# apra-fleet **Run a fleet of AI agents across your devices, your providers, your workflows.** What Kubernetes did for containers, apra-fleet does for AI agents: scheduling, credentials, isolation, and observability for an agentic workforce -- on any machine, anywhere, using every LLM provider at once. [![CI](https://github.com/Apra-Labs/apra-fleet/actions/workflows/ci.yml/badge.svg)](https://github.com/Apra-Labs/apra-fleet/actions/workflows/ci.yml) [![License: Apache 2.0](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) [![Platform](https://img.shields.io/badge/platform-macOS%20%7C%20Linux%20%7C%20Windows-lightgrey.svg)](https://github.com/Apra-Labs/apra-fleet/releases) [![MCP](https://img.shields.io/badge/MCP-compatible-8A2BE2.svg)](https://modelcontextprotocol.io) [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/Apra-Labs/apra-fleet) [Quick Start](#quick-start-5-minutes) - [Live Demo](#watch-a-fleet-work) - [How It Works](#how-it-works) - [fleet-sprint Getting Started Guide](docs/fleet-sprint-getting-started.md) - [Website](https://apra-labs.github.io/apra-fleet)

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apra-fleet fleet-sprint dashboard, real recording: the sprint's own integration tester finds two real bugs, files them against itself, then a second cycle plans, fixes, and closes them -- captions burned in.

> **This repository is built by the product you are looking at.** An > autonomous apra-fleet workflow plans, codes, reviews, tests, and ships > this codebase in multi-hour sprints -- filing bugs against itself and > fixing them. The recording above is a real run, not a mockup. --- ## Why a fleet? Running one AI agent is a demo. Running fifty -- across a MacBook in the office, a GPU box in the lab, three cloud VMs, and your CI -- is an operations problem nobody else has solved: - **Which machine runs which agent?** Real devices, not throwaway sandboxes: registered, credentialed, health-checked members you already own. - **Which model does which job?** Claude for review, a cheap tier for mechanical edits, a local vLLM model for private data -- all in one fleet, routed by cost tier, switchable per task. - **Who watches the agents?** Durable workflows with supervisors, watchdogs, reservations, and live dashboards. Agents that die get detected. Work that stalls gets resumed. Nothing runs silently. - **Who holds the keys?** Secrets entered out-of-band, never visible to any model. Per-provider permission composition. Network egress policy per credential. One control plane. Any device. Any model. Any workflow. Any domain. ## What you get | Pillar | Concretely | |---|---| | **Any device** | Register any Windows / macOS / Linux machine (local or over SSH) as a fleet member in one command. Cloud members auto-start on demand. Windows members are fully supported for dispatch, command execution, and background long-running tasks (launched detached via WMI). | | **Any model** | Claude, Codex, Copilot, Antigravity, local models (any OpenAI-compatible endpoint via OpenCode) -- mixed freely. Tier-based routing (cheap / standard / premium) keeps cost governance built in. Cross-provider review is a quality mechanism: a different model, with different blind spots, checks every change. | | **Any workflow** | Workflows are durable programs, not prompt chains: multi-hour, resumable, observable, with member reservations and atomic state. Write your own; ship it to the fleet. | | **Any domain** | Not just software development. The pattern fits wherever work decomposes into agent-sized pieces that need orchestration and an audit trail: nightly retail replenishment (reconcile inventory deltas, draft purchase orders for sign-off), logistics exception handling (triage a delayed shipment, re-book, notify), healthcare intake (summarize referrals, check completeness, route), back-office runs (invoice matching, compliance evidence collection). Software engineering is the vertical running today -- your domain is a workflow away. |

apra-fleet topology: one control plane dispatching to six heterogeneous member devices across providers and operating systems

