Build an AI social agent

Build an AI agent
that runs your socials.

An AI social media agent needs to do three things: decide what to post, publish it, and read the results to decide what's next. The publishing part is where most builds die — nine platform APIs, nine OAuth flows, nine response shapes. PostLake collapses that into one tool your agent calls, and returns analytics in one normalised shape it can actually reason over.

Get started free →

No card for the free tier · one API for every network · full MCP access

The fastest way: ask your agent

Prefer no code at all? Connect PostLake's hosted MCP server to your AI assistant (Claude, ChatGPT, Cursor and others) and just ask it to post. It authorizes over OAuth — no key to paste. See the MCP guide.

Give the agent one tool

An agent that posts needs exactly two things: a way to publish, and a way to see how it did. PostLake is both, behind one API:

import requests

HEADERS = {"Authorization": "Bearer YOUR_API_KEY"}

def publish(text: str, platforms: list[str]) -> dict:
    """The agent's publish tool — one call, every network."""
    return requests.post(
        "https://api.postlake.dev/v1/posts",
        headers=HEADERS,
        json={"text": text, "platforms": platforms, "profile": "my-brand"},
    ).json()

def performance(period: str = "30d") -> dict:
    """The agent's feedback loop — same metric names on every network."""
    return requests.get(
        f"https://api.postlake.dev/v1/analytics?period={period}",
        headers=HEADERS,
    ).json()

Step by step

  1. Create a PostLake API key (Settings → API Keys) and connect the accounts your agent should manage, under one profile.
  2. Give the agent a publish tool that POSTs to /v1/posts, and a performance tool that GETs /v1/analytics.
  3. Let the agent decide the content — that's your model's job; PostLake is the hands, not the brain.
  4. Close the loop: feed the normalised analytics back in so the agent learns which networks and formats actually work.
  5. Schedule ahead by adding scheduledAt, so the agent can plan a week rather than post reactively.

Good to know

Why PostLake

One call, every network

List the platforms in one request and PostLake posts to all of them, returning a single normalised shape — your code never branches per platform.

No app setup

PostLake manages each network's developer app and OAuth for you — nothing to register or get reviewed yourself.

Agent-safe

Idempotency keys mean a retry never double-posts, and analytics come back normalised so an agent can decide what to post next.

Honest pricing

A free tier to start, then usage-based credits — one per post. You only pay for posts that actually publish.

Common questions

How do I build an AI agent that posts to social media?

Give your agent two tools: one that POSTs to PostLake's /v1/posts endpoint to publish, and one that GETs /v1/analytics to read results. PostLake handles every network's API and OAuth behind those calls, so the agent only ever deals with one request shape and one response shape.

What stops an AI agent from double-posting?

Idempotency keys. Send an Idempotency-Key header with each publish and a retry — or an agent that restarts mid-task — resolves to the same post instead of creating a second one.

How does the agent know what to post next?

PostLake returns cross-platform analytics in one normalised shape, with the same metric names on every network. The agent reads that directly and compares posts and platforms without special-casing anything.

Can the agent use MCP instead of the REST API?

Yes — PostLake ships a hosted MCP server with a curated toolset for exactly this: publish, schedule, validate and read analytics. Connect it and your agent posts by asking, authorised over OAuth.

More guides

Python · Node.js · n8n · Make · Zapier · All guides · Full docs

One API and MCP server for every major network. Free to start.

Get started free →