Squidbrake

Change control for AI agents. Your agents run commands, edit code, send emails, issue refunds and change databases. Squidbrake keeps the record of every one of those changes: which agent, what it changed, who approved it, and what led to it. Risky changes wait for someone other than the requester to sign off; routine ones run on their own and are still recorded. The record is tamper-evident, readable by people who don't write code (a weekly summary, an evidence pack for your auditor), and destructive changes keep an undo. One policy for every agent your company runs (Claude Code, Cursor, Codex, Gemini CLI, VS Code Copilot, Antigravity and your MCP tools), and a record that sits outside them: the agents can't quietly change it, or switch Squidbrake off, without a person.

Free and open source (Apache 2.0). Runs on your laptop or your own server. What your agents do (commands, files, prompts, the audit trail) never leaves it; usage counts go out only if you say yes when it asks (Enter means yes).

• A second person signs off: risky changes wait in the dashboard, on your phone (one-tap links, push via ntfy) or in Slack. The approver sees what led to it, e.g. the email the agent just read.
• Shows what it will change, and keeps an undo: before a risky command, the approver sees it measured on the developer's machine ("removes 3 commits from origin/main: fix login, ...", "deletes 1,204 files (56 MB) in data", "deletes 4,312 rows of 10,240 in orders", "destroys 12 resources in workspace prod, including 1 that holds data: aws_db_instance.main", "deletes the bucket and all 8,200 objects (14.0 GB)"). Database rows are counted with the same WHERE in a read-only session with a timeout (psql, mysql, sqlite3); terraform is read from its state or a plan without refresh or lock; AWS and Kubernetes use list and describe calls with the command's own credentials. Right before an approved delete, git reset --hard or git clean runs, Squidbrake keeps a copy; squidbrake undo lists them and squidbrake undo ID puts one back.
• A record someone else can read: every Monday a short summary of what the agents did and who approved what goes to Slack (when Slack is connected). squidbrake evidence (or Reports → Evidence pack) writes one printable page: the controls in place, what was blocked, held and approved and by whom, whether the audit trail is intact, and which SOC 2, ISO/IEC 42001, EU AI Act, CERT-In and RBI requirements those records speak to.
• For teams: one gateway for everyone's laptops (connect all --url https://gateway.yourcompany.com --key gw_..., one agent key per person, made in Team with "Works for" set), roles (only finance approves wires), second-person approval (nobody approves what their own agent asked for), an emergency stop (all agents, one agent, or one conversation, which also ends Claude Code's turn), reports, CSV export, and evidence anyone can verify offline (python verify.py).
• Works with real agents: squidbrake connect all connects Claude Code, Cursor, Codex, Gemini CLI, VS Code Copilot and Antigravity (their commands, reads and edits, via hooks) and the MCP servers they already use; any MCP app (Stripe, GitHub, Slack, databases, internal tools) can be wrapped too.
• Agents can't switch it off: an agent removing, stopping or uninstalling Squidbrake waits for a person. For a company, squidbrake lockdown --url https://gateway.yourcompany.com writes the managed-settings files IT pushes to every machine (Claude Code, Codex, Gemini CLI, Cursor), so each agent must run Squidbrake's hook and --dangerously-skip-permissions / --yolo are turned off; only someone with admin rights on the machine can undo that. One policy, every agent.
• Rules, not vibes: rules.yaml says what runs by itself, what's blocked, and what waits for a person. No LLM in the decision path.
• Reads what a command really does: ls && rm -rf ~/, bash -c "...", rmdir /s /q d:\ or curl ... | sh are split and read before they run. Wiping a disk or home folder is blocked; git push --force, terraform destroy, kubectl delete or cloud deletes wait for a person; commands that only look (ls, git status) run without asking.
• Catches prompt injection without a model: if an agent sends data to an address that only a web page, email or issue mentioned (not you, not your own systems), it's held and the approver is told where the address came from.
• Judges by history: blocks a retry of something a person rejected, catches look-alike domains (acrne-corp.com pretending to be acme.com), flags duplicate refunds, and lets you write sequence rules ("deleting a database right after its backups were turned off") that say which earlier step caused them.
• Fails closed: if Squidbrake is down, guarded tools don't run.