## Watch a fleet work Our flagship workflow, **fleet-sprint**, develops software autonomously: plan -> develop -> review -> deploy -> integration-test -> harvest, in cycles, until the goal is met or the evidence says stop. It is not a toy. It builds apra-fleet itself: - Multi-cycle sprints running for hours, unattended - 2,300+ unit tests and an 81-file integration suite against real backends - Files bugs against itself, decomposes them, fixes them, and blocks its own release until quality gates pass - Every dispatch, verdict, and dollar visible live on the dashboard - Every sprint's raw child stdout/stderr is captured to a per-sprint log and linked from the dashboard, so a run's output is traceable even if it crashes before reporting anything back A fleet that has run in production: ``` pm-1 Opus (premium) orchestrator doer-1 Sonnet (standard) feature work doer-2 Antigravity large-context tasks reviewer Opus (premium) final review ``` The engine does not know what a "sprint" is; it knows how to run your workflow reliably across your fleet (see **Any domain** above). ## Quick start (5 minutes) **1. Install** -- one command via npm (Node.js 22.16+), or grab the standalone installer binary for your platform from [Releases](https://github.com/Apra-Labs/apra-fleet/releases) and double-click it (installation is the default action): ```bash npm install -g @apralabs/apra-fleet apra-fleet install # installs for Claude Code (default) apra-fleet install --llm agy # or --llm opencode / codex / copilot apra-fleet start # npm install registers no OS service: start the server (again after reboot) ``` The standalone installer instead registers and starts a user-level service (no `start` needed) 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`). See [docs/install.md](docs/install.md). The default install includes fleet-se (fleet-sprint, supervisor, bd), which requires a system Node.js 22.16+ and npm on PATH; the installer fails up front if they are missing. Use `apra-fleet install --workflows none` to install the core only. For Claude Code, `install` also adds the **fleet-status mod** -- a Claude Code plugin that shows every member's state in the status line and opens a `/fleet-status` pane where you can also remove a member and list, add, replace or delete secrets (a secret's value is typed in another terminal or the server's one-time form, never in Claude Code). It is installed through the `claude` CLI from a local marketplace in `~/.apra-fleet/claude-plugins/` and replaces the old statusLine script. If the `claude` CLI is missing or too old to install plugins, install says why and keeps the statusLine script instead. A statusLine of your own is never overwritten. See [docs/install.md](docs/install.md#the-fleet-status-mod-claude-code). **2. Connect your agent.** Load the fleet server in Claude Code with `/mcp` (or restart your provider CLI). Your agent now has a fleet. To check from a shell, `claude mcp list` should show a line like `apra-fleet: http://localhost:7523/mcp (HTTP) - ... Connected` (7523 is the default port). **3. Register members -- in plain language.** apra-fleet is driven conversationally through any MCP-capable agent: > "Register a local member called `doer`. Register another called > `reviewer`. Pair them." > "Register 192.168.1.10 as `build-server`. Username akhil, work folder > `/home/akhil/projects/myapp`." Remote passwords are collected out-of-band -- typed into a separate terminal, never the chat -- used once to set up SSH keys, then forgotten. **4. Run your first workflow:** ```bash apra-fleet workflow hello-world ``` Then point the fleet at real work: ```bash apra-fleet workflow fleet-sprint \ --issue my-project-epic --members doer \ --branch fleet-sprint/first-run --base main ``` Open the dashboard, watch your fleet PLAN->BUILD->REVIEW->TEST->SHIP in a loop till closure > **New to fleet-sprint?** Read the > [fleet-sprint Getting Started Guide](https://apra-labs.github.io/apra-fleet/fleet-sprint-getting-started.html) > ([Markdown](docs/fleet-sprint-getting-started.md) if you're reading this on > GitHub) -- a plain-English walkthrough > of what it does, what you need to prepare (beads backlog, `deploy.md`, > test playbooks, member registration), how to launch and monitor a sprint, > and what's automated versus what's still your call. **Running fleet-sprint after npm install:** the `apra-fleet workflow fleet-sprint ...` command above is the same one command for everyone -- whether you installed via `npm install -g @apralabs/apra-fleet`, the standalone binary, or a git-clone dev checkout. There is no separate `fleet-sprint` command to install or remember. (Standalone installer: the binary is not on PATH, so use `~/.apra-fleet/bin/apra-fleet` or add `~/.apra-fleet/bin` to PATH -- see step 1.) See [the full flag reference](packages/apra-fleet-se/fleet-sprint/docs/README.md) for every option. ## How it works ### Layered Architecture

apra-fleet layered architecture stack: dependencies from OS primitives up to autonomous engineering orchestrators

