42ecc96556 perf(ci): prefetch the consumer image and Grok binary during package staging (#15849)
## Thinking Path

> - Paperclip is the open source app people use to manage AI agents for
work
> - Pull requests to this repository run a large CI matrix. The slowest
check sets the time a contributor waits for a green run.
> - After #15826 leveled the server shards and #15831 split
`workspaces-b`, the `Build` lane is the slowest check: 634s in run
38107578322 (2026-10-11 03:11Z), ahead of the next server shard at 574s.
It was 533s in run 38107430861 and 543s to 568s in earlier runs that
night.
> - `Build` is about 100s of setup, 148s to 195s of `pnpm build`, and
278s to 320s of `scripts/verify-grok-npm-install.mjs`. The verifier
stages and packs the public packages first (84s to 100s of CPU work),
and only then pulls the pinned `node:24-trixie` consumer image (8
layers, 440 MB compressed) and downloads the Grok binary (65 MB gzip,
166 MB verified). Both network waits sit after the staging work on the
critical path.
> - Neither download depends on the packed packages. The image is pinned
by digest and the Grok binary is checked against its pinned sha256 by
the provisioning script, so starting them early changes no verified
property.
> - This pull request starts both downloads as background children
before staging and joins them where they are needed: the image before
the first sandbox, the binary after the negative probe. It also prints
one timing line per phase so the next slow part is visible in the CI
log.
> - The benefit is that the whole pull and the whole Grok download hide
behind the staging phase. The verifier should shed the full pull time on
a fresh runner, and the next cycle can read the phase split instead of
inferring it from log gaps.

## Linked Issues or Issue Description

**Describe the enhancement**

The `Build` PR check is now the slowest check in the run. Its last step,
the public npm install verifier, pulls the consumer Docker image and
downloads the Grok binary only after it has finished staging and packing
the eighteen public packages. Both downloads could run while the
packages are staged.

**Why is this needed**

The slowest check decides the wall-clock time of every PR run. In the
most recent fully green run (38107578322), `Build` took 634s: `pnpm
build` 195s and the verifier 320s. The verifier's log shows the staging
phase ends at +100s and the Grok result arrives at +267s. The image pull
and the Grok download are inside that second window, in series with the
consumer install and the probes.

**Proposed solution**

Start `docker pull --platform linux/amd64 <pinned image>` and
`provision-grok.mjs` as background children at the top of the verifier,
before the staging loop. Await the image right before the first sandbox
call. Provision Grok into a prefetch directory and rename it into
`native/grok` only after the "npm must not provision Grok" assertion and
the negative probe, so that assertion keeps its meaning and only the
positive probe sees the binary. Print a
`paperclip.public-npm-install.timing.v1` line with wall time per phase
and the time the main flow waited on each prefetch.

**Alternatives considered**

- Run the verifier as its own job in parallel with `Build`. It needs the
full `pnpm build` output, so that job would be as long as `Build` is
now.
- Warm the consumer npm cache from an Actions cache. That changes the
"clean public npm install" property the verifier reports. Not done here;
the new timing line will show whether the consumer install is worth that
discussion.
- Pack the eighteen packages concurrently. The three bundled packages
run their own npm installs in series inside `prepareBundledPackage`,
which is synchronous. A larger restructure; left for a later cycle with
timing data.
- Overlap the Pi runtime setup with the Grok probes. Both use the same
consumer directory from separate containers, and the probes after
provisioning are short. Not worth the concurrency on shared state.