See SHOWCASE.md for a 5-minute demo with a sandbox company, and incidents/ for 10 real AI-agent incidents replayed against the shipped rules (Replit, the Railway volume deletion, GitHub MCP, Supabase MCP, Postmark MCP, Claude Code and Antigravity deletes...): 12 of 13 harmful actions stopped, checked in CI. The same steps through two other guards (destructive_command_guard, Microsoft's Agent Governance Toolkit), plus everyday coding work: bench/compare/.

An agent read an "urgent CEO" email from acrne-corp.com and tried to wire $24,800. Blocked, with the story of what led to it.

Approve or reject from your phone with one tap.

See it live, nothing to install

Click the button and the live demo starts in your browser (free with a GitHub account): a sandbox company's AI support agent works its inbox while you watch. A scam wire is blocked, refunds wait for a person, and a demo manager approves or rejects them. If the editor asks whether to allow tasks that run automatically, click Allow: that's the demo starting. On your own machine: pip install -r requirements.txt then python demo/live_demo.py.

Try it in 30 seconds

pipx install squidbrake           # or: pip install squidbrake
squidbrake connect all            # every AI agent on this computer now goes through it
squidbrake                        # start it: opens the dashboard (asks once about running in the background)

connect all finds the agents you have (Claude Code, Cursor, Codex, Gemini CLI, VS Code Copilot, Antigravity) and the MCP servers they already use, and routes them all through Squidbrake. It prints your dashboard key the first time, backs up every config it changes, and squidbrake connect all --remove undoes it. Restart the agents, then ask one to run rm -rf ~/ and watch it get blocked. squidbrake connect status shows which agents are covered, and catches the one step people miss (Codex runs a new hook only after you approve it in /hooks).

Your rules, keys and data live in ~/.squidbrake; edit ~/.squidbrake/rules.yaml and changes apply at once.

Only Claude Code? It's also a plugin, installed from inside Claude Code (see plugin/): /plugin marketplace add batrapulkit/squidbrake, then /plugin install squidbrake@squidbrake.

From a clone instead: git clone https://github.com/batrapulkit/squidbrake && cd squidbrake, then ./start.sh (Windows: start.bat) and ./connect.sh claude-code (Windows: connect.bat claude-code).

Or with Docker: docker run -d -p 8080:8080 -v squidbrake-data:/app/data --name squidbrake ghcr.io/batrapulkit/squidbrake (keys: docker logs squidbrake).

1. Start it

The first start installs everything, prints an admin key (for the dashboard) and an agent key (shown once, so save them), and opens http://localhost:8080/dashboard. No configuration needed; every setting in .env.example is optional.

The first start in a terminal also asks once whether to keep Squidbrake running in the background, so your agents (which fail closed) are never locked out because it's down. Enter means no; nothing is installed without a yes, and scripts or CI are never asked. A yes adds a login item: a LaunchAgent on macOS, a systemd user service on Linux, a Run registry value on Windows (some endpoint security tools flag these, so check with IT first). squidbrake start --background installs it without asking (or set SQUIDBRAKE_BACKGROUND=1 for unattended installs), squidbrake service status checks it, squidbrake service stop takes it out, and squidbrake run (or ./start.sh --foreground) always runs in the window. Docker restarts by itself already.

Keys: python server.py add-key NAME [--approver], python server.py remove-key NAME, python server.py keys. Changes apply immediately, no restart needed. (Inside Docker, prefix with docker compose exec gateway.)

2. Connect real agents

With the gateway running, one command per agent (installed with pip, type squidbrake connect ... instead; from a clone, use the .venv Python that start.bat / start.sh created):

.venv/Scripts/python connect.py all                  # every agent at once (Windows; macOS/Linux: .venv/bin/python)
.venv/Scripts/python connect.py claude-code          # or one at a time
.venv/Scripts/python connect.py mcp --name antigravity   # also: claude-desktop, cursor

• Claude Code: a hook sends every tool call (Bash, PowerShell, Edit, Write, Read, WebFetch, MCP tools) through the gateway before it runs. Blocked calls are refused with the reason, and calls held for approval wait until you decide in the dashboard. It also adds the database tools below. Add --project DIR to limit it to one project; --remove undoes it. If the gateway is down, Claude Code's tool calls are blocked (fail closed) and it says why.
• Antigravity, Claude Desktop, Cursor, any MCP client: prints the config block to paste in. The agent gets list_tables, describe_table, query and execute tools on a SQLite database (data/shop.db, created with sample customers / products / orders; set DB_PATH to use your own). Reads run immediately, UPDATE/DELETE/INSERT/ALTER wait for your approval, and DROP/TRUNCATE are blocked.