**Component Docs:** [fleet-sprint](packages/apra-fleet-se/fleet-sprint/docs/README.md) | [auto-sprint.js](packages/apra-fleet-se/apra-pm/docs/sprint-workflow.md) | [apra-fleet-client](packages/apra-fleet-client/docs/overview.md) | [apra-pm](packages/apra-fleet-se/apra-pm/README.md) | [apra-fleet-mcp](docs/mcp-tools.md) | [Agent Roles](packages/apra-fleet-se/docs/role-contracts.md) | [Sprint reliability gates](docs/sprint-reliability-gates.md) ### Fleet Dispatch Topology ```mermaid flowchart LR CP["Control Plane
(Server, Engine, Supervisor)"] -->|Dispatch & Sync| M1["MacBook
(Claude)"] & M2["Linux GPU
(vLLM)"] & M3["Cloud VM
(AGY)"] & M4["Windows
(OpenCode)"] ``` - **Fleet server**: the control plane. Registers members, dispatches commands and prompts, moves files, brokers credentials. Speaks MCP, so any MCP-capable agent can drive a fleet. `execute_prompt` supports session forking (`fork`) on fork-capable providers -- branch a new, independent session from an existing one's context (e.g. a primed session reused across per-task dispatches) without continuing to write into the source session. See [docs/mcp-tools.md](docs/mcp-tools.md#execute_prompt) for the parameter contract. - **Members**: real machines running provider CLIs. Composes provider-native permissions before every dispatch; unattended modes are scoped, never blanket. - **Workflow engine**: runs workflow programs with phases, retries, turn budgets, resumable sessions, per-activity persistent state, and a cooperative pause/resume gate any workflow can hook into. - **Supervisor**: always-on layer -- launch, pause/resume, & stop sprints over HTTP, member reservation ledger, crash watchdog (including a live "paused" state and base-branch-drift indicator), run history, and a supervisor-owned LLM-less backlog member that every launch is pinned to. The dashboard and backlog read a cached, tip-checked beads view and show when it was last refreshed ("Beads as of"); launch always forces a fresh beads check and answers 503, without reserving members or starting a sprint, when beads cannot be verified. ## Knowledge Layer Every agent session starts by calling `kb_session_prime`. The KB checks which files have changed since last read and returns exactly those. Unchanged files are served from cached summaries -- no re-read, no wasted tokens. ``` Cold session: kb_session_prime returns stale_files=[a.ts, b.ts, c.ts] Agent reads all three, calls kb_capture for each. Warm session: kb_session_prime returns stale_files=[], session_warm=true Agent works from KB summaries. Zero file reads. ``` MCP tools that ship with the KB: | Tool | What it does | |------|--------------| | `kb_session_prime` | Prime a session: stale files, fresh summaries, GitNexus call list | | `kb_capture` | Store a learning, context-cache, runbook, or knowledge entry | | `kb_query` | Two-level FTS retrieval (L1: title+summary, L2: full content) | | `kb_list` | Audit-list entries by confidence/type/module/symbol (read-only, no use_count bump) | | `kb_context` | Batch file freshness check (single git call for N files) | | `kb_invalidate` | Mark files stale immediately (also called by the git hook), or discard own entries by `ids` | | `kb_promote` | Advance confidence: UNVERIFIED -> INFERRED -> CONFIRMED (FULL session, or the sprint's kb_maintainer member session only) | | `kb_harvest` | Extract learnings from a session transcript (auto-fires after execute_prompt) | | `kb_export` | Additively merge live CONFIRMED entries whose cited files still match their recorded hashes into `.fleet/kb-canonical.json` -- the git-shareable team bible (FULL session only) | | `kb_bible_commit` | Merge