## What Changed

- `scripts/prefetch-child.mjs` (new): `startPrefetch({ name, cmd, args,
cwd, env, logPath })` spawns a background child with no inherited
terminal and its stdout and stderr in a log file, and returns `{ name,
child, done, durationMs }`. `done` resolves on exit 0 and otherwise
rejects with the tail of the log; the rejection is observed inside the
helper, so an unawaited failure never becomes an unhandled rejection.
`durationMs` ends at the child's last write to its log (the exit event
is observed late when the caller is blocked in synchronous work; both
downloads print their final status line as they complete).
`stopPrefetches(prefetches, { graceMs })` sends `SIGTERM` to every child
still running, escalates to `SIGKILL` after the grace period, and
resolves only once all of them have exited.
- `scripts/verify-grok-npm-install.mjs`: the image pull and the Grok
provisioning start through the helper inside the `try` block, before the
staging loop. The main flow awaits the image before the first `npm
install` sandbox and awaits Grok after the negative probe, then renames
the staged binary into `native/grok` before the positive probe. The
`finally` block awaits `stopPrefetches` and only then removes the
scratch root, so cleanup never races a child's writes. The script prints
one timing JSON line after the Pi probe, with the per-phase wall times,
the two `*_wait_ms` values, and the corrected `*_prefetch_ms` values.
- `scripts/__tests__/prefetch-child.test.mjs` (new): seven tests run
small local Node children, with no Docker or network: a successful
prefetch resolves and keeps its output; the duration ends at the
download even when the parent is blocked for 1.5s; a nonzero exit
rejects with the log tail; a command that cannot start rejects instead
of hanging the join or the stop; stopping a running child waits for its
exit; a child that ignores `SIGTERM` is killed after the grace period;
an unawaited failure does not produce an unhandled rejection in a fresh
process.
- `scripts/__tests__/grok-public-install-sandbox.test.mjs`: the source
test asserts that exactly the pinned image is pulled for `linux/amd64`,
that Grok is provisioned into the prefetch directory and never straight
into `native/grok`, that both prefetches start before staging, that the
image is joined before the first sandbox, that the rename happens after
the npm assertion and the negative probe and before the positive probe,
that the verifier imports the helper, and that `stopPrefetches` is
awaited before the single `rmSync(root, ...)`.
- `package.json`: `test:release-registry` runs the new test file.

## Verification

- `node --test scripts/__tests__/prefetch-child.test.mjs
scripts/__tests__/grok-public-install-sandbox.test.mjs
scripts/__tests__/cursor-public-install-sandbox.test.mjs
scripts/__tests__/retain-runner-qualification-packages.test.mjs`: 22
pass (7 new in `prefetch-child.test.mjs`, 12 in the sandbox file). The
new file passed on three consecutive runs.
- `node --check scripts/verify-grok-npm-install.mjs` passes.
- `node packages/paperclip-runner/scripts/provision-grok.mjs
<scratch>/prefetch/grok` on this host: `Verified Grok 1.0.13
(linux-x64)` in 1.0s, `native/grok` absent before the move, 166079904
bytes in place after it.
- Docker is not installed on the host that produced this PR, so the
verifier was not run end to end locally. This PR's own `Build` job runs
`node scripts/verify-grok-npm-install.mjs` from the branch checkout, so
it exercises the change in full.

**Measured on this PR's first CI run**
([38108964834](https://github.com/paperclipai/paperclip/actions/runs/38108964834),
created 03:37Z), against two `master` runs created in the same twelve
minutes:

| | 38108878166 (`master`, 03:35Z) | 38108995800 (`master`, 03:37Z) |
38108964834 (this PR, 03:37Z) |
| --- | --- | --- | --- |
| Verifier step | 307s | 248s | 219s |
| `Build` job | 555s | 484s | 459s |
| Slowest check in the run | `Build` 555s | server (8/12) 535s | server
(12/12) 547s |

The PR run's timing line: `stage_packages_ms 84464`, `image_wait_ms 1`,
`consumer_install_ms 97305`, `lifecycle_rebuild_ms 3545`,
`codex_probe_ms 1211`, `grok_wait_ms 0`, `grok_probes_ms 626`,
`pi_setup_ms 22194`, `pi_probe_ms 7753`. Both waits are at most 1ms, so
the staging phase hid both downloads in full. The window from the end of
staging to the Grok result went from 161s to 167s on the two runs that
motivated this PR to 102s here, so the hidden downloads were about 60s
on a fresh `runs-on` spot instance. Against the two same-window `master`
runs, the verifier step is 29s to 88s shorter and `Build` is 25s to 96s
shorter; the spread is runner variance in `pnpm build` and the consumer
install.

