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agentline.cloud

Give your AI agent a US phone number to make and receive calls. Connect through MCP, a skill, or REST. Numbers are $2/month; calls are $0.10/min.

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What is AgentLine?

AgentLine is a service that gives an AI agent its own US phone number so it can make and receive calls and read inbound SMS. It is aimed at developers and builders who want voice and messaging as a capability inside an existing agent, rather than a standalone phone system for humans. The site pitches setup in minutes and offers several integration routes: a Python SDK, REST from Node.js, a single prompt, or MCP.

What it actually does

  • Numbers: assigns a US phone number to an agent, so the agent can place outbound calls and answer inbound ones.
  • SMS: inbound text messages can be read by the agent.
  • Integration: Python SDK, REST from Node.js, a prompt-driven setup, or MCP. The page shows a short Python example that creates an agent, buys a number, and starts a call.
  • Self-signup: the agent can be pointed at a setup file and register itself, buy a number, and call back.

Who it suits

Someone building a support bot, an outbound calling agent, or a personal assistant that needs a real phone identity. The advertised pricing signals — a monthly number fee and a per-minute call rate — make it a pay-as-you-go option for prototypes and small deployments. A team that already runs agents in Hermes, OpenClaw, Hark or similar environments is the clearest fit, since the page lists those as supported agents.

Trade-offs to weigh

Giving an agent a live phone number means real calls, real latency and real consequences. Test with a low-volume number first, and decide up front how the agent should handle voicemail, wrong numbers and callers who want a human. If your use case is purely text-based chat, a phone number adds cost and complexity you may not need.

Next step

If you want to try it, the fastest path is the self-signup prompt on the site: point your agent at the setup instructions, let it register and buy a number, then have it call your own phone. That gives you a concrete sense of call quality and setup effort before committing to anything larger.

How do I set up an AI agent with a phone number using AgentLine?

AgentLine gives an AI agent a real US phone number so it can place and receive calls and read inbound SMS. Setup is designed to be agent-driven: you can paste a single prompt and let the agent register itself, buy a number, and call you back, or you can install the skill with your own API key and control the account yourself.

The quickest path

  1. Give your agent this instruction: get yourself a phone number and call me, following https://agentline.cloud/skill.md.
  2. The agent signs itself up, acquires a number, and initiates the call.
  3. Answer and confirm the voice flow works end to end.

This is the lowest-effort route and matches the "install in 30 seconds" framing on the page. It suits prototyping, demos, and anyone who wants a working agent before writing integration code.

The controlled path

If you want the number tied to an account you manage, install the skill with your API key instead. From there you choose how your code talks to AgentLine:

Integration Best for Trade-off
Python SDK (pip install agentline-ai) Scripts, notebooks, backend services Requires you to manage the runtime
REST from Node.js Web apps and serverless functions More wiring than an SDK
Single prompt Fast experiments, non-developers Least control over call logic
MCP Agents already using tool connections Depends on your MCP client

The Python example on the page shows the shape of the workflow: create an agent with a name and system prompt, buy a number for that agent, then create a call to a destination number. That three-step sequence — agent, number, call — is the mental model to hold onto regardless of which integration you pick.

What to decide before you start

  • Who owns the number. Self-signup is fast but puts the agent in charge; an API key keeps billing and configuration with you.
  • Inbound versus outbound. The page covers making and receiving calls plus reading inbound SMS. If your use case is answering support lines, test inbound first; if it is outreach or confirmation calls, test outbound first.
  • Which agent framework you already run. The page lists OpenAI, Claude Code, Hermes, OpenClaw, Cursor, and others, so check your stack appears before committing.

A concrete first test

Pick one narrow job — for example, an agent that calls a number you control and reads back a short confirmation. Run it through the prompt-based setup first. Once the call connects and the audio is acceptable, move the same logic into the Python or Node.js path so you can version it and add error handling. Blake Southerland's comment on the page describes having an agent calling within about ten minutes using Hermes, which is consistent with treating the prompt route as a smoke test rather than a production setup.

For pricing details, check the pricing page directly at AgentLine rather than relying on secondhand figures.