confirmed entry ids into the bible (format v3, per-entry file hashes), remove superseded or invalidated entries, and commit locally with base-branch provenance (used by the sprint kb_maintainer) | | `kb_setup` | Install git hook, write provider config, store remote token encrypted (FULL session only) | `code_*` tools resolve the calling session's own folder, refuse with typed `E-CODE-INDEX-NOT-READY` / `E-CODE-INTEL-DISABLED` errors when the index is not usable, and report `indexedCommit` on every result; `code_reindex` and `code_status` drive and inspect the index (see [docs/code-index-readiness.md](docs/code-index-readiness.md)). Registering a remote member also installs a member-mode apra-fleet on it and wires a per-folder MCP entry, recorded as `fleetMcp` (see [docs/member-fleet-mcp-wiring.md](docs/member-fleet-mcp-wiring.md)). `session_stats` reports each member's `kb_*` / `code_*` call counts, which fleet-sprint shows per dispatch in its Knowledge & Code Intel viewer tab. `kb_setup --remote --token ` takes effect immediately: the next KB tool call resolves its project provider from this config, so a stock build points at a remote KB server by configuration alone, with no code change and no separate "server mode" build. A config with no remote, or any config the reader cannot parse, always falls back to the local SQLite provider -- see [Client-side provider selection](docs/knowledge-layer-design.md#client-side-provider-selection) for the exact selection rule and its fallback-construction invariant. **The provider config is install-wide, not per repo.** There is one `knowledge/config.json` per fleet install, so pointing it at a remote KB points EVERY repo that install serves -- every member, every project -- at that server. `kb_setup` only places the git post-commit hook (in the calling session's own repo); it does not scope the config. On a shared fleet server, treat `kb_setup --remote` as a change for all of its users. Every KB tool call is scoped to the calling session's own repo -- a fleet server handling many members across many repos never lets one repo's learnings land in another repo's KB. No `kb_*` tool takes a scope argument: a member session uses its registered work folder and any other session uses the fleet server's working folder, and a folder that is not a git repository with an origin remote is refused with a typed error that names the fix: `E-SELF-NO-WORKFOLDER` (create the folder or re-register the member), `E-SELF-NOT-A-REPO` (`git init` or clone there) or `E-SELF-NO-REMOTE` (`git remote add origin `; for a remote member, record its origin with `update_member git_repos`). Member sessions get a reduced, explicit tool list: the `code_*` tools, a few self-reporting tools and the `kb_*` tools except `kb_setup` and `kb_export` (never served to a member) and `kb_promote` / `kb_resolve_contradiction` (served only to the sprint engine's kb_maintainer session, which applies the reviewer's promotions). The `apra-fleet call` verb lets a process call tools as a member. KB reads default to CONFIRMED, undisputed entries. See [Per-repo KB isolation](docs/knowledge-layer.md#per-repo-kb-isolation) for the resolution, anchor and cache-keying rules, and [Member-session tool calls](docs/knowledge-layer.md#member-session-tool-calls) for the member tool model. The backend is swappable: start with local SQLite, add a central HTTP server for a team, or plug in Postgres later -- all via a one-line config change. See [docs/knowledge-layer.md](docs/knowledge-layer.md) for the full guide. ## Explore with agents. Operate with programs. There are two ways to orchestrate agents, and apra-fleet is built on the observation that you need both -- at different stages of a workflow's life: - **Exploration mode.** While a workflow is still being discovered, let an LLM orchestrate: flexible, adaptive, and token-hungry -- every step is a decision, and every decision costs thinking. - **Operation mode.** Once you know what must happen, the control flow becomes a deterministic workflow program. Shell, git, and file steps run through `execute_command` -- zero tokens. The model is invoked only at the corners that genuinely require judgment (`execute_prompt`): review this diff, plan this backlog, decide this exception.