The `image_prefetch_ms` and `grok_prefetch_ms` values on that first run
(84550 and 186696) were the parent's late observation of each exit, not
download times; that is the first Greptile finding, fixed by the second
commit, which measures from the download's last output instead.

## Risks

- Low risk. The verifier proves the same properties in the same order.
The only ordering change is that the downloads start earlier; every
assertion, probe, and sandbox argument is unchanged.
- The Grok binary is written under the scratch root in a `prefetch`
directory during staging. That directory is never mounted into a
container, and the `native/grok` assertion still runs against an absent
path.
- A prefetch that fails early now fails the step at its join point,
where the old code would have failed at the same download a few seconds
later. Same outcome, same message source.
- `*_prefetch_ms` is a last-output time. A child that printed early and
then kept working silently would be under-reported; neither `docker
pull` nor `provision-grok.mjs` behaves that way (both print their final
status as they finish), and a silent child falls back to the observed
exit time.
- Cleanup now waits up to 5s for a child that ignores `SIGTERM` before
killing it, so a failing run can end up to 5s later than before. Both
children exit promptly on `SIGTERM` in practice.
- The timing line is one more stdout line. Nothing in the repository
parses the verifier's stdout; the two workflows that run it write the
output to the job log or to a file.

## Model Used

- Claude (Anthropic), model ID `claude-fable-5-1`, running as a
Paperclip agent through the Claude local adapter with tool use (shell,
file edits, GitHub CLI). The agent collected the CI data, made the
change, ran the tests, and wrote this description.

## Checklist

- [x] I have included a thinking path that traces from project context
to this change
- [x] I have specified the model used (with version and capability
details)
- [x] I have checked ROADMAP.md and confirmed this PR does not duplicate
planned core work
- [x] I have searched GitHub for duplicate or related PRs and linked
them above
- [x] I have either (a) linked existing issues with `Fixes: #` / `Closes
#` / `Refs #` OR (b) described the issue in-PR following the relevant
issue template
- [x] I have not referenced internal/instance-local Paperclip issues or
links (only public GitHub `#NNN` / `github.com/paperclipai/paperclip`
URLs)
- [ ] My branch name describes the change (e.g. `docs/...`, `fix/...`)
and contains no internal Paperclip ticket id or instance-derived details
- [x] I have run tests locally and they pass
- [x] I have added or updated tests where applicable
- [x] I have updated relevant documentation to reflect my changes
- [x] I have considered and documented any risks above
- [ ] All Paperclip CI gates are green
- [ ] Greptile is 5/5 with no open P2s, recommendations, or follow-ups
- [x] I will address all Greptile and reviewer comments before
requesting merge

🤖 Generated with [Claude Code](https://claude.com/claude-code)

---------

Co-authored-by: devinfoley <139239+devinfoley@users.noreply.github.com>
Co-authored-by: Claude Fable 5.1 <noreply@anthropic.com>
2026-10-10 21:10:13 -07:00
…
…
…
…

Paperclip is the app people use to manage AI agents for work.

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Paperclip is the app people use to manage AI agents for work.

Open-source orchestration for teams of AI agents.

If OpenClaw is an employee, Paperclip is the company.

Paperclip is a Node.js server and React UI that orchestrates a team of AI agents to run a business. Bring your own agents, assign goals, and track work and costs from one dashboard. Choose models and harnesses per agent while keeping your team's tasks, skills, permissions, and history in one place.

It looks like a task manager. Under the hood: org charts, budgets, governance, goal alignment, and agent coordination.

Manage business goals, not pull requests.

Step Example
01 Define the goal "Build the #1 AI note-taking app to $1M MRR."
02 Hire the team CEO, CTO, engineers, designers, marketers — any bot, any provider.
03 Approve and run Review strategy. Set budgets. Hit go. Monitor from the dashboard.

