Grounded context
Combine graph, vector, and keyword retrieval. Grounded output carries citations; ungrounded output is labeled.
Use one platform for durable agents, knowledge, memory, permissions, human approvals, and audit. Build your own product—or start from Libra Desk, the reference application built on LibraOS.
Combine graph, vector, and keyword retrieval. Grounded output carries citations; ungrounded output is labeled.
Scope conversational and long-lived memory to the application, end user, and agent that own it.
Keep external actions behind explicit policy and human review instead of hiding control in prompts.
Start long-running jobs, follow their progress, recover from failures, and preserve an execution receipt.
Trace work to the application, user, employee, and job without inventing a second identity system.
Use the LibraOS SDK and native APIs, with OpenAI-compatible model access where it fits your stack.
pip install libraos-sdk
# Then follow the maintained three-call example:
# employee.create → agent.create → messages.create
open https://libraos.com/docs/creating-an-agent
Tell the wizard about your team, recurring work, knowledge sources, and approval boundaries. It proposes editable employees and tools for you to review before activation.
Get help with installation, APIs, model connections, or an issue in a running LibraOS environment.
Tell us what happened and include relevant logs or screenshots. Our team replies by email.
Open support →Use the installation and developer guides for setup steps, API examples, and deployment details.
Read the docs →Discuss your use case, environment, and governance needs with the LibraOS team.
Contact sales →