Wrapping a GitHub, Stripe, Slack or Postgres / Supabase MCP server? Start with the commented example policies in examples/rules/ and adjust their tool-name patterns to the server's tool list. Running an agent with nobody watching (a nightly loop, a cron job, a Routine)? Start from unattended-agent.yaml: a step cap, an allowlist, no redoing work, and no deletes or money movement, enforced from outside the agent instead of promised in its prompt.

Things to ask the agent, then watch the dashboard:

• "Show me the top 5 customers by revenue" (runs)
• "Give every customer on the team plan a 15% discount" (waits for you to approve)
• "Delete all failed orders" (approve or reject it; a rejection note is passed back to the agent)
• "Drop the orders table" (blocked)
• In Claude Code: "commit and push this" (the git push waits for approval)

3. Show it to someone

• Right now, from your PC: cloudflared tunnel --url http://localhost:8080 prints a public https://….trycloudflare.com link. Give viewers their own key: python server.py add-key guest. Afterwards, press Ctrl+C and run python server.py remove-key guest.
• Permanently: ./install.sh gateway.yourdomain.com on a small cloud server.

4. Other computers (a friend, a teammate, a server)

Make a clean copy (no keys, no history): python pack.py -> dist/squidbrake.zip.

• Their own gateway: unzip, double-click start.bat (Windows) or run ./start.sh. It makes its own keys.
• Their agents on YOUR gateway: in your dashboard's Team tab add them (a person, to watch/approve) and add their agent (type AI agent); send them that agent key. They unzip and run, for example: connect.bat wrap --sandbox --agent claude-code --url https://your-gateway --key gw_... (or connect.bat claude-code --url ... --key ... to route every Claude Code action through your gateway).
• A cloud server, 24/7: unzip there and run bash install.sh (HTTPS included, no domain needed).

Other ways to send calls through it

Any MCP server - put Squidbrake in front of it, in any MCP client. The agent sees the app's normal tools, and each call is checked first (with GATEWAY_URL and GATEWAY_API_KEY set in the client's MCP config):

squidbrake proxy --app linear --url https://mcp.linear.app/mcp          # a remote MCP server
squidbrake proxy --app github --url https://api.githubcopilot.com/mcp/ --header 'Authorization: Bearer ${GITHUB_TOKEN}'
squidbrake proxy --app stripe -- npx -y @stripe/mcp --tools=all          # one started by a command

squidbrake connect guard does this for the servers your agents already use, including remote ones with their own auth headers.

Agents that connect to MCP by URL (ChatGPT and claude.ai connectors, Devin, n8n, cloud agents) - easiest: in the dashboard, Settings → Agents that connect by URL, add the app's MCP server (its URL, and the header its auth needs). The agent then uses https://<your gateway>/mcp/<name> and signs in with one of its agent keys (Authorization: Bearer gw_..., or ?key=gw_... for apps that take only a URL). Every call is checked and recorded as that agent; the app's own token stays on the gateway and is never shown again. Hosted dashboards take servers by URL; servers started by a command need a self-hosted gateway with SQUIDBRAKE_MCP_COMMANDS=1.

Apps whose MCP server signs in only through a browser (OAuth: Slack, HubSpot, Notion, Asana, ClickUp, Grafana Cloud): choose Sign in to the app (OAuth), add it, click Connect and sign in once. The gateway keeps the session and refreshes it before it runs out. Apps that only let in OAuth apps registered with them (Slack, HubSpot) need your own OAuth app's client ID and secret, with the redirect URL the dashboard shows.

Or run the proxy yourself and serve it over HTTP. The agent adds https://your-host:9000/mcp as its MCP server and sends the token (Authorization: Bearer, X-Squidbrake-Token, or ?token= for clients that take only a URL):

squidbrake proxy --app github --serve 0.0.0.0:9000 --token "$PROXY_TOKEN" -- npx -y @modelcontextprotocol/server-github

Each connected conversation is checked on its own, so one agent's chain never mixes with another's. A caller can name its conversation with an X-Squidbrake-Session header. Put HTTPS in front of it (Caddy, a tunnel) before exposing it.