Works
with
OpenClaw
OpenClaw
Claude Code
Claude Code
Codex
Codex
Cursor and Cursor Cloud
Cursor
+ Cloud
Gemini CLI
Gemini CLI
OpenCode
OpenCode
Pi
Pi
Hermes and Hermes Gateway
Hermes
+ Gateway
Grok Build
Grok Build
Kimi Code
Kimi Code

If it can receive a heartbeat, it's hired.

Custom processes, HTTP endpoints, and external adapter packages extend the roster. See the adapter overview for setup and capabilities.


Paperclip is right for you if

  • ✅ You want to build autonomous AI organizations
  • ✅ You coordinate many different agents (OpenClaw, Codex, Claude, Cursor) toward a common goal
  • ✅ You have 20 simultaneous Claude Code terminals open and lose track of what everyone is doing
  • ✅ You want agents running autonomously 24/7, but still want to audit work and chime in when needed
  • ✅ You want to monitor costs and enforce budgets
  • ✅ You want a process for managing agents that feels like using a task manager
  • ✅ You want to manage your autonomous businesses from your phone

The four pillars

Four things have to work for an organization of AI agents to actually produce: the tasks, the org, the training, and the infrastructure. Paperclip is built around exactly those four pillars.

The four pillars of Paperclip
Pillar Built for What it covers
Agentic Task Manager — Declare intent. Agents work. You verify the output. Everyone, daily Tasks, approvals & review gates · proactive agent coworkers · auditable routines & workflows · verify from diffs, screenshots & tests
Org Chart for Agents — Roles, permissions & boundaries for humans and agents. Managers Mixed human + agent org chart · responsibilities, delegation, specialization · governance: who can do what · scoped secrets & company boundaries · connection permissions & responsible-user identities
Agent Employee Training — Design, train & evaluate your AI employees. Enablers Skill Studio & shared org-wide skills · evals & saved test runs · active learning loops & quality metrics · performance reviews for agents · saved test inputs · skill version history & restore · reusable team templates
Agentic OS — The infrastructure that makes the work run. IT & platform Cross-provider runtime: any model, any agent · sandboxing, integrations & MCP servers · SSO, GRC, RBAC & cost controls · data privacy, internal trace collection, compounding data value · personal & shared app connections · run history & opt-in tracing

Features

🔌 Bring Your Own Agent

Any agent, any runtime, one org chart. If it can receive a heartbeat, it's hired.

🎯 Goal Alignment

Link tasks and projects to your organization goals. Agents receive the goal context behind their work.

💓 Heartbeats

Agents wake for assigned work, follow-up messages, or configured schedules. Delegation flows up and down the org chart.

💰 Cost Control

Company, agent, and project budgets. Track reported spend, get threshold alerts, and pause work at configured limits.

🏢 Multi-Organization

One deployment, many organizations. Separate tasks, agents, permissions, and activity histories for each.

🎫 Task Threads

Keep conversations, plans, blockers, files, and run history attached to the work. Assign tasks to agents or people.

🛡️ Governance

Configure review and approval stages, approve hires, and pause, reassign, or stop work when needed.

📊 Org Chart

Hierarchies, roles, reporting lines. Your agents have a boss, a title, and a job description.

📱 Mobile Ready

Monitor and manage your autonomous businesses from anywhere.

🔗 Apps & Connections

Connect services such as GitHub, Notion, and Railway, or your own MCP server. Set gateway actions to Allowed, Ask first, or Off.

👥 Shared Agents, Personal Accounts

Choose who can use a connection and which agents can access it. Managed GitHub operations can use the account of the person directing the work.

🧠 Skills & Skill Studio

Install or write shared skills, test them with saved inputs, inspect results, and restore earlier versions.

📅 Scheduled Routines

Run recurring work on a schedule or trigger it through an API or webhook. Each run has a task, an owner, and a history.

📎 Artifacts & Feedback

Find the files and documents agents produce. Preview supported formats and leave comments on specific passages in documents.

📦 Ready-Made Teams

Preview and install teams with roles, skills, projects, and routines. Choose their runtimes and make the setup your own.