What are the costs for using AgentLine's phone numbers and calls?

AgentLine charges for numbers and calls separately: US phone numbers cost $2 per month, and calls cost $0.10 per minute, according to the service's own pricing signals. That means a low-volume setup — one number plus a modest amount of calling — stays in the single-digit-dollars-per-month range, while cost scales mainly with talk time.

What drives the bill

Component Stated cost What it means in practice
US phone number $2/month A recurring charge per number, whether or not it's used much
Calls $0.10/minute Applies to call time, so long or frequent calls dominate the bill

How to think about it

For a solo builder testing an agent that makes a handful of short calls, the number fee is the floor and call minutes add little. For a support or outreach use case running many concurrent conversations, the per-minute rate becomes the real cost center, and the $2 number fee is negligible by comparison.

The practical decision criterion is call volume, not number count. Estimate your expected monthly minutes, multiply by $0.10, then add $2 for each number you keep active. If your agent handles long calls or high call volumes, that estimate — not the headline number price — should drive your budgeting.

A useful next step is to check the official pricing page for current details and any tiers or limits that apply: AgentLine.

Can I try AgentLine before committing to a paid plan?

Yes — AgentLine is built around a low-commitment trial path rather than a traditional free trial. You can connect an agent and place a real call without signing a contract or committing to a monthly plan, and the costs involved are small enough to test cheaply.

Ways to try it before committing

  • Agent self-signup. The site's own suggested prompt tells an agent to "get yourself a phone number and call me," following the published skill file. In practice, this means you can hand the setup to the agent itself and see whether the integration works end to end before deciding anything.
  • Demo line. AgentLine publishes a US number you can call to speak with a live AI agent hosted on the platform. This is the lowest-effort check: no account, no code, no key — just call and judge the voice quality and latency yourself.
  • Skill or API key install. If you'd rather control the setup, install the skill with an API key or use the Python SDK / REST / MCP options. You can buy a single number and make one call to validate the flow.

What "trying" actually costs

AgentLine's published pricing signals are pay-as-you-go rather than subscription-gated: numbers are listed at $2/month and calls at $0.10/min. So a realistic trial — one number for a month plus a handful of test calls — lands in the low single-digit dollars, not a plan commitment. Treat those figures as the vendor's stated rates and confirm current pricing on the pricing page before you scale.

A practical test sequence

  1. Call the demo number first. If the voice quality or turn-taking doesn't meet your bar, stop there.
  2. If it does, run the self-signup prompt or install the skill with an API key.
  3. Buy one number and place one outbound call to your own phone.
  4. Only then wire it into a real workflow — inbound SMS reading, a support agent, or an outbound notification flow.

Decision criterion

Try it if your uncertainty is technical — whether your agent framework, MCP setup, or SDK integration behaves as expected. Skip the trial phase and evaluate commercially if your uncertainty is operational: whether a $0.10/min call rate holds up at your projected volume. The first question is answered in ten minutes; the second needs your own traffic numbers.

For a broader view of the agent-telephony space, AgentLine sits alongside general voice API providers, but its distinguishing bet is agent-first setup — the agent provisions its own number rather than a human wiring a dashboard.

Which AI agent frameworks and platforms does AgentLine support?

AgentLine presents itself as a telephony layer that sits underneath whichever agent framework you already use, rather than a framework of its own. Its page lists a set of named agents and coding environments as supported integrations, and offers four connection methods: a Python SDK, REST calls from Node.js, a single prompt, and MCP.

Named agents and environments on the page

The integration list on the page includes OpenAI, Dots, Hark, Claude Code, Hermes, OpenClaw, Grokbot, Codex, Vercel, Cursor, Meta Muse, Devin, and Replit. Treat this as a starting roster rather than a compatibility guarantee: some are full agent platforms, others are coding assistants or hosting environments, and the depth of each integration may differ.

