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What is Orca?

Orca is a hosted, multi-tenant platform for AI agents. You define a reusable agent once — its runtime, model, instructions, tools, skills, and memory — then run it, orchestrate it with other agents, and publish it as a chat endpoint your own apps can call. Every run streams live, every workspace is isolated per tenant, and usage, spend caps, and observability come standard. Under the hood Orca separates a stateless control plane (the Conductor) from stateful session management (Runners), so the platform scales horizontally to coordinate many concurrent agents across multiple LLM providers. You never operate that split on the hosted product — it’s what keeps your agents fast and reliable.

Quickstart

Create your account, build your first agent, and run it — in a few minutes.

Dashboard Guide

A screen-by-screen tour of everything you can do in the Orca dashboard.

Core Concepts

Agents, sessions, runs, pools, workflows, skills, memory, and more.

SDKs

Typed TypeScript, Python, and Go clients over the same API.

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Getting started

  • Sign up at www.orcapods.ai and create (or join) an organization.
  • Use the dashboard at app.orcapods.ai to build and run agents visually.
  • Call the API / SDKs with an ao_ tenant key (base URL https://api.orcapods.ai) for application code and automation.
Orca is also self-hostable — the source lives on GitHub and runs on Docker Compose or Kubernetes. See Local development and the deployment guides. The rest of this documentation leads with the hosted product; self-hosting notes are called out where they differ.

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What you can build

Reusable agents

Define an agent as a profile: a runtime, a model, a system prompt, and the tools, skills, and MCP servers it can use. Launch it anywhere, as many times as you like.

Multi-provider

Run on Claude, OpenAI, Google, or Groq — chosen per agent, not per deployment.

Live streaming

Every run streams progress, tool calls, and token usage over Server-Sent Events, with replay, in the dashboard and via the API.

Multi-agent workflows

Compose agents into pools and DAG workflows; a deterministic engine schedules and runs nodes in parallel.

Skills & memory

Give agents packaged Skills and a per-agent Memory Bank that persists useful knowledge across sessions.

Tools & MCP

Built-in platform tools plus any external Model Context Protocol server — including SaaS apps connected through OAuth.

Publish as chat

Publish an agent behind a stable chat endpoint and mint scoped API keys for your app.

Isolated files & storage

Each session gets an isolated virtual filesystem; a shared artefact store holds agent outputs.

Credits, caps & RBAC

Prepaid credits, per-tenant spend caps, usage metering, and role-based access control keep teams safe by default.

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How it works

  1. A dashboard action, SDK call, or POST /api/runs request names an agent profile and a prompt.
  2. The Conductor places a session on a capable Runner.
  3. The Runner builds a session-scoped toolkit and MCP endpoint, then hands the run to its agent-worker sidecar, which drives the chosen LLM.
  4. Run events stream back through the Conductor to every subscriber in real time.
See Architecture for the full design.

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Supported runtimes

Each agent picks a runtime, which selects how its model is driven: general is accepted as a legacy alias for the vercel runtime. Models are named provider:model, e.g. anthropic:claude-haiku-4-5, anthropic:claude-sonnet-4-6, openai:gpt-5.2, or google:gemini-2.5-pro. Availability and pricing follow the platform’s live rate feed.

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Where to next

Build your first agent

The fastest path from sign-up to a running agent.

Learn the dashboard

Every screen, explained.

Agent profiles

How agents are defined and configured.

Publish an agent

Expose an agent as a chat endpoint for your app.