve a single PhantomBuster container by its ID. Returns status, timestamps, launch type, exit code, and optionally the full output, result object, and runtime events.

Name	Type	Required	Description
id	string	Yes	ID of the container to fetch.
withNewerAndOlderContainerId	boolean	No	Set to true to include the IDs of the next and previous containers for this agent.
withOutput	boolean	No	Set to true to include the container’s console output.
withResultObject	boolean	No	Set to true to include the container’s result object.
withRuntimeEvents	boolean	No	Set to true to include runtime events (progress, notifications, etc.).

phantombuster_container_fetch_output

Retrieve the console output and execution logs of a specific PhantomBuster container (agent run). Useful for monitoring execution progress, debugging errors, and viewing step-by-step agent activity.

Name	Type	Required	Description
containerId	string	Yes	The unique identifier of the container whose output to retrieve.

phantombuster_container_fetch_result

Retrieve the final result object of a completed PhantomBuster container (agent run). Returns the structured data extracted or produced by the agent, such as scraped profiles, leads, or exported records.

Name	Type	Required	Description
containerId	string	Yes	The unique identifier of the container whose result to retrieve.

phantombuster_containers_fetch_all

Retrieve all execution containers (past runs) for a specific PhantomBuster agent. Returns container IDs, status, launch type, exit codes, timestamps, and runtime events for each execution.

Name	Type	Required	Description
agentId	string	Yes	The unique identifier of the agent whose containers to retrieve.

phantombuster_leads_delete_many

Permanently delete multiple leads from PhantomBuster organization storage by their IDs.

Name	Type	Required	Description
ids	array<string>	Yes	Array of lead IDs to delete.

phantombuster_leads_fetch_by_list

Fetch paginated leads belonging to a specific lead list in PhantomBuster organization storage.

Name	Type	Required	Description
includeTotalCount	boolean	No	Include the total count of leads in the response.
listId	string	Yes	ID of the lead list to fetch leads from.
paginationOffset	integer	No	Offset for pagination.
paginationOrder	string	No	Sort order for pagination.
paginationSize	integer	No	Number of leads per page.

phantombuster_leads_save

Save a single lead to PhantomBuster organization storage.

Name	Type	Required	Description
lead	object	Yes	Lead data object to save.

phantombuster_leads_save_many

Save multiple leads at once to PhantomBuster organization storage.

Name	Type	Required	Description
leads	array<object>	Yes	Array of lead objects to save.

phantombuster_list_delete

Permanently delete a lead list from PhantomBuster organization storage by its ID.

Name	Type	Required	Description
id	string	Yes	ID of the lead list to delete.

phantombuster_list_fetch

Retrieve a specific lead list from PhantomBuster organization storage by its ID.

Name	Type	Required	Description
id	string	Yes	ID of the lead list to fetch.

phantombuster_lists_fetch_all

Retrieve all lead lists in the PhantomBuster organization’s storage.

phantombuster_location_ip

Retrieve the country associated with an IPv4 or IPv6 address using PhantomBuster’s geolocation service.

Name	Type	Required	Description
ip	string	Yes	IPv4 or IPv6 address to look up.

phantombuster_org_export_agent_usage

Export a CSV file containing agent usage metrics for the current PhantomBuster organization over a specified number of days (max 6 months).

Name	Type	Required	Description
days	string	Yes	Number of days of usage data to export. Maximum is ~180 days (6 months).

phantombuster_org_export_container_usage

Export a CSV file containing container usage metrics for the current PhantomBuster organization. Optionally filter to a specific agent.

Name	Type	Required	Description
agentId	string	No	Filter the export to a specific agent ID.
days	string	Yes	Number of days of usage data to export. Maximum is ~180 days (6 months).

phantombuster_org_fetch

Retrieve details of the current PhantomBuster organization including plan, billing, timezone, proxy config, and CRM integrations.

Name	Type	Required	Description
withCrmIntegrations	boolean	No	Include the organization’s CRM integrations.
withCustomPrompts	boolean	No	Include the organization’s custom prompts.
withGlobalObject	boolean	No	Include the organization’s global object in the response.
withProxies	boolean	No	Include the organization’s proxy pool configuration.

phantombuster_org_fetch_agent_groups

Retrieve the agent groups and their ordering for the current PhantomBuster organization.

phantombuster_org_fetch_resources

Retrieve the current PhantomBuster organization’s resource usage and limits. Returns daily and monthly usage for execution time, mail, captcha, AI credits, SERP credits, storage, and agent count.

phantombuster_org_fetch_running_containers

List all currently executing containers across the PhantomBuster organization. Returns container IDs, associated agent IDs/names, creation timestamps, launch types, and script slugs.

phantombuster_org_save_agent_groups

Update the agent groups and their ordering for the current PhantomBuster organization. The order of groups and agents within groups is preserved as provided.

Name	Type	Required	Description
agentGroups	array<unknown>	Yes	Array of agent groups. Each item is either an agent ID string or an object with id, name, and agents array.

phantombuster_org_save_crm_contact

Save a new contact to the organization’s connected CRM (HubSpot). Requires a CRM integration to be configured in the PhantomBuster organization settings.

Name	Type	Required	Description
company	string	No	Company the contact works at.
crmName	string	Yes	The CRM to save the contact to.
email	string	No	Contact’s email address.
firstname	string	Yes	Contact’s first name.
jobtitle	string	No	Contact’s job title.
lastname	string	Yes	Contact’s last name.
pb_linkedin_profile_url	string	Yes	LinkedIn profile URL of the contact.
phone	string	No	Contact’s phone number.

phantombuster_script_fetch

Retrieve a specific PhantomBuster script by ID including its manifest, argument schema, output types, and optionally the full source code.

Name	Type	Required	Description
branch	string	No	Branch of the script to fetch.
id	string	Yes	ID of the script to fetch.
withCode	boolean	No	Set to true to include the script’s source code in the response.

phantombuster_scripts_fetch_all

Retrieve all scripts associated with the current PhantomBuster user. Returns script IDs, names, slugs, descriptions, branches, and manifest details.

Name	Type	Required	Description
branch	string	No	Filter scripts by branch name.
exclude	string	No	Exclude modules or non-modules from results.
org	string	No	Filter scripts by organization.