Python agent frameworks - OpenAI Agents SDK, LangChain / LangGraph, CrewAI, PydanticAI, smolagents, Google ADK: put all of an agent's tools behind it in one line. A call Squidbrake stops doesn't run, and the model gets a "NOT RUN: ..." message as the tool's result:

from squidbrake.client import Gateway          # pip install squidbrake (or drop client.py into your project)
gw = Gateway("https://gateway.example.com", api_key="...", source="support-agent", session_id=run_id)

agent = Agent(name="support", tools=gw.guard_tools([lookup_order, refund, send_email]))   # OpenAI Agents SDK
graph = create_react_agent(llm, gw.guard_tools(tools))                                      # LangGraph

Or one function at a time (raises Denied when it's stopped):

@gw.guard(name="shell.exec")
def shell_exec(command: str): ...

Any language - two HTTP calls (header X-Gateway-Key: <secret>):

POST /v1/events               {"name": "shell.exec", "input": {...}, "source": "...", "session_id": "..."}
  -> {"event_id": "...", "decision": "allow" | "deny", "reason": "...", "rule_id": ...}
POST /v1/events/{id}/result   {"output": ..., "error": null, "duration_ms": 12}

Record an action that already happened in one call by including output/error in the first POST.

HTTP proxy - no code changes: define upstreams in rules.yaml, then point the client at http://gateway:8080/proxy/<upstream>/.... Optional headers: X-Gateway-Source, X-Gateway-Session. Denied requests get 403; every response carries X-Gateway-Event-Id.

Try it on real work first: shadow mode

Set mode: shadow in rules.yaml (or shadow_agents: ["new-bot*"] for some agents) and Squidbrake blocks and holds nothing: it records what it would have done. Reports then shows would block and would hold counts, and each event is tagged, so a team can see a week of real decisions before switching mode: enforce on. Stops and catastrophic commands (rm -rf /, wiping a drive) are enforced even in shadow mode.

Command checks

Shell tools (Claude Code's Bash and PowerShell, or any tool matching command_checks.tools) are read by commands.py before the rules decide: the line is split on &&, ;, | (outside quotes), and sudo, xargs, bash -c, powershell -Command and $(...) are looked inside. Nothing is ever run or expanded.

Command checks apply even when a rule allows the tool, and read_only: allow only relaxes the default (never a rule or a warning). Configure them under command_checks: in rules.yaml.

Terminal guard

Commands people paste from ChatGPT, a forum or a README never pass an agent hook. This puts the same command reader (commands.py) in front of your own shell: catastrophic lines are blocked, irreversible or unreadable ones (git push --force, curl ... | sh) ask Run it anyway? [y/N], everything else runs without a word. It works offline, nothing is sent anywhere, and if the guard itself fails your command still runs.

squidbrake shell-guard install --shell bash >> ~/.bashrc        # then open a new terminal
squidbrake shell-guard install --shell zsh  >> ~/.zshrc

if (!(Test-Path $PROFILE)) { New-Item -ItemType File -Force $PROFILE }
squidbrake shell-guard install --shell powershell | Add-Content $PROFILE

install only prints the snippet; it never edits a file, so read it first. Scripts and CI (non-interactive shells) are never blocked or prompted. Each Enter starts Python, which adds about 150 ms. If bash already has a DEBUG trap (VS Code's shell integration, bash-preexec), the snippet leaves it alone and prints a warning. Check one line by hand: squidbrake shell-guard check "rm -rf ~/" (exit code 0 run, 1 block, 2 ask).

Prompt injection, caught without a model

The attacks that actually happened to agents (a GitHub issue, a support ticket or a web page telling the agent to send data somewhere) share one shape: the destination comes from content someone else wrote. Squidbrake records what you ask (Claude Code prompts, via the hook) and which tools bring in outside content (WebFetch, inboxes, issues, tickets...). When an action sends something to an email address, URL, bank account or repo that appears in that outside content but not in what you asked or in your own systems' results, it's held with the reason:

This sends to keys@evil.io (to), which appears in WebFetch (2 minutes ago) but not in anything you asked or in your own systems. Content from outside can carry hidden instructions (prompt injection).

