DottaandClaude Opus 5.5 f8e1fb0278 perf(docker): take the orphan-reaping pull and pack chmod off the image path (#21013)
## Thinking Path

> - Paperclip is the open source app people use to manage AI agents for
work
> - `docker.yml` builds and signs the standard Core image for every
master push. Paperclip Cloud deploys a Core commit only after this image
is published and attested.
> - A master commit takes 29 min (p50) and 56 min (p90) to reach a
published image. Every minute here delays a staging fix.
> - The run logs show where the time goes. The exact-source gate (about
14 min) is the critical path today. The build legs finish about 3 min
before it. After the gate, `merge-and-push` spends about 95 s to pull
the 4.9 GB amd64 image for one probe.
> - This pull request moves that probe into the build legs, which run
before the gate. It also removes a duplicate 187 MB layer from the amd64
image.
> - The benefit is about 95–100 s less from gate to signed image on
every master commit, with the same proof. arm64 is now also probed.

## Linked Issues or Issue Description

**What existing behavior does this improve?**
The time from a commit landing on master to the signed standard image
(`ghcr.io/paperclipai/paperclip:sha-<full>` plus its attestation).

**Subsystem affected**
`.github/workflows/docker.yml`, `Dockerfile`,
`scripts/standard-image-contract.mjs`.

**Current behavior**
Measured on run 43133035209 (sha 42fd790e, total 999 s):

- Push 01:13:47. The slowest exact-source job, `General tests (server
(7/10))`, ends 01:27:08. `Cloud source verified v1` waits 20 s for a
runner and ends 01:27:44. The watcher sees it at its next 30 s poll,
01:27:58.
- `build-and-push (amd64)` ends 01:24:52 and arm64 ends 01:21:59. Both
have slack before the gate. Run 43134250912 shows the same shape: the
gate opened 01:44:37 and amd64 ended 01:41:53.
- `merge-and-push` starts 01:28:03 and pushes the manifest at 01:28:34.
Then "Verify PID 1 reaps orphaned processes" pulls the full amd64 image
(01:28:34 to 01:30:09, 95 s). "Verify the immutable standard image" runs
the same probe again on the same bytes (5 s). The attestation ends
01:30:22.

Last 60 master pushes (2026-11-11 20:17Z to 2026-11-12 03:21Z): 43
cancelled or superseded, 14 succeeded, 2 failed, 1 in progress. Push to
first published image that contains the commit: p50 29.4 min, p90 55.5
min, max 85.6 min. A successful run takes 22.5 min (p50) from push. The
median gap between pushes is 4.5 min.

Where the amd64 "Build and push by digest" step spends its 582 s:

| Time | Phase | What happens |
| --- | --- | --- |
| 0–8 s | Load | Dockerfile frontend, cache manifest import |
| 8–54 s | Pull cached layers (46 s) | `deps`, `rust-chef`,
`runner-deps`, `runner-build` and `base` are **all cache hits**. The
`build` stage starts `FROM runner-build` and copies `deps`, so BuildKit
downloads and extracts them: a 932 MB node_modules layer (13 s download,
24.5 s extract) and about 650 MB of Rust `target/` |
| 54–108 s | Per-commit build (54 s) | `COPY . .` 10 s, mtime
normalization 11 s, UI 5 s, plugin SDK 3 s, server tsc 18.5 s, runtime
bundle 5.6 s |
| 108–206 s | `runner-provider-pack` (98 s) | Rebuilt every commit: it
is `FROM build` and stamps the commit. pnpm install about 25 s, pack 9
s, **Kimi smoke 37 s (amd64 only)**, archive 28 s |
| parallel | `production` | Tool layer is a cache hit but pulls 1.11 GB
(11 s). `COPY --from=build /app` 24 s |
| 206–294 s | Pack copy and chmod | `COPY` pack 7.5 s. `chmod -R a+rX`
plus UID check **71 s** (arm64: 20.5 s) |
| 294–420 s | Image export (126 s) | 115 s compressing new layers:
`/app` 1.40 GB, pack 1.01 GB, pack archives 1.06 GB, chmod copy 0.19 GB
(compressed). Push 10 s |
| 420–582 s | Cache export (172 s) | `mode=max`: 152 s "preparing"
(compressing the per-commit layers of `build` and
`runner-provider-pack`, which no later commit can reuse), 20 s upload |

What rebuilds every commit, and why: only the `build` stage and
everything after it. `PAPERCLIP_BUILD_COMMIT` is already declared after
the dependency layers, `CLI_TOOLS_CACHE_EPOCH` is weekly, and dependency
install already copies manifests first. The commit ARG is used by the UI
build, the server stamp, and the provider pack, so those must rebuild. A
pnpm `--mount=type=cache` gives nothing here: the runners are ephemeral,
and cache mounts are not exported to the registry cache.

