Omnara

Status: Open source and actively developed. The current platform design launched publicly in October 2026, so expect fast change.

The problem with long-running agents

A chat request takes seconds. An agent that researches a market, works through a codebase, or monitors something for a week can run for hours or days. Over that time, things fail: a server restarts, a container runs out of memory, a deploy rolls the workers. In a simple agent loop, the agent’s state lives in the memory of the process running it, so a crash means starting over, or worse, repeating actions it already took.

Omnara is built around fixing that. It treats an agent’s history as the source of truth and stores it outside the process doing the work. That approach is usually called durable execution, and it is what makes long-running agents practical in production.

How it works

Every agent has an append-only event log in Postgres. Inputs, model outputs, and tool calls are recorded as typed rows. Workers are stateless: a worker picks up an agent, runs one iteration of the loop (a model call, or the tool calls the model asked for), writes the result to the log, and puts the next iteration on a queue. Then it lets go.

That has three practical effects. If a worker crashes, any other worker can resume the agent from the log. An agent waiting on a human approval or a scheduled check uses no compute while it waits. And one worker can serve many agents, because none of them need a process of their own. This is a concrete form of persistent agent state.

Where the agent runs

Omnara separates the agent from the machine its tools act on. An agent can run with no machine at all (for API-only tools), on Omnara-managed sandboxes, on your own Blaxel, Daytona, or Unikraft Cloud account, or on any macOS or Linux laptop, VM, or container running the omnarad daemon. The daemon connects outbound to Omnara, so you do not need to open ports. That makes it possible to run an agent against a local development machine and a cloud sandbox with the same setup.

You launch, steer, and watch agents through a REST API, a TypeScript SDK (@omnara/sdk), or a CLI, and you can change an agent’s configuration mid-conversation without redeploying. There are integrations for MCP servers, reusable skills, Slack, Discord, and GitHub.

Safety and visibility

Omnara includes human approval for risky tool calls (an authorization gate), credentials stored as encrypted and scoped secrets, role-based access for people and API keys, and an append-only audit history of every action. Agent timelines can be streamed, and the site mentions cost visibility, though it gives few details.

These are the right building blocks. How strong they are in practice depends on how you configure them, and Omnara does not list security certifications or compliance standards on its site. Pair it with a sandbox that limits what tools can reach; see agent sandboxes.

Pricing and hosting

The code is Apache 2.0 on GitHub (around 3,200 stars as of late October 2026) and free to self-host with Docker Compose for a quick start, with a separate guide for production. Omnara Cloud adds managed services with no platform fee: bring your own model keys and machines for free, or pay through credits at provider token rates plus active machine time. You can start on the cloud and move to your own VPC later.

Who it is for

Omnara is aimed at developers and small teams building agents for their users or for internal work who want durability and control without committing to one vendor’s managed agent service. Its own positioning is as an open-source alternative to hosted options such as Claude Managed Agents and OpenAI’s Agents API. Example agents on the site include an SRE agent with read-only AWS access, a Slack coding agent, a social-signal digest, and a browser agent.

It is not a no-code tool. If you want to build workflows by clicking rather than coding, platforms like n8n or Lindy are closer fits.

Limitations

• Young platform. The repository has no tagged releases yet, and the current design was announced publicly in October 2026.
• You still pick everything else. Omnara handles execution and state; models, tools, prompts, and evaluation are up to you.
• Postgres is central. Self-hosting means running and backing up the database that holds every agent’s history.
• Light on observability detail. Streaming timelines and audit history exist, but deeper tracing and cost analytics are not well documented.
• Thin independent track record. Most information so far comes from Omnara’s own site and launch posts.

Alternatives

• LangGraph, an open-source framework with its own persistence and checkpointing for stateful agents
• CrewAI, for orchestrating multiple role-based agents
• NVIDIA OpenShell, a sandboxed runtime that focuses on policy enforcement rather than durability
• Ona, persistent cloud environments for software agents

For context, see agent runtime observability, AI Agent Infrastructure Explained and Estimate AI Agent Cost.