Connect your agent
Use MCP, the HTTP API, or the CLI to apply a task and start a run. Add a schedule or webhook when the work needs to repeat.
A Niltavi product
Your agent starts the work. Lerryn gives it a sandbox to run in, credentials by reference, and a clear record of what happened.
Prepare the daily report.
daily-report · Python
report.csv + execution log
From request to result
Use MCP, the HTTP API, or the CLI to apply a task and start a run. Add a schedule or webhook when the work needs to repeat.
Execute Python, Node, and Shell in Docker containers. Start with SQLite on one host, then use PostgreSQL and S3 for a multi-node deployment.
See stages, logs, results, and artifacts in the web console. Keep execution history with the task, so your next decision has context.
One platform, familiar interfaces
Use a local MCP connection, inspect the HTTP API, or work directly from your terminal. Each route reaches the same task and run model.
Read the CLI & agent guidelerryn login https://your-lerryn.example --username alice
claude mcp add lerryn -- lerryn mcp --stdioRemote MCP clients can also connect to your deployment’s /mcp endpoint.
curl https://your-lerryn.example/api/v1/discover \
-H "Authorization: Bearer $LERRYN_TOKEN"The OpenAPI contract describes the operations, schemas, and errors.
lerryn tasks apply ./daily-report
lerryn runs start daily-report --test --wait 60sKeep the task manifest and files together in a versioned directory.
Your infrastructure. Your controls.
Bind credentials by reference and inject them at runtime. Lerryn encrypts stored values and redacts known secret values from captured logs. Task code still needs the same care and review as any code with access to your systems.
Start with a single-node deployment. Connect an agent when you’re ready.
Start self-hosting