Why amd64 is slower than arm64: both legs run on runs-on AWS 32-core
instances (m6a.8xlarge and m6g.8xlarge), not on `ubuntu-latest`. The
difference is content, not CPU. amd64 has a larger provider pack with
the Kimi smoke test (37 s), a 187 MB chmod copy layer (arm64: 0 MB), a
4.9 GB image (arm64: 2.9 GB), and a 172 s cache export (arm64: 100 s).

**Proposed behavior**
Run the PID 1 probe in each build leg, on its own architecture, against
the digest that leg pushed. Before signing, require that every Linux
image in the standard index is one of those probed digests. Set the
provider pack modes in the stage that writes the pack.

**Reason and benefit**
The probe pull leaves the post-gate path. The leg has about 3 min of
slack before the gate. The chmod change removes about 50 s and 187 MB
from the amd64 leg, which offsets most of the probe pull that the leg
now does.

**Breaking changes**
None. Tags, digests, attestation subject, and signer identity are
unchanged.

**Additional context**
The concurrency proposal and the build-in-PR plan are below, in "Risks".
They are not implemented.

## What Changed

- `docker.yml` `build-and-push`: new step "Verify PID 1 reaps orphaned
processes". It runs `scripts/assert-orphan-reaping.sh` in
`ghcr.io/...@<pushed digest>` with `--platform` set to the leg platform.
It runs before "Export digest", so `merge-and-push` only receives probed
digests.
- `docker.yml` `merge-and-push`: removed the two `docker run` probes
(tag and digest). "Resolve standard image digest" now runs
`standard-image-contract.mjs --resolve "$GITHUB_SHA" --verified-digests
/tmp/digests`.
- `standard-image-contract.mjs`: `--verified-digests <dir>` reads each
probed leg digest from the registry, checks that its bytes hash to that
digest, and collects its Linux image digests. It fails unless every
Linux image in the standard index is in that set. Registry content is
addressed by digest, so this proves that the signed index names exactly
the probed bytes, without a second pull.
- `Dockerfile`: `chmod -R a+rX /provider-pack` now runs in the same
`RUN` that writes the pack. `COPY` keeps the modes. The production stage
keeps the readability check as UID 65534.
- Tests: new contract tests (membership, bare manifest legs, digest
mismatch, empty set, lookup by digest). New workflow test: the probe
runs between the build and "Export digest", and the publisher does not
`docker run`. Updated the provider-pack Dockerfile test.
- `doc/standard-image-contract.md`: describes the per-leg probe and the
membership check.

Expected savings, per master commit:

| Change | Saving | Confidence |
| --- | --- | --- |
| Probe moved out of `merge-and-push` | 95–100 s from gate to
attestation (measured: 95 s pull + 5 s second probe) | High |
| Pack chmod in its own stage | About 50 s and 187 MB in the amd64 leg
(71 s vs 20.5 s on arm64, where the chmod layer is empty) | Medium-high
on size, medium on seconds |
| Net amd64 leg | About +10 to +45 s (probe pull estimated at 60–95 s,
minus about 50 s). Still before the gate | Medium |

## Verification

- `node --test scripts/standard-image-contract.test.mjs`: 10 pass (2
new).
- `node --test` on
`.github/scripts/tests/{cloud-readiness,docker-canary-promotion,post-merge-runner-routing,lockfile-refresh-workflows}.test.mjs`,
`scripts/__tests__/release-verify-workflow.test.mjs`,
`scripts/reuse-canary-image.test.mjs`,
`scripts/standard-image-contract.test.mjs`,
`scripts/cloud-source-verification.test.mjs`: 484 pass.
- `vitest run server/src/__tests__/docker-build-stamp.test.ts
server/src/__tests__/container-init-reaping.test.ts`: 22 pass. Red
check: with the `origin/master` `Dockerfile` and `docker.yml`, the 2 new
or changed assertions fail.
- Real registry, read only: `node scripts/standard-image-contract.mjs
--resolve 42fd790e80 --verified-digests
<dir>` with the two leg digests from run 43133035209 returns the
published index digest. With only the arm64 leg digest, it fails:
"linux/amd64 image sha256:0f0642e6… did not pass the build-leg
orphan-reaping probe".
- `actionlint .github/workflows/docker.yml`: the same 6 shellcheck
findings as `origin/master`, none new.
- Shell syntax check (`sh -n`) of the changed pack `RUN`.

Not exercised: the workflow itself (it runs only on master pushes, and I
did not dispatch it). No local `docker build`: the Docker daemon is not
running on this machine, and a full build does not fit a few minutes.
The leg probe pull time is an estimate from the publisher runner.

## Risks

- The leg now pulls its own image (4.9 GB amd64). AWS legs have about
175 GB free. Hosted tag builds run "Free runner disk" first, and the old
publisher pulled the same image on a hosted runner for tag builds.
- If the registry returns a leg index in an unexpected form, the resolve
step fails closed and no master image is signed. The real-registry check
above used today's exact shapes (OCI index with one platform image and
one attestation manifest per leg).
- Tag builds (`v*`, `nightly/v*`, `beta/v*`) keep the probe, now per
leg. They have no standard-image signing step, and the manifest list is
made only from probed digests.