Uploads from the shell count too (curl -d @.env https://..., scp, git push). Anything sent out after outside content was read gets a warning for the approver. Configure it under taint_checks: in rules.yaml.

Customer names and personal data in public places

Agents copy what they read: a ticket's customer ends up in a pull request description or a public issue. When an agent puts text where other people read it (a PR, an issue or comment, a gist, a chat post, a page, and gh pr|issue|gist ... create|comment|edit), it waits for a person if the text:

• names a customer on your list (dashboard → Settings → Customers), or
• carries personal data: a card number, a social security number, an IBAN, or a list of 3+ emails or phone numbers.

Looking things up (list_issues, search_issues) doesn't count, and email has its own rule. The approver sees which customer it named, or which kind of data; the data itself isn't repeated. Configure it under data_checks: in rules.yaml (block, review, warn, off).

Sequence rules

Some actions are only dangerous because of what came before them. sequences: in rules.yaml judges an action by the steps before it and names the step that caused the decision:

sequences:
  - id: destroy-after-recovery-removed      # turn off backups, then delete the database
    action: review
    reason: Destroying data right after its backups or deletion protection were turned off
    match: { input_regex: 'delete[-_ ]?db[-_ ]?instance|terraform\s+destroy|drop\s+(table|database)' }
    after:
      match: { input_regex: 'backup[-_ ]?retention[-_ ]?period\W{0,4}0|deletion[-_ ]?protection\W{0,4}false' }
      within_hours: 24
  - id: runaway-refunds                     # an agent stuck in a loop
    action: deny
    reason: Too many refunds in a short time
    match: { name: ["*refund*"] }
    count: { more_than: 10, within_hours: 1, scope: agent }

The approver then sees, for example: Destroying data right after its backups were turned off. Because earlier: Bash aws rds modify-db-instance --backup-retention-period 0 (12 minutes ago). after: looks at steps in the same conversation that went ahead (same_target: true = on the same charge, file or account); count: counts earlier matching actions per session, agent or all. Actions: deny, review or warn.

Human approval

Rules with action: review hold the call until a person approves or rejects it:

- id: approve-payments
  action: review
  reason: Money movement needs a human
  timeout_seconds: 600     # default APPROVAL_TIMEOUT (300)
  on_timeout: deny         # or allow
  approvers: [alice, bob]  # optional; default = any approver key
  match: { name: "payments.*" }

• The first POST /v1/events returns "decision": "review". The Python client handles this for you: check() and @gw.guard block until the call is decided, then run the tool or raise Denied. Other clients long-poll GET /v1/events/{id}/decision?wait=25 until decision is allow or deny.
• To decide, use the Needs approval queue at the top of the dashboard, or call POST /v1/events/{id}/approve / .../reject with an optional {"note": "..."}.
• Only approver keys (python server.py add-key NAME --approver, and the rule's approvers if set) can decide, and a key can never approve its own request. So give people their own keys, separate from the agents' keys.
• If nobody decides before the deadline, on_timeout applies and decided_by is recorded as timeout.
• Every decision is stored on the event with who decided, when, and their note.
• Set APPROVAL_WEBHOOK_URL (plus PUBLIC_URL) to get a Slack- or Discord-formatted message with a link to the event whenever something needs approval.
• Microsoft Teams and email: in Settings, paste a Teams Workflows webhook ("Post to a channel when a webhook request is received"), or an SMTP server and the addresses to send to. Each approval links to the one-tap review page; an email never decides anything by being opened, since mail scanners open links.
• Every Monday, what it caught: Slack, Discord, Teams and email get the week's summary: what was blocked, rejected, or (in shadow mode) would have been, why, and what it would have changed. Reports → What it caught shows it any time, with Copy weekly report.
• Stop asking about what you always approve: Settings → Rules suggests an allow rule for anything people approved 5+ times in 30 days and never rejected (never for money, an agent's own setup, or what the command, data or chain checks held). Allow it adds the rule right before the one that held it, with a backup.
• Starter packs for a support, finance or ops agent add a block of rules after the read rule, so blocks above still win.
• To your SIEM: Settings → Send to your SIEM streams every decision to Splunk (HEC), Datadog (logs) or any HTTP endpoint, in batches in the background. Or set SIEM_URL, SIEM_TOKEN, SIEM_FORMAT.
• Approve and Reject right in Slack: in Settings, open Approve and Reject right in Slack, create the Slack app from the manifest shown there, install it to your approvals channel, and paste its webhook URL and signing secret. Each click is checked against the signing secret and carries that one event's signed token; the message then says who decided.
• Through the HTTP proxy, a held request stays open until it's decided, so the caller's HTTP timeout must be longer than the rule's timeout_seconds.

