Open source -- Apache-2.0
What Kubernetes did for containers, apra-fleet does for AI agents: scheduling, credentials, isolation, and observability for an agentic workforce -- on hardware you already own, mixing every LLM provider at once.
register members -- plain language
"Register a local member called doer. Register
another called reviewer. Pair them."
fleet manifest -- 4 members registered
| doer-1 | claude | standard | sonnet |
| reviewer-1 | claude | premium | opus |
| integration-test-gpu | agy | standard | gemini-3.5-flash |
| harvester | claude | standard | sonnet |
launch a sprint
apra-fleet workflow fleet-sprint \ --issue my-project-epic --members doer \ --branch fleet-sprint/first-run --base main
Three real moments, not a mockup: the registration wording, the manifest, and the launch command are all quoted from the project README.
The problem
A laptop in the office, a GPU box in the lab, a few VMs, and several provider subscriptions -- nobody's single-agent tool answers the questions that show up next:
Real devices you already own -- registered, credentialed, health-checked members. Local or over SSH, on Windows, macOS, or Linux.
Claude for review, a cheap tier for mechanical edits, a local model for private data -- one fleet, routed by cost tier, switchable per task.
Durable workflows with watchdogs, stall detection, reservations, and live dashboards. Dead agents get detected; stalled work gets resumed.
Secrets entered out-of-band, never visible to any model. Composed, allow-listed permissions per member -- not god-mode.
What it is
apra-fleet is an MCP server + CLI. Any MCP-capable agent -- Claude Code, Codex, Copilot, OpenCode, Antigravity -- becomes the orchestrator of a fleet: it registers machines as members, dispatches commands and prompts to them, moves files, and brokers credentials. You drive it in plain language: "Register a local member called doer. Register another called reviewer. Pair them."
The architecture
The architecture overview: layer by layer -- from OS primitives and provider CLIs up through the fleet MCP server, agent roles, and client runners. Note where the seam sits: Layer 4, apra-fleet-workflow, is a domain-agnostic engine; Layer 5, apra-fleet-se, is the software-engineering vertical built on top of it.
Why it's different
Each of these is checkable in the repository -- file paths included.
Agent frameworks compose agents inside one process. apra-fleet's unit of execution is a member: a machine plus a provider CLI, swappable at registration. Every provider sits behind one strategy interface, which is what makes mixing Claude, Codex, Copilot, Antigravity, and local models genuinely work.
src/providers/ -- AgentStrategyDiscover a workflow with an LLM orchestrating -- flexible and token-hungry. Then harden it into a deterministic program: shell, git, and file steps run at zero tokens; models are invoked only at judgment nodes (review this diff, plan this backlog, decide this exception).
execute_command / execute_prompt -- README, docs/cost-model.mdWorkflows are resumable programs with execution journals, turn budgets, stall detection, crash watchdogs, member reservations, and self-healing for credential auth and orphaned CLI processes -- the unglamorous layer that lets multi-hour unattended runs actually finish.
packages/apra-fleet-workflow/ -- src/services/orphan-recovery.tsSecrets are typed into a separate terminal, encrypted at rest, and resolved server-side at execution time. Credentials scope to members, expire on TTL, and carry per-credential network egress policy. Members run with composed, provider-native, allow-listed permissions.
docs/features/oob-auth.md -- docs/architecture.mdThe cost curve
One measured example, from this repository's own e2e setup-and-teardown step: four real LLM-orchestrated runs cost $0.46 to $3.05 each. Hardened into a deterministic workflow, the same step now runs at roughly $0 -- the model is simply no longer in the loop for mechanical steps. Development tokens are not operating tokens.
The existence proof
apra-fleet's flagship workflow, fleet-sprint, develops software autonomously: plan -> develop -> review -> deploy -> integration-test -> harvest, in cycles, against an issue tracker. It is how this codebase ships.
Planner
Turns a sprint goal into a beads feature+task DAG with acceptance criteria. No structured verdict -- the DAG itself is the output.
Doer
Works an assigned list of ready task beads, committing after each. Never closes a feature or bug issue itself -- that is another role's job.