That is not a projection -- it is this repository's own e2e setup+teardown step, before and after we hardened it. Development tokens are not operating tokens: pay once to discover the workflow, then run it free. | | LLM-orchestrated (explore) | Workflow-orchestrated (operate) | |---|---|---| | Control flow | the model decides each step (tokens) | deterministic program (free) | | Shell / git / file steps | narrated through the model | `execute_command`, zero tokens | | Where the model runs | everywhere | judgment nodes only (`execute_prompt`) | | Cost curve | scales with every step | scales with thinking only | | Failure mode | drift and silent retries | typed errors, resumable state | The collapse is two-dimensional. As a workflow hardens, control flow moves from model to program -- and the judgment nodes that remain move from frontier models to cheaper ones, because a well-specified task no longer needs discovery-grade reasoning. **Develop a workflow with Claude; operationalize it on OpenCode against a local or OpenRouter model.** Same fleet, same workflow -- swap the members. Tier routing makes it a registration change, not a rewrite. Only a fleet makes that trade possible. Single-provider tools cannot leave their vendor; in-process frameworks cannot move orchestration out of the token path. Because apra-fleet's unit of execution is the member -- a machine plus a provider, swappable at registration -- the same hardened workflow runs on frontier models the day you design it and on commodity models every day after. fleet-sprint is this principle, lived: it began as LLM-orchestrated exploration; each discovered pattern was hardened into the deterministic engine; today the engine drives hour-long autonomous runs in which models are consulted only as planner, doer, reviewer, tester, and harvester. ## Compare to alternatives | Tool | Overlap | Where apra-fleet differs | |------|---------|--------------------------| | Single-agent coding assistants | AI writes code | A fleet adds agents that review, test, and deploy each other's work -- across vendors. | | CI self-hosted runners | Runs work on other machines | Conversational and stateful, not pipeline-triggered; agents carry context between phases. | | SkyPilot / dstack | Multi-machine compute | Coordinates agents and their context, credentials, and permissions -- not just jobs. | | Google A2A | Agent-to-agent messaging | An opinionated orchestration and operations layer, not just a transport. | | Agent frameworks (LangGraph, CrewAI, ...) | Multi-agent logic | Those compose agents inside one process; apra-fleet operates agents across real machines, providers, and days-long workflows. | When NOT to use it: a one-off single-file change needs no fleet. ## Security model, in one paragraph Secrets are entered out-of-band into a credential store and referenced as `{{secret.NAME}}` -- resolved server-side at execution, never visible to any LLM or log; see [docs/secret-variables.md](docs/secret-variables.md). Credentials scope to members, expire on TTL, and can carry a network egress policy (allow / deny / confirm). Every member runs with composed, provider-native permission files -- allow-listed tools, not god-mode. VCS access is provisioned and revocable per member, across GitHub, Bitbucket, and Azure DevOps -- host differences (URL shape, PR REST dialect, auth pattern, error vocabulary) are hidden behind a per-provider descriptor rather than leaking into shared code; see `docs/design-azure-devops-vcs-auth.md` for the Azure DevOps provider's credential-assembly and PAT-lifetime details. A credential-requiring VCS command can be handed to the server for execution (`vcs_credential_exec`) rather than the orchestrator learning the plaintext token itself: the server substitutes the credential into the command, runs it on the member, and redacts the token from every field of the result -- the plaintext never transits an orchestrator-readable output. Permission composition verifies its own delivery: a grant is read back off the target member and structurally compared