DottaandClaude Fable 5.2
8ab955776d
<!-- Write all pull request text in Simplified Technical English (ASD-STE100): short sentences, one instruction per sentence, simple approved vocabulary, and the active voice. --> ## Thinking Path > - Paperclip is the open source app people use to manage AI agents for work. > - An agent can review a GitHub pull request. After the agent publishes the review, the task waits in review for the next GitHub reply. A later run of the same task can inherit this wait. > - Issue lists, issue summaries and inbox attention show these waits. For each waiting task, the server first checks the requester provenance of the waiting run (`chatRunSourceMessageId`). This check is an authorization check: it walks the identity-context lineage of the run and rejects a changed requester, another task, another company or another provider. > - #20673 batched the review reads and the publication reads of one list request. It kept the provenance check as one call for each waiting run, inside `Promise.all`. > - Thus a request with N waits still built and sent at least 2N statements at the same moment. The database is fast for each statement, but the server builds each statement on its main thread. > - On a hosted instance, a CPU profile of the server showed the main thread at 96% of one core. SQL text building, per-statement timers, socket writes and garbage collection had the largest shares. Connection samples showed 235 to 292 pool connections whose last statement was the source-message link read of this check, and 53 to 112 whose last statement was its identity-context read. > - This pull request checks the provenance of all the waiting runs of one request in one batch. The runs that need the same kind of read share one statement. Each run gets the same decision as a separate call, from the same walk and the same reads. > - The benefit is that a list request with 250 waits sends 17 statements, not 658, and uses about one quarter of the CPU time. The statement count no longer follows the number of waits. ## Linked Issues or Issue Description Refs #20673 (batched the review reads and the publication reads of the same request; this change does the same for the provenance reads that #20673 kept for each run). Refs #20551 (authorizes the waits of one issue page with one read). **What happened** `readInheritedGitHubReviewWaits` called `chatRunSourceMessageId` one time for each waiting run, inside `Promise.all`. Each call sent an identity-context read and a source-message link read. A run that continues an earlier run also sent a parent-context read and a source-run read for each continuation in its lineage. `listExternalReviewWaits` calls this function for the issue list, the issue summaries and the inbox attention of a company, so each of those requests repeated the same reads for every waiting task. Evidence from a hosted instance (sanitized): - CPU profile of the server process, 30 s, 5 ms samples: main thread at 96% of one core. Building SQL text for select statements: 17.7% inclusive. The idle-timer restart that the database client does for each statement: 7.6%. Socket writes: 6.2%. Garbage collection: 13%. - Three samples of the pool connections (about 494 connections). 235 to 292 connections had the source-message link read of `chatRunSourceMessageId` as their last statement. 53 to 112 connections had its identity-context read as their last statement. - The `DISTINCT ON` review read that #20673 added ran about 29 times each minute. It runs one time for each list request, so each request started hundreds of provenance reads. **Expected behavior** One list request sends a number of provenance statements that does not follow the number of waiting tasks. The decision for each run does not change. **Steps to reproduce** 1. Put many tasks of one company in review, each with an inherited GitHub review wait. 2. Open the issue list or the inbox. 3. Count the statements that the server sends. The identity-context read and the source-message link read run one time for each waiting task. The benchmark below does these steps on a local PostgreSQL. ## What Changed - `server/src/services/chat-channels/run-origin.ts`: - The provenance decision is now one walk (`sourceMessageWalk`). The walk asks for one read at a time and decides on the returned rows only. Its checks, their order and its error messages are the checks of the previous `chatRunSourceMessageId`. - The five reads of the walk (active identity context, source-message link, board message, parent context, source run) are defined one time in `provenanceReads`. Each definition takes its values either as literals or as SQL columns. - `chatRunSourceMessageId` (single run) drives the walk with one statement for each read, as before. The joins, the company scope and the filters are the same. The identity-context reads now select the seven columns that the walk uses instead of every column. - New `chatRunSourceMessageIds(db, runs, options)` (batch). It drives one walk for each run. In each round, the runs that wait for the same kind of read share one statement: `unnest(...)