Credentials. The deployment needs a LangSmith API key with deployment permissions and a model key, so store both in a secure secrets manager and never in the repo.
Who it is forA marketing operations lead who wants recurring reports and Slack questions handled by a hosted agent without running servers.
The problemRunning reports on a schedule and answering team questions in Slack needs hosting, Slack setup, and a scheduler the team would otherwise maintain.
The outcomeA deployed agent in LangSmith with managed Slack setup and weekly or monthly report schedules, plus a printed LangSmith URL to inspect it.
What you need first
- Python 3.11+ and uv installed
- Git installed to clone the repo
- Access to the ad platforms you use, through Pipeboard MCP or direct adapters for X Ads and OpenAI Ads
- A model key for the ask command and a chosen model for setup
- A LangSmith organization with MDA access, an API key with deployment permissions, and a model key for managed deployment
Steps
git clone https://github.com/langchain-ai/paid-media-agent.gitcd paid-media-agentuv syncuv run paid-media-agent mda checkuv run mda deploy .- Authorize your Slack workspace when prompted, then open the printed LangSmith URL to inspect the agent
Prompt for your agent
Deploy the paid-media-agent repo to Managed Deep Agents for the <target LangSmith organization>. Expected output: a deployed agent with a printed LangSmith URL and configured Slack. Verify that the deployment syncs the instructions and skills and that the scheduled report delivery channel is set before expecting Slack posts.
Caveat. Managed hosting is paid, and managed schedules post text to Slack only after you configure a delivery channel, with no automatic PDF attachments.
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