Dashboard

Open http://<host>:8080/dashboard and paste your admin key (or any key made with add-key). It is stored only in that browser. The page shows:

• totals per status (click a tile to filter), a calls-per-hour/day chart (hover for counts, click a bar to list just that hour, "View as table" for exact numbers), and the top tools
• the event list, updated every 5 s while "Live" is on. Filter by time range, search, status, kind, source or session. Click a source or session to filter by it. "Load older" pages back.
• the full record for any event: input, output, error, metadata, the rule that blocked it, timings
• filters are kept in the URL, so a view like /dashboard?status=denied&range=7d can be bookmarked or shared

FAQ

How is Squidbrake different from Claude Code's built-in allow/ask permissions?

Claude Code's built-in permissions control actions for an individual agent. Squidbrake provides centralized rules for a whole team, with approvals from your phone or Slack, history checks, an audit trail, and support across multiple agents.

What happens if Squidbrake is down?

Squidbrake fails closed. Guarded tool calls are blocked if the gateway is unreachable, rather than being allowed through.

Does my data leave my machine?

What your agents do doesn't: commands, files, prompts, tool inputs and outputs, and the audit trail stay on your laptop or your server. What can leave is usage counts, and only after a yes: the first run asks once (Enter means yes) to send the command name, version, OS, how many actions were allowed, held and blocked, and for each held or blocked one the program (like rm) and the rule. See Usage sharing.

Does Squidbrake use an LLM to make decisions?

No. Squidbrake uses deterministic rules and checks. There is no LLM in the decision path.

Can an agent get around Squidbrake?

Squidbrake is built for the common case: a cooperative agent that makes a mistake or follows instructions it found in an email, a web page or an issue. It is not a sandbox against an agent that is deliberately trying to escape. Here's what that means in practice:

• Caught: what the agent does through a connected tool. That includes shell commands read before they run (ls && rm -rf ~/, bash -c "...", curl ... | sh), MCP calls, edits to its own settings, hooks and MCP servers or to Squidbrake's rules (held for a person), and chains of steps such as backups switched off and then a delete. Through the hooks, what a command runs underneath is read too: a Makefile target's recipe (make clean), a package.json script (npm run release) and a shell script (bash cleanup.sh).
• Held because it can't be read: a program written inline (python -c, node -e, perl -e), a program named only when it runs ($(...), eval), and a script the agent created in this conversation and then runs (python cleanup.py right after writing cleanup.py). A person reads it first.
• Held: the agent turning Squidbrake off. squidbrake connect --remove, service stop, uninstalling it, killing its process, or editing the agents' hook settings all wait for a person.
• Not caught:
  • A script that was already in the project, edited and then run: python scripts/seed.py runs, and what it does inside isn't seen (running the project's own scripts after a change is everyday work). Cursor reports new files and edits the same way, so there a new script counts as written too.
  • Programs that aren't common developer tools are recorded, not held (command_checks.unknown: warn; set it to review to hold them).
  • Tools that aren't connected (squidbrake connect status shows which are).
  • Damage done inside an MCP server after the call it checks: see the postmark-mcp row in incidents/.
  • A person, or a program running outside the agents, can still remove Squidbrake from their own laptop. For a company, squidbrake lockdown writes policy files IT pushes to every machine, so agents can't run without it (only an admin on the machine can undo that).
• For a hostile agent, add a sandbox (a container or a VM, with no credentials it doesn't need) underneath. Squidbrake then decides what is allowed, and the sandbox makes sure nothing goes around it.

To report a vulnerability, see SECURITY.md.