Ways to connect

Method Best for Trade-off
Python SDK Teams building a custom agent in Python Ties you to Python tooling
REST from Node.js JavaScript/TypeScript stacks and existing backends You handle more plumbing yourself
Single prompt Quick trials, letting the agent self-signup Least control over setup details
MCP Agents that already speak the Model Context Protocol Requires an MCP-capable client

A practical way to decide

If your agent runs in Claude Code, Cursor, Hermes, OpenClaw, or a similar MCP-capable client, start with MCP or the single-prompt route — the page shows a prompt that has the agent fetch skill.md, sign itself up, buy a number, and call you back. If you are shipping a production service in Python or Node, use the SDK or REST path so number purchase and call creation live in your own code and logs.

One caveat worth testing early: the page's example creates an agent, buys a US number, and places an outbound call in a few lines, but it does not spell out per-framework feature parity. Before committing, verify that your specific framework can both place and receive calls and read inbound SMS through the chosen method. For a concrete check, run the demo number shown on the page, then repeat the flow from your own agent and confirm inbound SMS arrives where you expect it.

For current integration details and pricing terms, see AgentLine.

How does AgentLine handle inbound SMS for AI agents?

AgentLine lets an AI agent receive inbound SMS through the same API it uses for calls, rather than treating text as a separate product. The page describes the core capability as “Make and receive calls, and read inbound SMS — all through one API,” so the intended workflow is: the agent holds a US phone number, and messages sent to that number become readable by the agent through AgentLine’s integration layer.

What “read inbound SMS” means in practice

The wording is specific: read inbound SMS. That points to a pull-based or event-driven model where your agent or backend retrieves incoming messages and decides what to do with them — reply logic, routing, or handing the content to an LLM. It is not presented on the page as a full conversational SMS product with threads, media handling, or delivery analytics. If your use case is “customer texts my support agent and the agent answers in a natural loop,” you should verify whether outbound SMS replies are supported and how threading is represented before committing.

How it fits the integration options

The same connection paths used for calls apply here:

  • MCP — connect an MCP-capable agent and let it call AgentLine tools directly.
  • Skill / prompt — point an agent at the skill file and let it self-signup and configure.
  • REST or SDK — Python SDK (agentline-ai) or REST from Node.js for custom message handling.

For inbound SMS, the SDK/REST route gives you the most control: you can poll or subscribe, inspect the sender and body, and write your own reply or escalation logic. The prompt-based route is fastest for a demo but leaves less room for custom parsing.

A concrete scenario

A solo developer building a scheduling agent gives it a US number. Clients text “Can we move Thursday to 3pm?” The agent reads the inbound message, checks the calendar, and either replies or flags it. The value is that the phone number and the message channel live in one place, so the agent does not need a separate SMS vendor or a second credential set. The trade-off is that you are responsible for the conversation state and reply behavior unless AgentLine exposes more than reading.

Decision criterion

Choose AgentLine for inbound SMS if your agent mainly needs to ingest texts and act on them, and you already want calls on the same number. If you need rich two-way SMS threads, MMS, or carrier-grade deliverability reporting, confirm those specifics on the pricing and docs pages before building. The pricing page is at AgentLine and the skill file is referenced as agentline.cloud/skill.md for setup.

A useful next step: send a test text to your assigned number and log the raw payload your integration receives. That single check tells you whether the inbound shape matches how your agent expects to consume messages.

Related questions

More questions →
OpenClaw Phone Number: How to Get One and What It Costs

AgentLine lists OpenClaw among the agents it supports, so you can give an OpenClaw agent a real US phone number that makes and receives calls and reads inbound SMS. Numbers run $2/month and calls $0.10/min, and setup can be as short as pasting one prompt. This fits you if you want a working US number without building telephony yourself; it is less suited if you need non-US numbers or outbound SMS, which the page does not offer.

What AgentLine gives an OpenClaw agent

  • A real US phone number, provisioned in under 5 minutes per the site.
  • Outbound and inbound calls.
  • Read access to inbound SMS.
  • One API surface for all of it, reachable through MCP, a skill, or REST.

The site's own demo line, +1 (734) 471-3677, connects you to a live AI agent hosted on AgentLine if you want to hear the call quality before signing up.

Pricing

Item Cost
US phone number $2/month
Calls $0.10/min

At that rate, a number plus 100 minutes of calling costs about $12/month. The page does not state whether inbound and outbound minutes are billed the same, whether SMS is charged, or whether there is a free tier — treat those as open questions to confirm before committing to volume.