### Deliberately not changed

- **The exact-source gate.** It is a deliberate "never publish an
unverified image" rule (`watch-release-source.mjs --require-proof` and
`--check-proof`, `doc/cloud-build-readiness.md`). I kept it. The owner
levers are: the slowest `cloud-readiness` shard (`server (7/10)`, 12
min, sets the gate), the 20 s runner wait for `Cloud source verified
v1`, and the watcher's 30 s poll (15 s average delay). I did not shorten
the poll because the org API rate limit was exhausted today.
- **Cache export (172 s amd64, 100 s arm64).** `mode=max` compresses
about 2 GB of per-commit pack layers every build. A fix: a separate
cache-only build of a small aggregate stage (`deps`, `runner-build`, the
production tool layer) with `mode=max`, and the main build with
`cache-from` only. Not done here: the legs are not on the master
critical path today, and I cannot measure it without a master run. It is
worth doing for tag builds or if the gate gets faster.
- **Cached layer pull (46 s).** The `build` stage starts `FROM
runner-build`, so it downloads about 650 MB of Rust `target/` that it
deletes at the end. The server build's `prepare:runner-vendor` uses that
target, so this needs a separate change.
- **Provider pack per commit (98 s amd64).** It rebuilds because it is
`FROM build` and stamps the commit. Its 1.06 GB transport archives also
duplicate the 1.01 GB pack inside the image. Keying the pack on its own
inputs is a product-contract change.

### Concurrency proposal (not implemented)

Today one run holds `docker-${{ github.ref }}` for 17–23 min. Most of
that is waiting for the gate, not building. A newer commit waits for
that run before its build starts (run 43134250912 waited 6.4 min).
Proposal:

1. Remove the workflow-level group. Add job-level groups:
`build-and-push` uses `docker-build-${{ github.ref }}-${{ matrix.arch
}}` with `cancel-in-progress: false`. At most one build per architecture
runs and the newest commit replaces the pending one. So not every commit
is built.
2. `merge-and-push` uses `docker-publish-${{ github.ref }}` with
`cancel-in-progress: false`. It skips publication when a newer master
commit already has a signed image, so the image order stays monotonic.
3. A superseded run must not keep its source watcher alive, or the API
load grows with each push. Fold the early watcher into the gate job, or
end it when its build job is cancelled.

Effect: the newest commit starts building at push, so its image
publishes at about gate time plus 45 s, instead of after the previous
run ends. That removes the 0–17 min queue wait, which is most of the gap
between the p50 of 29 min and the gate time of about 14 min.

### Plan: build in PR CI and promote on merge (not in this PR)

What must change:
1. Turn on the GitHub merge queue for `master`. The queue tests a
`gh-readonly-queue/...` commit and then fast-forwards `master` to that
exact commit, so the SHA stamped in the image (`PAPERCLIP_BUILD_COMMIT`,
`build-info.json`, the provider pack `runnerSourceRevision`, the UI
bundle) is the final master SHA.
2. On `merge_group`: build both architectures with **no secrets and no
registry credentials**, because the code under test is not yet on
master. Write each image as an OCI layout artifact. A second job that
runs no PR code pushes those artifacts by digest to a staging
repository, keyed by the merge-group SHA.
3. Run `cloud-readiness` on `merge_group` for the same SHA, so
exact-source proof exists before merge.
4. On the master push, `docker.yml` looks up the staged digests for
`github.sha`, checks that the tree and SHA match, retags them as
`sha-<full>`, and signs with the same identity
(`docker.yml@refs/heads/master`). If no staged image exists, it falls
back to the current build.

Guarantees to keep: never publish or sign an image without exact-source
proof for that SHA. The signer identity and the attested digest stay the
same. No secrets in any job that runs non-CODEOWNER code. The PID 1
probe runs on both architectures before signing. Superseded queue
entries do not publish.

Expected saving: from master push to signed image, about 1–2 min
(lookup, retag, attest) instead of 29 min p50 and 56 min p90 today. The
work moves before the merge, so the merge itself waits for the queue
(about the current gate time of 14 min). A staging fix then reaches
Cloud about 25 min sooner at the median.

## Model Used

- Claude Opus 5.5 (`claude-opus-5-5`), Anthropic, used via Claude Code
with tool use (shell, file edits, read-only GitHub API and registry
reads). Default reasoning mode.

## 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)
- [x] 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
- [ ] I will address all Greptile and reviewer comments before
requesting merge

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

Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
2026-11-12 05:29:11 -06:00
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Paperclip is the app people use to manage AI agents for work.

Quickstart · Docs · GitHub · Discord · Twitter · Website

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Sign up for the Paperclip Cloud waitlist →


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 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, boxd, 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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