Reviewer
Reviews each commit against the task's acceptance criteria; can reopen work. Never closes issues either -- only reports a verdict.
Integ-Test-Runner
Runs the integration playbook, closes features and verify-set beads against real evidence, and files bugs for what fails.
Harvester
Extracts durable knowledge into docs, updates the README, and defers what's still low priority.
Each role is a separate dispatch, and each dispatch
can land on a different provider -- in the manifest above, doer-1
runs on Sonnet (claude), integration-test-gpu on Antigravity
(agy), reviewer-1 on Opus (claude). That's not incidental:
cross-provider review is a quality mechanism -- a
different model, with different blind spots, checks every change (README,
"Any model"). Role definitions live at
packages/apra-fleet-se/apra-pm/agents/<role>.md.
Quickstart
Via npm (Node.js 22.16+ and npm), or grab the standalone installer for your platform from Releases.
npm install -g @apralabs/apra-fleet apra-fleet install apra-fleet start
npm install registers no OS service, so apra-fleet start
starts the server (again after a reboot). The standalone installer
registers and starts a user-level service instead, and does not add
anything to PATH: the binary is ~/.apra-fleet/bin/apra-fleet
(Windows PowerShell: & "$HOME\.apra-fleet\bin\apra-fleet.exe" status).
The default install includes fleet-sprint, which needs a system Node.js
22.16+ and npm on PATH. Per-OS download commands:
docs/install.md.
Load the fleet server in Claude Code with /mcp. Your
agent now has a fleet.
"Register a local member called doer. Register another called reviewer. Pair them." "Register 192.168.1.10 as build-server. Username joe, work folder /home/joe/projects/myapp."
Remote passwords are collected out-of-band -- typed into a separate terminal, used once to set up SSH keys, then forgotten.
apra-fleet workflow hello-world apra-fleet workflow fleet-sprint \ --issue my-project-epic --members doer \ --branch fleet-sprint/first-run --base main
(Standalone installer: run ~/.apra-fleet/bin/apra-fleet, or add that directory to PATH.)
Open the dashboard and watch the fleet plan, build, review, test,
and ship in a loop. New to fleet-sprint? Start with the
plain-English
getting-started guide.
This walkthrough uses Claude Code, but the same install and workflow
commands work with any supported provider. For Antigravity/AGY:
apra-fleet --llm agy, then restart Antigravity to load the
fleet MCP server. For OpenCode: apra-fleet --llm opencode.
Codex and Copilot are also supported -- see
docs/install.md
for the full provider list.
Current state
An honest status page beats a surprised user. This list comes from the project's own release notes and roadmap, unedited in substance.
apra-fleet join / spoke) groundwork -- API contract and identity model landed; not yet usable end-to-endPositioning
| Category | Overlap | Where apra-fleet differs |
|---|---|---|
| AI pair programmerse.g. Cursor, Windsurf | AI writes code | A fleet adds agents that review, test, and deploy each other's work -- across vendors. |
| CI self-hosted runnerse.g. GitHub Actions self-hosted runners, Jenkins agents | Runs work on other machines | Conversational and stateful, not pipeline-triggered; agents carry context between phases. |
| Multi-machine compute schedulerse.g. SkyPilot, dstack | Multi-machine compute | Coordinates agents and their context, credentials, and permissions -- not just jobs. |
| Agent-to-agent protocolse.g. Google A2A | Agent-to-agent messaging | An opinionated orchestration and operations layer, not just a transport. |
| Multi-agent orchestration frameworkse.g. LangGraph, CrewAI, AutoGen | Multi-agent logic | Those compose agents inside one process; apra-fleet operates agents across real machines, providers, and days-long workflows. |
The "Overlap" and "Where apra-fleet differs" text is quoted from the project README's own comparison table; the category labels are generalized restatements, and the example tools per category are common, widely-known names for orientation, not a repo claim about those specific products.
Who is it for? Developers already paying for more than one AI subscription who want them working as one team; teams with idle real hardware; anyone whose agent runs are long enough to need babysitting; anyone who won't paste credentials into a model chat. When not to use it: a one-off single-file change needs no fleet.