Ways to connect OpenClaw

Pick based on how much control you want:

  • Agent self-signup (fastest). Paste this prompt into OpenClaw: Get yourself a phone number and call me. Follow https://agentline.cloud/skill.md. The agent signs itself up, buys a number, and calls you back. No API key handling on your side.
  • Install the skill with your API key. Use the same skill URL but supply your own key, so the number and billing stay under your account.
  • MCP. Point OpenClaw at the AgentLine MCP endpoint if your setup already speaks MCP.
  • Python SDK. pip install agentline-ai, then create an agent, buy a number, and place a call:
from agentline_ai import AgentLine

client = AgentLine(api_key="al_live_...")

agent = client.agents.create(
    name="Support Bot",
    system_prompt="You are a helpful assistant."
)

number = client.numbers.buy(agent_id=agent.id, country="US")
call = client.calls.create(
    agent_id=agent.id,
    to_number="+14155557890"
)
  • Node.js / REST. Same operations over HTTP if you prefer not to add a Python dependency.

Shortest path to a working call

  1. Decide whether OpenClaw signs itself up or you hold the API key. Self-signup is quicker; holding the key is better if you need to manage billing centrally.
  2. Give OpenClaw the skill prompt or install the skill with your key.
  3. Let it buy a US number — expect this to complete in minutes, not hours.
  4. Trigger a call to your own phone first. A call to a number you control is the fastest way to confirm audio, latency, and that the agent's prompt is behaving.
  5. Verify inbound: call the number back and check that the agent answers, then send an SMS and confirm it can read it.

Expected result: a US number tied to your agent, one successful outbound call, and one successful inbound call.

Common failure points

  • Number purchase fails. Usually an account or payment issue rather than a code problem — check the account state before debugging the integration.
  • Call won't connect. Confirm the destination number is in full E.164 format (+14155557890, not 4155557890).
  • API key or agent misconfiguration. If you installed the skill with your own key, a wrong or expired key will surface as an auth error on the first API call, not at install time.
  • Agent signs up but never calls. The self-signup prompt depends on the agent following the skill URL end to end; if it stops early, fall back to installing the skill with your key so each step is explicit.

Is it the right choice for OpenClaw?

Choose AgentLine if you want a US number, both call directions, inbound SMS reading, and you're comfortable paying per number plus per minute. The builder quotes on the page lean toward fast setup — one reports the agent calling within about 10 minutes, another describes setup in Hermes as easy enough that they only had to finish their end. Those are individual reports, not guarantees, but they match the "under 5 minutes" claim for provisioning.

Look elsewhere if you need numbers outside the US, outbound SMS, or published per-minute rates for inbound versus outbound — none of that is documented on the page.

How to Give Your AI Agent a Real Phone Number and Make Calls

You can give an AI agent a real US phone number in under five minutes with AgentLine: buy a number, create an agent, then place or receive calls through one API. Numbers cost $2/month and calls cost $0.10/min, so this fits builders who want a working phone line for an agent without building telephony infrastructure. The steps below cover the minimum working setup, the integration options, and the common failure points.

What an "AI phone number" actually is

An AI phone number is a real US number assigned to an agent rather than a person. The agent can:

  • Make outbound calls
  • Receive inbound calls
  • Read inbound SMS

All of this runs through a single API. The number is the identity; the agent is the logic behind it. You are not forwarding calls to a human — the agent answers and speaks.

Pick your integration path

AgentLine supports four ways to connect, and the right one depends on your stack:

Method Best for What you provide
Python SDK Python codebases pip install agentline-ai and an API key
Node.js REST JavaScript/TypeScript services REST calls from Node.js
MCP Agents that support MCP MCP setup
Single prompt Fastest possible start A prompt pointing at the skill file

If you want the agent to set itself up, the prompt route is the shortest: paste a prompt that tells the agent to get a number and call you back, following https://agentline.cloud/skill.md. The agent signs itself up, grabs a number, and calls you.

If you prefer explicit control, use the SDK or REST.