against what was intended before it is reported as applied, so a failed or partial write is surfaced as an explicit failure rather than a false success. Dropped or unmappable permission tokens are surfaced explicitly in composition warnings, and shell pipeline stages are decomposed into constituent command grants to provide precise permission suggestions across providers. ## Email Configuration The fleet `send_email` tool sends email via **SendGrid** or **SMTP**. Secrets are stored in the fleet credential store. Non-secret config (provider, host, port, from address) is passed by the workflow in each call. ### Storing secrets (one-time setup) Store email secrets via the CLI: ```bash # SendGrid API key apra-fleet secret --set sendgrid_api_key --persist # SMTP password apra-fleet secret --set smtp_password --persist ``` Or via the MCP tool (the path an LLM agent uses): ```json { "name": "sendgrid_api_key", "prompt": "Enter your SendGrid API key", "persist": true } ``` Secrets are encrypted in the fleet credential store. They never appear in workflow code, config files, or environment variables. ### Sending email from a workflow The workflow passes non-secret config inline and calls `send_email`. Load your config however you prefer (JSON file, hardcoded, etc.): ```javascript import { parseToolJson } from '@apralabs/apra-fleet-client'; import { connectFleet } from '@apralabs/apra-fleet-client/server-resolution'; const { fleetApi } = await connectFleet({ env: process.env }); // fleetApi wrappers return the raw MCP tool result ({ content: [...] }); // parseToolJson extracts the JSON payload. const result = parseToolJson(await fleetApi.sendEmail({ provider: 'smtp', host: 'smtp.example.com', port: 587, user: 'notifications@example.com', from: 'noreply@example.com', to: 'team@example.com', subject: 'Sprint Report', body: 'All tasks completed.' })); console.log(`Sent: ${result.messageId}`); ``` The SMTP password resolves from the credential store automatically. It never appears in the workflow. See `examples/workflows/email-notify/` for a complete runnable example and `docs/email-workflow-guide.md` for the full walkthrough. ### send_email Tool Reference | Parameter | Type | Required | Description | |---|---|---|---| | `provider` | `"sendgrid"` or `"smtp"` | no (default: `"sendgrid"`) | Email provider | | `from` | string | yes | Sender email address | | `host` | string | SMTP only | SMTP server hostname | | `port` | number | no (default: 587, or 465 when `secure` is true) | SMTP server port | | `user` | string | SMTP only | SMTP username | | `secure` | boolean | no (default: false) | Implicit TLS (port 465). When false, STARTTLS is required. | | `to` | string or string[] | yes | Recipient email address(es) | | `subject` | string | yes | Email subject line | | `body` | string | yes | Plain-text email body | | `html` | string | no | HTML email body | | `cc` | string[] | no | CC recipient addresses | | `bcc` | string[] | no | BCC recipient addresses | | `attachments` | attachment[] | no | File attachments (base64-encoded) | Each attachment: `filename` (string), `content` (string, base64), `contentType` (string, optional). Secrets are resolved from the credential store by name: - **SendGrid:** `sendgrid_api_key` - **SMTP:** `smtp_password` Returns: `{ ok: true, messageId }` on success, `{ ok: false, error }` on failure. ## The packages | Package | What it is | |---|---| | `apra-fleet` | The fleet platform: server, CLI, member management, credentials, workflows runtime | | `packages/apra-fleet-se` | The software-engineering vertical: fleet-sprint engine, agent contracts, integration suites | | `packages/apra-fleet-workflow` | Workflow authoring runtime: state, viewer, checkpointing | | `packages/fleet-api-contract` | Typed API contract shared by server and clients | ## Status and roadmap apra-fleet is under active development -- by its own fleet. Current focus: hardening autonomous sprint execution (the toughest workflow we know of), supervisor-orchestrated multi-sprint operation, and the workflow SDK for third-party