Experimental Agent Chat and chat/email connectors add conversations with agents in Paperclip and through configured services such as Slack, Discord, Telegram, and AgentMail. Enable the relevant instance settings to try them.


Problems Paperclip solves

Without Paperclip With Paperclip
❌ You have 20 Claude Code tabs open and can't track which one does what. On reboot you lose everything. ✅ Tasks are ticket-based, conversations are threaded, sessions persist across reboots.
❌ You manually gather context from several places to remind your bot what you're actually doing. ✅ Context flows from the task up through the project and company goals — your agent always knows what to do and why.
❌ Folders of agent configs are disorganized and you're re-inventing task management, communication, and coordination between agents. ✅ Paperclip gives you org charts, ticketing, delegation, and governance out of the box — so you run a company, not a pile of scripts.
❌ Runaway loops waste hundreds of dollars of tokens and max your quota before you even know what happened. ✅ Spend tracking, budget alerts, and automatic pauses help you control the cost of ongoing work.
❌ You have recurring jobs (customer support, social, reports) and have to remember to manually kick them off. ✅ Routines create assigned tasks on a schedule, with outputs and run history you can inspect.
❌ You have an idea, you have to find your repo, fire up Claude Code, keep a tab open, and babysit it. ✅ Add a task in Paperclip. Your coding agent works on it until it's done. Management reviews their work.

Why Paperclip is special

Paperclip handles the hard orchestration details correctly.

Atomic task checkout. A single assignee and execution locks prevent competing runs from claiming the same task.
Persistent work context. Tasks, comments, and documents stay in Paperclip. Supporting adapters resume saved sessions across runs.
Runtime skill injection. Agents can learn Paperclip workflows and project context at runtime, without retraining.
Governance with rollback. Approval gates are enforced, config changes are revisioned, and bad changes can be rolled back safely.
Accountable connections. Human access, agent eligibility, and gateway action permissions are separate controls. Approve a call once or save a revocable rule.
Goal-aware execution. Linked tasks and projects carry goal ancestry so agents see the "why," not just a title.
Portable company templates. Export/import orgs, agents, and skills with secret scrubbing and collision handling.
Organization boundaries. Company-scoped access checks keep each organization's work, agents, and activity separate within one deployment.

What's Under the Hood

Paperclip is a full control plane, not a wrapper. Before you build any of this yourself, know that it already exists:

┌──────────────────────────────────────────────────────────────┐
│                       PAPERCLIP SERVER                       │
│                                                              │
│  ┌───────────┐  ┌───────────┐  ┌───────────┐  ┌───────────┐  │
│  │Identity & │  │  Work &   │  │ Heartbeat │  │Governance │  │
│  │  Access   │  │   Tasks   │  │ Execution │  │& Approvals│  │
│  └───────────┘  └───────────┘  └───────────┘  └───────────┘  │
│                                                              │
│  ┌───────────┐  ┌───────────┐  ┌───────────┐  ┌───────────┐  │
│  │ Org Chart │  │Workspaces │  │  Plugins  │  │  Budget   │  │
│  │ & Agents  │  │ & Runtime │  │           │  │ & Costs   │  │
│  └───────────┘  └───────────┘  └───────────┘  └───────────┘  │
│                                                              │
│  ┌───────────┐  ┌───────────┐  ┌───────────┐  ┌───────────┐  │
│  │ Routines  │  │ Secrets & │  │ Activity  │  │  Company  │  │
│  │& Schedules│  │  Storage  │  │ & Events  │  │Portability│  │
│  └───────────┘  └───────────┘  └───────────┘  └───────────┘  │
└──────────────────────────────────────────────────────────────┘
         ▲              ▲              ▲              ▲
   ┌─────┴─────┐  ┌─────┴─────┐  ┌─────┴─────┐  ┌─────┴─────┐
   │  Claude   │  │   Codex   │  │   CLI     │  │ HTTP/web  │
   │   Code    │  │           │  │  agents   │  │   bots    │
   └───────────┘  └───────────┘  └───────────┘  └───────────┘

The Systems

Identity & Access — Two deployment modes (trusted local or authenticated), human roles and permissions, agent API keys, short-lived run JWTs, company memberships, and invite flows. Responsible-user attribution follows work through delegation and supported managed connections.