Querying

Evidence anyone can check

Every decision is written to a hash-chained audit trail together with the fingerprint of the rules.yaml version that made it, and each version's text is kept. Reports > Tamper check > Download evidence (or GET /v1/audit/export.json) gives one file that anyone can check on their own machine, without trusting the server:

python verify.py squidbrake-evidence-20261001-0930.json

It confirms the chain is unbroken, that every recorded action still matches the fingerprint taken when it happened (so rows edited in the database are caught, not just edits to the log), and that every decision's rules version is in the file. verify.py needs only the Python standard library. The dashboard's tamper check runs the same checks live. Add --json for one machine-readable object (ok, the number of records checked, and a list of problems with the record each one was found in) for CI; the exit code stays 0/1 either way.

Behaviour notes

• Fail closed: the Python client refuses to run a tool if the gateway is unreachable (fail_open=True to change that).
• Rules hot-reload on file save; a broken edit is logged and the last good rules stay active.
• Audit records are write-once: a result can be posted once, never for a denied call, and never while the call is still awaiting approval. Each approval can be decided only once. If two approvers click at the same moment, only the first decision counts.
• Upgrades: new columns are added to an existing database automatically at startup.
• Redaction: keys like password, token, api_key, authorization, cookie and values like Bearer ..., sk-..., ghp_..., AWS, Slack, Stripe, Google and npm keys, JWTs, private keys and the password in a URL (postgres://user:...@host) are stored as [REDACTED]. Rules still see the raw input.
• Payloads over MAX_PAYLOAD_CHARS are truncated in storage. Proxy responses are buffered (no streaming/SSE).
• SQLite (WAL) is fine for one machine. For several gateway replicas, set DATABASE_URL to Postgres.

Tests

pip install pytest && pytest -q
python tests/e2e_business_scenario.py

Usage sharing (asked first)

Squidbrake shares nothing about you or your agents without asking first. The one request it makes on its own is the daily update check below, which only asks for the newest version number.

Anonymous usage stats. The first time you run squidbrake in a terminal, it asks once whether to send anonymous stats (Enter means yes): the command name (e.g. doctor, never its arguments), version, OS, Python version and country, plus the gateway's usage counts described below (what it allowed, held and blocked). Never commands an agent ran, files, prompts, rules, keys or the audit trail. The agent hooks never send anything, and scripts, CI and --yes are never asked. squidbrake telemetry off (or SQUIDBRAKE_TELEMETRY=0, or DO_NOT_TRACK=1) stops it; squidbrake telemetry status shows exactly what is sent. Update check. Once a day, running squidbrake in a terminal asks pypi.org for the newest version number, and the next time says if there's a newer one. That request carries nothing about you beyond the request itself; never in hooks, scripts or CI; off with SQUIDBRAKE_NO_UPDATE_CHECK=1 or DO_NOT_TRACK=1.

If you'd like the team to know who you are, squidbrake register you@company.com (asks first). Details: telemetry.py.

Usage counts. There are two ways to share counts of what Squidbrake did, and both show exactly what will be sent and ask first (the default answer is no):

• squidbrake connect all, run in a terminal, asks once at the end whether to share counts with the Squidbrake team. It never asks again after a no, and never asks with --yes or in scripts.
• A pilot joins with the code they were given: squidbrake pilot join CODE --server URL.

What is shared is usage counts: actions allowed, held, approved and blocked per day, and which agents and rules. Never commands, code, prompts or keys. squidbrake pilot leave stops it. Details: pilot.py and insights/.

Roadmap

• Reach checks for AWS (#18, design): before an IAM change runs, work out what the agent will be able to reach afterwards, and hold it if that crosses a line (admin roles, production, secrets). Stops an agent from giving itself admin in steps that each look harmless.
• An optional risk model that can only escalate (#16): a second opinion that can hold an action, never allow one.
• More agents connected in one command: Cursor install (#2), more rule packs like the GitHub, Stripe, Slack and Postgres ones.

Tell us what you need most: 👍 or comment on the issues.

Contributing, security, license

• CONTRIBUTING.md: a 10-minute setup, and good first issues that name the file and function to change. Comment on one to claim it. Hacktoberfest pull requests are welcome.
• SECURITY.md: report vulnerabilities privately
• Licensed under the Apache License 2.0