verticals. ## Documentation | Topic | Link | |-------|------| | **fleet-sprint Getting Started Guide (start here, plain English)** | [Website](https://apra-labs.github.io/apra-fleet/fleet-sprint-getting-started.html) - [Markdown](docs/fleet-sprint-getting-started.md) | | fleet-sprint set-aside rules (BLOCKED beads, deploy Permissions failure, immediate stall) | [docs/fleet-sprint-set-aside.md](docs/fleet-sprint-set-aside.md) | | Codebase wiki (architecture, internals, AI Q&A) | [DeepWiki](https://deepwiki.com/Apra-Labs/apra-fleet) | | Install, uninstall, the `--llm` flag | [docs/install.md](docs/install.md) | | Choosing a provider (roles, gotchas, mixing providers, OpenCode/local models) | [docs/provider-guide.md](docs/provider-guide.md) | | Antigravity (agy) provider (project binding, permission mapping, model resolution, Windows parity) | [docs/agy-provider.md](docs/agy-provider.md) | | Transport, service mode, and supported interfaces | [docs/transport-and-service-mode.md](docs/transport-and-service-mode.md) | | Cost model (tiering, shell-over-prompts, measured token spend) | [docs/cost-model.md](docs/cost-model.md) | | The PM skill (doer-reviewer sprints, `/pm` commands) | [docs/pm-skill-overview.md](docs/pm-skill-overview.md) | | FAQ | [docs/FAQ.md](docs/FAQ.md) | | Troubleshooting | [docs/troubleshooting.md](docs/troubleshooting.md) | | Keeping Fleet updated (`apra-fleet update`) | [docs/features/update.md](docs/features/update.md) | | Live member activity (`apra-fleet watch`, `logging.previewChars`) | [docs/features/watch.md](docs/features/watch.md) | | Secret variables and passwords | [docs/secret-variables.md](docs/secret-variables.md) - [docs/features/oob-auth.md](docs/features/oob-auth.md) | | Member category and tags | [docs/features/member-tags.md](docs/features/member-tags.md) | | Enabling SSH on a remote machine (if it does not have it yet) | [docs/ssh-setup.md](docs/ssh-setup.md) | | Git authentication | [docs/design-git-auth.md](docs/design-git-auth.md) | | GitHub App setup (exact permissions per git_access level) | [docs/github-app-setup.md](docs/github-app-setup.md) | | Cloud compute | [docs/cloud-compute.md](docs/cloud-compute.md) | | Architecture | [docs/architecture.md](docs/architecture.md) | | Dispatch and orchestration reliability design (Windows completion-on-exit, stall detector, test-runner wall-clock bound) | [docs/dispatch-reliability-hardening.md](docs/dispatch-reliability-hardening.md) - [docs/stall-detector-resilience.md](docs/stall-detector-resilience.md) | | Windows shell selection (probe order, gitbash/pwsh7/powershell5, shell vs os) | [docs/windows-shell-selection.md](docs/windows-shell-selection.md) | | Cross-shell command construction for member-bound commands | [docs/cross-shell-command-construction.md](docs/cross-shell-command-construction.md) | | Knowledge Layer (setup, usage, provider swap) | [docs/knowledge-layer.md](docs/knowledge-layer.md) | | Code intelligence provider abstraction | [docs/code-intelligence-providers.md](docs/code-intelligence-providers.md) | | Hub-spoke cloud migration plan (historical; see tier-3 ownership ADR) | [docs/hub-spoke-master-plan.md](docs/hub-spoke-master-plan.md) | | Tier-3 ownership decision (fleet-dashboard vs `src/hub-service/`) | [docs/adr-tier3-ownership.md](docs/adr-tier3-ownership.md) | | Shared hub/dashboard API contract package | [packages/fleet-api-contract/README.md](packages/fleet-api-contract/README.md) | | Workflow engine internals (`agent()`/`parallel()`/`pipeline()`, journal, budget, pause/resume) | [packages/apra-fleet-workflow/docs/apra-fleet-workflow-architecture.md](packages/apra-fleet-workflow/docs/apra-fleet-workflow-architecture.md) | | Cooperative workflow pause/resume (engine, viewer, supervisor, fleet-sprint) | [docs/features/workflow-pause-resume.md](docs/features/workflow-pause-resume.md) | | Sprint run summary (`GET /state?summary=1`, published once per state, pulled by supervisor rows) | [docs/sprint-run-summary.md](docs/sprint-run-summary.md) | | Supervisor dashboard live-refresh (`/state` + `/events` SSE, tab-activation