Org Chart & Agents — Agents have roles, titles, reporting lines, permissions, and budgets. Adapter examples match the diagram: Claude Code, Codex, CLI agents such as Cursor/Gemini/bash, HTTP/webhook bots such as OpenClaw, and external adapter plugins. If it can receive a heartbeat, it's hired.

Work & Task System — Issues carry company/project/goal/parent links, atomic checkout with execution locks, first-class blocker dependencies, comments, documents, attachments, work products, labels, and inbox state. Search across work, review document revisions, and leave anchored feedback.

Heartbeat Execution — DB-backed wakeup queue with coalescing, budget checks, workspace resolution, secret injection, skill loading, and adapter invocation. Runs produce logs, usage records, and adapter-specific session state. Bounded recovery handles supported failures and surfaces cases that need human action.

Workspaces & Runtime — Project repositories and workspaces, optional isolated execution workspaces (git worktrees, operator branches), and runtime services (dev servers, preview URLs). Sandbox providers extend execution beyond the local host; availability depends on the configured environment and adapter.

Governance & Approvals — Board approval workflows, execution policies with review/approval stages, decision tracking, budget hard-stops, and agent pause/resume/terminate. Configured task reviews govern completion; connection action approvals govern calls through the tool gateway.

Budget & Cost Control — Token and cost tracking by company, agent, project, goal, issue, provider, and model. Scoped budget policies with warning thresholds and hard stops. Enforcement uses recorded spend; usage reporting and in-flight work can delay a stop.

Routines & Schedules — Recurring tasks with cron, webhook, and API triggers. Concurrency and catch-up policies. Each routine execution creates a tracked issue and wakes the assigned agent — no manual kick-offs needed.

Plugins — Instance-wide plugin system with out-of-process workers, capability-gated host services, job scheduling, tool exposure, and UI contributions. Extend Paperclip without forking it.

Secrets & Storage — Company secrets and per-person secret values, encrypted credential storage, local or S3-compatible file storage, attachments, and work products. Secret references supply credentials to authorized runs without copying values into ordinary agent configuration.

Activity & Events — Mutating actions, heartbeat state changes, cost events, approvals, comments, and work products are recorded as durable activity so operators can audit what happened and why.

Company Portability — Preview, export, and import organization packages with agents, skills, and optional projects, routines, tasks, and attachments. Referenced secret values are omitted; review packages before sharing because plain environment values and local paths can remain. Packages share an operating setup; full-instance recovery uses backups.


What Paperclip is not

Not just a chatbot. Conversations stay attached to tasks, plans, decisions, and outputs. Experimental Agent Chat can hand work off to assigned tasks.
Not an agent framework. We don't tell you how to build agents. We tell you how to run a company made of them.
Not just a workflow builder. Routines and experimental pipelines operate within an organization, with roles, goals, budgets, and governance.
Not a prompt manager. Agents bring their own prompts, models, and runtimes. Paperclip manages the organization they work in.
Not limited to one agent. Start with one agent and grow into a team with shared skills, delegation, and review.
Not only for code review. Coding and PR review fit alongside research, operations, content, and other work.

Quickstart

Open source. Self-hosted. No Paperclip account required. Follow the guided quickstart to set up your first agent.

Just ask your agent to install Paperclip

Share the installation guide with your agent.

Or install it yourself

With Node.js 24.11 or newer installed:

npx paperclipai@latest onboard --yes

The CLI runs from npm's cache; your instance configuration and data persist locally.

See the installation guide for managed installs, pinned versions, canary and git-ref installs, updates, rollback, service management, and uninstalling.