` of the request values, `CROSS JOIN LATERAL` the same single-row read with the request columns in place of the literals. Thus each run gets the row that its own single read returns, with `LIMIT 1` and the company scope on each join. The result for each run is `{ messageId }` or `{ forbidden }` (the 403 that the single call throws). A database error rejects the whole batch, as `Promise.all` did. - `server/src/services/chat-channels/github/review-wait.ts`: `readInheritedGitHubReviewWaits` checks the provenance of its runs with the batch. A 403 still gives `revoked`. The single-run `readInheritedGitHubReviewWait` does not change. - `server/src/services/chat-channels/run-origin.test.ts`: two new tests (see Verification). - `server/src/__tests__/github-review-wait.test.ts`: one new regression test for the statement count. - `server/scripts/benchmark-review-wait-provenance.ts`: the reproduction and the before/after measurement. It loads the pre-fix modules from `--baseline-ref` and fails unless both versions return the same waits. - `scripts/benchmark-github-review-wait.ts`: removed an assertion that `run-origin.ts` is byte-identical to its baseline. Both sides of that benchmark import the current helper, so the comparison stays fair. Without this change, the documented command of that benchmark fails. - `doc/DATABASE.md`: describes the batch provenance check and the new benchmark command. Statement count of one batch: the walk of one run does at most 3 + 4L reads, where L is the number of continuations in its requester lineage. The batch does the same number of rounds for its longest lineage. Each round sends at most one statement for each kind of read (5 kinds), for any number of runs. A request whose waiting runs accepted their GitHub comment directly sends 2 provenance statements. ## Verification Red/green regression test (`server/src/__tests__/github-review-wait.test.ts`): - `reads the waits of forty runs with the statements of four runs`. Before the fix: `expected { few: 10, many: 82 } to deeply equal { few: 4, many: 4 }`. After the fix: passes. Equivalence tests (`server/src/services/chat-channels/run-origin.test.ts`), on real PostgreSQL: - `gives every run of a batch the decision of its own check, in every provenance state`. 33 runs, each in a different provenance state, are checked with the single function and with the batch for five option sets (no options, legacy wake allowed, GitHub, GitHub with legacy wake, Slack). The test compares the decision of each run (message, `null`, or the 403 and its message) for: the full batch, the reversed batch, and a batch of one run. The states include the negative cases: a run of another company, a message that is linked only in another company, a message that is linked to another task, a conversation that moved to another task, a continuation on another task, a changed requester (run, identity context, continuation, board message), a link of another provider, a missing link, an outbound link only, a deleted message, an agent message, a rejected or pending identity context, a missing parent, another agent and a lineage cycle. The test also states the expected GitHub decision for each state, so the comparison cannot pass on two wrong answers. - `checks the runs of a batch with statements that follow the lineage, not the number of runs`. 4 runs and 40 runs each send 2 statements. 40 separate checks send 80. One continuation in each lineage gives 4 statements for the batch. - A mutation check: when the batch gives a row to the wrong request, both tests fail. Commands: - `pnpm vitest run server/src/services/chat-channels/run-origin.test.ts server/src/__tests__/github-review-wait.test.ts` - Focused run of 15 test files that import the changed modules, their callers or the shared seed helper, including `external-chat-wait.integration.test.ts`, `chat-github-tools.test.ts`, `slack-authority.test.ts` and `issue-list-review-wait-authorization.integration.test.ts`: 482 tests passed. In `idle-completed-history.test.ts`, `afterEach` hooks (a `TRUNCATE` of the test database) timed out at 30 s for 1 test in this run and for 3 tests in a second run of the file, while other agents loaded the machine. No assertion failed. The three tests of that file that reach the changed code pass when they run alone. CI runs the full file. - `server/src/__tests__/chat-channels.integration.test.ts` also uses the single check. It was not run locally (very large file). CI runs it. - Typecheck of the changed files and of the callers of `chatRunSourceMessageId` with a focused `tsc` program: no errors in these files. The full server typecheck was not run locally (shared machine). CI supplies it. - The first CI run of this pull request failed `ci / policy`: the module boundary check rejects an insert into the agents table outside the agent lifecycle module, and the benchmark seeded its fixture agent that way. The second commit seeds that agent with SQL. `pnpm check:module-boundaries` passes locally, and the benchmark gives the same statement counts. - `pnpm verify` returns exit 2 (broader verification required) because the change adds a file under `server/scripts/`. CI supplies the broad coverage. Before/after measurement: ```sh pnpm --filter @paperclipai/server exec tsx scripts/benchmark-review-wait-provenance.ts --baseline-ref=e2cf9a670fc5d5f5058fd0972beb71650f77c554 ``` One call of `listExternalReviewWaits` (the read of one issue list request). Embedded PostgreSQL 18.1, Node v26.4.0, `VACUUM ANALYZE` after seeding. One company, one agent, one GitHub repository. Every wait has a published review. Every tenth waiting run continues three earlier runs. 3 warm-up calls, then 15 pairs in alternating order. Both versions return the same waits and the same inherited wait for each run. | Waits | Version | Statements | SQL text bytes | Wall ms, median (min–max) | CPU ms, median (min–max) | | --- | --- | --- | --- | --- | --- | | 20 | before | 60 | 31,084 | 14.1 (12.8–19.6) | 13.7 (8.7–39.3) | | 20 | after | 17 | 15,847 | 12.5 (9.4–22.7) | 8.8 (4.1–87.1) | | 250 | before | 658 | 300,611 | 63.3 (58.1–89.6) | 78.1 (64.8–103.3) | | 250 | after | 17 | 17,171 | 20.8 (18.9–29.2) | 18.7 (14.1–27.3) | | 1000 | before | 2,608 | 1,179,710 | 256.0 (221.0–363.2) | 303.3 (262.2–507.8) | | 1000 | after | 17 | 21,695 | 54.9 (46.7–81.7) | 51.0 (41.6–72.2) | Of the 658 statements before the fix at 250 waits, 650 are provenance reads: 325 identity-context reads, 250 source-message link reads and 75 source-run reads. After the fix, 9 statements do the same reads. The other 8 statements of the request do not change. Sizes are set with `WAITS=20` and `WAITS=1000`. Other agents used the same machine during the measurement, so the maximum values have noise. - The batch statements use index scans only (`run_identity_contexts_pkey`, `run_identity_contexts_company_run_idx`, `chat_message_links_company_comment_idx`, `chat_conversations_pkey`, `chat_endpoints_company_id_uq`, `heartbeat_runs_pkey`). The 9 batch statements of a request with 250 waits take about 1 ms of server execution in total. - Wall time and CPU time are almost equal before the fix. This shows that the cost is in the server process, not in the database. Not verified: - The effect on the hosted instance. This pull request does not deploy. The share of the main-thread CPU that this path uses there is not known; the profile gives the share of all statement building. ## Risks - The provenance check is an authorization check. The risk is a batch decision that differs from the single decision. Mitigation: both paths run the same walk and build each read from the same definition. The batch read is the single read inside `CROSS JOIN LATERAL`, for each request row. The equivalence test compares both paths in every state. - A database error (for example an invalid UUID in a legacy task binding) rejects the whole batch. The previous `Promise.all` also rejected the whole request. The callers of the batch pass the native task of each run. - The statement count of the batch follows the longest lineage of its runs. A batch with one very long lineage sends as many statements as that run sent before, in sequence. It does not send more. - The batch sends its statements in sequence on one connection. Before, the reads of N runs used up to N pool connections at the same moment. - The single-run callers (GitHub tools, review publication, response comments, Slack authority, the finalizer, the status committer) keep one statement for each read. Their identity-context reads select fewer columns. - No migration. No new index. > For core feature work, check [`ROADMAP.md`](ROADMAP.md) first and discuss it in `#dev` before opening the PR. Feature PRs that overlap with planned core work may need to be redirected — check the roadmap first. See `CONTRIBUTING.md`. ## Model Used - Claude Fable 5.2 (`claude-fable-5-2`), Anthropic, used through Claude Code with tool use (code reading, editing, shell, tests). ## 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 - [x] All Paperclip CI gates are green - [x] 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: Claude Fable 5.2 <noreply@anthropic.com>
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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 |
 Claude Code |
 Codex |
 Cursor + Cloud |
 Gemini CLI |
 OpenCode |
 Pi |
 Hermes + Gateway |
 Grok Build |
 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.
| 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 AgentAny agent, any runtime, one org chart. If it can receive a heartbeat, it's hired. |
🎯 Goal AlignmentLink tasks and projects to your organization goals. Agents receive the goal context behind their work. |
💓 HeartbeatsAgents wake for assigned work, follow-up messages, or configured schedules. Delegation flows up and down the org chart. |
💰 Cost ControlCompany, agent, and project budgets. Track reported spend, get threshold alerts, and pause work at configured limits. |
🏢 Multi-OrganizationOne deployment, many organizations. Separate tasks, agents, permissions, and activity histories for each. |
🎫 Task ThreadsKeep conversations, plans, blockers, files, and run history attached to the work. Assign tasks to agents or people. |
🛡️ GovernanceConfigure review and approval stages, approve hires, and pause, reassign, or stop work when needed. |
📊 Org ChartHierarchies, roles, reporting lines. Your agents have a boss, a title, and a job description. |
📱 Mobile ReadyMonitor and manage your autonomous businesses from anywhere. |
🔗 Apps & ConnectionsConnect services such as GitHub, Notion, and Railway, or your own MCP server. Set gateway actions to Allowed, Ask first, or Off. |
👥 Shared Agents, Personal AccountsChoose 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 StudioInstall or write shared skills, test them with saved inputs, inspect results, and restore earlier versions. |
📅 Scheduled RoutinesRun recurring work on a schedule or trigger it through an API or webhook. Each run has a task, an owner, and a history. |
📎 Artifacts & FeedbackFind the files and documents agents produce. Preview supported formats and leave comments on specific passages in documents. |
📦 Ready-Made TeamsPreview 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
.npmrcIf this fails with an
E404forpaperclipai(or similar) and you use a private npm registry (for example GitHub Packages) via a global~/.npmrc,npxmay be resolvingpaperclipaiagainst that private registry instead of the public npm registry.Diagnostic:
npm config get registryWorkaround (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.
Community
- Discord — Join the community
- Twitter / X — Follow updates and announcements
- GitHub Issues — bugs and feature requests
- GitHub Discussions — ideas and RFC
License
MIT © 2026 Paperclip Labs, Inc
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Open source under MIT. Built for people who want to get work done, not babysit agents.