Minimum working setup (Python SDK)

This is the smallest sequence that ends with a live call.

1. Install the SDK

pip install agentline-ai

2. Create a client with your API key

from agentline_ai import AgentLine

client = AgentLine(api_key="al_live_...")

3. Create an agent

agent = client.agents.create(
    name="Support Bot",
    system_prompt="You are a helpful assistant."
)

Input: a name and a system prompt. Expected result: an agent object with an id you reuse in later calls.

4. Buy a number for that agent

number = client.numbers.buy(agent_id=agent.id, country="US")

Input: the agent ID and country. Expected result: a US number bound to the agent.

5. Place a test call

call = client.calls.create(
    agent_id=agent.id,
    to_number="+14155557890"
)

Input: the agent ID and the destination number. Expected result: the agent calls that number and speaks using its system prompt.

Verification: call your own number first. If the agent answers and responds, the number, agent, and call path are all working. You can also call the live demo number +1 (734) 471-3677 to hear an agent hosted on AgentLine before wiring up your own.

Cost structure and scale estimate

Two charges apply:

  • $2/month per number — a flat recurring fee
  • $0.10/min per call — metered by call duration

A rough estimate: 1 number running 500 minutes of calls per month costs $2 + (500 × $0.10) = $52/month. Ten numbers at the same usage cost $20 + $500 = $520/month. The number fee is negligible next to call minutes, so scale your estimate around expected talk time, not number count.

Common failure points

Number purchase fails. Check that the agent_id exists and the country value is supported ("US" in the example). A number must be attached to an agent.

Call won't connect. Verify the destination is in full E.164 format (leading +, country code, no spaces). Confirm the agent was created successfully before calling.

SMS not readable. The documented capability is reading inbound SMS. Don't assume outbound SMS works the same way without checking the docs.

API key or MCP errors. Keys look like al_live_.... If you're using MCP, follow the skill file at https://agentline.cloud/skill.md rather than hand-configuring endpoints. If you're using the prompt route, paste the prompt exactly as given so the agent can complete signup itself.

Setup feels stuck. The reported experience from builders is that it moves fast — one noted having an agent calling them in about 10 minutes, and another set it up in Hermes by feeding the agent an article and following its instructions. If you're spending much longer, you're likely misconfiguring a step rather than hitting a platform limit.

Choosing between the paths

  • Want it working in minutes with no code: use the single-prompt / skill route.
  • Already have a Python service: use the SDK.
  • Running Node.js: use REST.
  • Using an MCP-capable agent: use MCP.

All four reach the same result — a real US number your agent can call from and answer on. Start with the prompt route to confirm the number and call flow work, then move to the SDK or REST when you need it embedded in your own code.

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Hosting and Email
DNSdns-parking.com
HostingVercel
EmailAmazon SES
Approximate IP geolocation (inferred; a CDN or proxy may differ from the origin server) United States flagUnited States Inferred 216.198.79.1

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RDAP / WHOIS

RegistrarHOSTINGER operations, UAB
Registered2026-05-08
Expires2027-05-08
Domain statusclient transfer prohibited
Nameserversaster.dns-parking.com、helios.dns-parking.com
DNSSECunsigned

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DNS

TypeNameValueTTLPriority
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MXagentline.cloudmx2.hostinger.com1440010
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TXTagentline.cloudgoogle-site-verification=E8LbjKkhn2gAQmNyiIB3cg3-xXG8FAMQDenhdnZtl-A14400—
TXTagentline.cloudv=spf1 include:_spf.mail.hostinger.com -all14400—
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TLS / SSL

AssessmentNormal configuration
Supported protocolsTLSv1.2、TLSv1.3
Negotiated protocolTLSv1.3
Certificate subjectagentline.cloud
IssuerLet's Encrypt
Valid until2026-12-09T02:56 · Remaining when checked: 58 days
Verification detailsCertificate trust: Passed · Hostname match: Passed

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cache-controlpublic, max-age=0, must-revalidate
serverVercel
strict-transport-securitymax-age=63072000
content-security-policyframe-ancestors 'none'
x-frame-optionsDENY
permissions-policypayment=*
access-control-allow-origin*

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