For an isolated manual test instance that is already initialized with a CEO agent, use test-drive. It stays in the foreground, never installs a service or creates a first task, and opens the browser only after setup succeeds:

ANTHROPIC_API_KEY=... npx paperclipai test-drive
OPENAI_API_KEY=... npx paperclipai test-drive --harness codex
OPENROUTER_API_KEY=... npx paperclipai test-drive \
  --harness opencode \
  --model openrouter/anthropic/claude-sonnet-4.5

Each run without --data-dir gets a unique, retained temporary directory; its absolute path is printed at startup. Pass --data-dir to reuse one, or --no-browser to leave the initialized instance unopened. When invoked from a linked Git worktree, test-drive also enables task execution in that worktree. See the test-drive guide for credential and reuse behavior.

Troubleshooting: private npm registry .npmrc

If this fails with an E404 for paperclipai (or similar) and you use a private npm registry (for example GitHub Packages) via a global ~/.npmrc, npx may be resolving paperclipai against that private registry instead of the public npm registry.

Diagnostic:

npm config get registry

Workaround (cross-platform; force the public npm registry for this command):

npx --registry https://registry.npmjs.org paperclipai@latest onboard --yes

That quickstart path now defaults to trusted local loopback mode for the fastest first run. To start in authenticated/private mode instead, choose a bind preset explicitly:

npx paperclipai@latest onboard --yes --bind lan
# or:
npx paperclipai@latest onboard --yes --bind tailnet

If you already have Paperclip configured, rerunning onboard keeps the existing config in place. Use npx paperclipai configure to edit settings.

Or manually:

git clone https://github.com/paperclipai/paperclip.git
cd paperclip
pnpm install
pnpm dev

This starts the UI and API at http://localhost:3100. An embedded PostgreSQL database is created automatically — no setup required.

Requirements: Node.js 24.11+, pnpm 9.15+

Local Claude and Codex subscription sign-in also needs Python 3 and the corresponding provider CLI on the Paperclip host. The Docker image includes them.

Source development also builds the native Paperclip Runner when enabled (the self-hosted default). Install a Rust toolchain, or set PAPERCLIP_RUNNER_BINARY to a compatible prebuilt runner.


FAQ

Q: Is this project maintained or just slop?

A: Paperclip is maintained by the Paperclip team. We've merged over 2,700 pull requests.


Q: What does a typical setup look like?

A: Locally, a single Node.js process manages an embedded Postgres and local file storage. For production, point it at your own Postgres and deploy however you like. Configure projects, agents, and goals — the agents take care of the rest.

For remote access, use authenticated mode with a private-network bind such as Tailscale, or deploy the persistent server with Docker. See deployment modes and the Docker guide.


Q: Can I run multiple companies?

A: Yes. A single deployment can host multiple organizations with company-scoped data and access checks.


Q: How is Paperclip different from agents like OpenClaw or Claude Code?

A: Paperclip uses those agents. It orchestrates them into a company — with org charts, budgets, goals, governance, and accountability.


Q: Why should I use Paperclip instead of just pointing my OpenClaw to Asana or Trello?

A: Agent orchestration has subtleties in how you coordinate who has work checked out, how to maintain sessions, monitoring costs, establishing governance - Paperclip does this for you.

(Bring-your-own-ticket-system is on the Roadmap)


Q: Do agents run continuously?

A: Agents wake for assigned work and follow-up messages. Optional timer heartbeats let them check for work periodically; routines create recurring tasks on their own schedules. You can also connect externally running agents such as OpenClaw. A mention alone does not assign work or wake another agent.


Development

pnpm dev              # Full dev (API + UI, watch mode)
pnpm dev:once         # Full dev without file watching
pnpm dev:server       # Server only
pnpm dev:mobile       # Serve prebuilt UI on :3101 for phones/tablets (proxies /api → :3100)
pnpm dev:both         # Run `pnpm dev` and `pnpm dev:mobile` together
pnpm build            # Build all
pnpm typecheck        # Type checking
pnpm test             # Cheap default test run (Vitest only)
pnpm test:watch       # Vitest watch mode
pnpm test:e2e         # Playwright browser suite
pnpm db:generate      # Generate DB migration
pnpm db:migrate       # Apply migrations

pnpm test does not run Playwright. Browser suites stay separate and are typically run only when working on those flows or in CI.