refresh, in-memory scope expansion) | [docs/features/supervisor-dashboard-live-refresh.md](docs/features/supervisor-dashboard-live-refresh.md) | | Writing and running workflow scripts | [packages/apra-fleet-workflow/docs/workflow-guide.md](packages/apra-fleet-workflow/docs/workflow-guide.md) | | Authoring a SEA-embedded `apra-fleet workflow` (manifest, entry contract, launcher env vars) | [docs/authoring-workflows.md](docs/authoring-workflows.md) | | Workflow launcher fleet-server resolution order (HTTP singleton vs. stdio) | [docs/adr-workflow-server-resolution.md](docs/adr-workflow-server-resolution.md) | | Running fleet-sprint (full flag reference; identical for npm-install, standalone binary, and git-clone dev checkout) | [packages/apra-fleet-se/fleet-sprint/docs/README.md](packages/apra-fleet-se/fleet-sprint/docs/README.md) | | fleet-sprint overview (autonomous plan-develop-review-publish loop) | [packages/apra-fleet-se/docs/overview.md](packages/apra-fleet-se/docs/overview.md) | | fleet-sprint CLI reference | [packages/apra-fleet-se/docs/cli-reference.md](packages/apra-fleet-se/docs/cli-reference.md) | | fleet-sprint internals (cycle loop, stall detection, budget, topology) | [packages/apra-fleet-se/docs/architecture.md](packages/apra-fleet-se/docs/architecture.md) | | fleet-sprint agent role contracts | [packages/apra-fleet-se/docs/role-contracts.md](packages/apra-fleet-se/docs/role-contracts.md) | | fleet-supervisor skill (start/stop/restart/auto-start-on-boot, sprint launch via HTTP API) | [packages/apra-fleet-se/fleet-sprint/skills/fleet-supervisor/SKILL.md](packages/apra-fleet-se/fleet-sprint/skills/fleet-supervisor/SKILL.md) | | Scoped in-cycle replan findings threading, and the wrapper-injection role KB contract | [docs/scoped-replan-and-planner-kb-contract.md](docs/scoped-replan-and-planner-kb-contract.md) | | MCP client SDK overview (transports, `ApraFleet` API) | [packages/apra-fleet-client/docs/overview.md](packages/apra-fleet-client/docs/overview.md) | | MCP client SDK API reference | [packages/apra-fleet-client/docs/api-reference.md](packages/apra-fleet-client/docs/api-reference.md) | | MCP client SDK getting started | [packages/apra-fleet-client/docs/getting-started.md](packages/apra-fleet-client/docs/getting-started.md) | | Memory contract v1 inventory findings and invariants (`kb_*`/`code_*` tool surface) | [docs/memory-contract-v1-inventory-notes.md](docs/memory-contract-v1-inventory-notes.md) | | Memory contract v1 schema generation design (zod -> JSON Schema 2020-12) | [docs/memory-contract-v1-generator-design.md](docs/memory-contract-v1-generator-design.md) | | Memory contract v1 round-trip validation, drift guard, and T1/T2/T3/T7 handoff design | [docs/memory-contract-v1-roundtrip-and-handoff.md](docs/memory-contract-v1-roundtrip-and-handoff.md) | ## Community - Questions and ideas: [GitHub Discussions](https://github.com/Apra-Labs/apra-fleet/discussions) - Releases: [GitHub Releases](https://github.com/Apra-Labs/apra-fleet/releases) - Issues: [GitHub Issues](https://github.com/Apra-Labs/apra-fleet/issues) - What is planned next: [ROADMAP.md](ROADMAP.md) If Apra Fleet helped you ship faster with better quality, please [star the repo](https://github.com/Apra-Labs/apra-fleet) -- it helps others find it. ## Development Build from source (also the path for Intel Macs): ```bash git clone https://github.com/Apra-Labs/apra-fleet && cd apra-fleet npm install && npm run build && npm test ``` `npm test` runs the full local suite: the root vitest suite, the `apra-fleet-se` workspace suite, and the `apra-pm` suite (which is not an npm workspace and is otherwise only reachable via an explicit `--prefix` invocation) -- so a green local run and a green CI run see the same tests. See [CONTRIBUTING.md](CONTRIBUTING.md) to contribute. ## License Apache 2.0 -- see [LICENSE](LICENSE). ---

**Stop babysitting agents. Start operating fleets.** [Quick Start](#quick-start-5-minutes) - [GitHub Issues](https://github.com/Apra-Labs/apra-fleet/issues) - [Apra Labs](https://apralabs.com)