See doc/DEVELOPING.md for the full development guide.


Roadmap

  • ✅ Plugin system (e.g. add a knowledge base, custom tracing, queues, etc)
  • ✅ Get OpenClaw / claw-style agent employees
  • ✅ companies.sh - import and export entire organizations
  • ✅ Easy AGENTS.md configurations
  • ✅ Skills Manager, Skill Studio & Skills Store
  • ✅ Scheduled Routines
  • ✅ Better Budgeting
  • ✅ Agent Reviews and Approvals
  • ✅ Multiple Human Users
  • ✅ Cloud / Sandbox agents (e2b, Cloudflare, Daytona, Modal, Novita, self-hosted Kubernetes)
  • ✅ Artifacts & Work Products
  • ✅ Deep Planning (planning mode, revisioned plans, plan approvals)
  • ✅ Enforced Outcomes (watchdogs, recovery actions, review gates)
  • ✅ MCP Tool Gateway & Apps (governed tool access)
  • ✅ Secrets Manager with per-agent access
  • ✅ Activity log & action attribution
  • ✅ Self-healing runs & automatic recovery
  • ✅ Agent evals & feedback
  • ✅ Connected Apps
  • ✅ Personal & Shared AI Accounts
  • ✅ Shared Agents Use Personal GitHub Identities
  • ✅ Skill Version History & Restore
  • ✅ Document Comments & Revision History
  • ✅ Company-Wide Search
  • ✅ Multi-Model & Multi-Harness Teams
  • 🟡 Memory / Knowledge
  • ⚪ MAXIMIZER MODE
  • ⚪ Work Queues
  • ⚪ Self-Organization
  • ⚪ Automatic Organizational Learning
  • 🟡 Agent Chat
  • 🟡 Cloud deployments
  • ⚪ Desktop App
  • ⚪ Bring-your-own-ticket-system (Asana / Linear / Jira as on-ramps)

This is the short roadmap preview. See the full roadmap in ROADMAP.md.


Community & Plugins

Find Plugins and more at awesome-paperclip

Observability

Paperclip ships with opt-in OpenTelemetry auto-instrumentation for the server (traces only). It activates when OTEL_EXPORTER_OTLP_ENDPOINT is set and supports grpc, http/protobuf, and http/json via the standard OTEL_EXPORTER_OTLP_PROTOCOL env var. @opentelemetry/api is a normal server dependency; the SDK, auto-instrumentation, and exporter packages are optional peer dependencies — install them only if you want tracing. See doc/observability.md for install commands and the full env-var reference.

Paperclip also ships with opt-in Sentry error monitoring for the server and the browser. Set SENTRY_DSN_FRONTEND to activate it for the browser and SENTRY_DSN_BACKEND to activate it for the server — each variable is optional, and the legacy SENTRY_DSN variable still works as a fallback for either component. The supported server SDK version is @sentry/node@10.71.0; it is an optional peer dependency for the server, so install it only if you want error monitoring. The browser SDK, @sentry/browser, is pinned to the same exact version. See doc/observability.md for the install command, the privacy settings, and the full default capture set.

Telemetry

Paperclip collects anonymous usage telemetry to help us understand how the product is used and improve it. No personal information, issue content, prompts, file paths, or secrets are ever collected. Private repository references are hashed with a per-install salt before being sent.

Contributors changing emitted telemetry events should follow the Telemetry Data Contract. For proposed first-party events that are not in the generated contract yet, follow Telemetry Workflow.

Telemetry is enabled by default and can be disabled with any of the following:

Method How
Environment variable PAPERCLIP_TELEMETRY_DISABLED=1
Standard convention DO_NOT_TRACK=1
CI environments Automatically disabled when CI=true
Config file Set telemetry.enabled: false in your Paperclip config

Contributing

We welcome contributions. See the contributing guide for details.

We're hiring


Community


License

MIT © 2026 Paperclip Labs, Inc

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