breakerbox 0.5.0

Circuit breaker + hierarchical cost budgeting for AI agent workflows

The visual agent builder that can't be hacked into — because there's nothing to hack.

No server execution · No stored keys · Codegen only

pip install breakerbox

🔗 Live site · The demo ↓ · Quickstart ↓

Prototype anywhere; ship with Breakerbox. You draw a workflow on a canvas, and it generates plain, editable LangGraph Python you download and run yourself — wrapped in guard(), a hierarchical dollar budget that stops runaway loops at a hop boundary and writes a receipt of exactly where the money went. In the bundled demo, a runaway retry loop that burns $12.63 unguarded stops at $0.82 under guard(). Three things make it different:

1. Zero attack surface — codegen only. No endpoint ever executes your flows; no provider key is ever stored or transmitted. There's nothing on our side to compromise. (See Why no Run button?)
2. Budget-first. The only builder where hierarchical budget escrow, trip rules, and side-effect tagging are part of the canvas itself.
3. Code you own. The output is readable, hand-editable Python — scaffolding, not a walled garden.

Breakerbox is the product; the Python package is breakerbox (pip install breakerbox). The import, CLI, and API names are unchanged.

Contents

• Quickstart
• The demo
• What it actually does
• Build it visually
• Why no Run button?
• How it compares
• Notes & limitations
• Cloud dashboard (optional)
• Roadmap
• Contributing
• License

Quickstart

pip install breakerbox

Wrap the compiled LangGraph app you already have — the call site doesn't change:

from breakerbox import guard

app = guard(
    my_langgraph_app,
    budget_usd=5.00,           # hard ceiling for the whole workflow DAG
    max_hops=50,               # total model/tool steps across all branches
    ttl_seconds=600,           # wall-clock limit
    velocity_usd_per_min=2.0,  # trip if the burn rate spikes
    on_trip="pause",           # "pause" | "kill"
)

result = app.invoke(inputs)    # the same call you already make

Every model and tool dispatch inside the graph — including sub-agents and subgraphs — inherits one real-dollar budget. When a limit trips, the workflow stops at the next hop boundary (never mid-call) and drops a shareable HTML receipt. No proxy, no Docker, no gateway to run: enforcement lives inside your process, so there's no direct-call bypass.

Prefer to start on the canvas? Open the builder, draw the graph, and let it write this for you:

cd cloud/dashboard && npm install && npm run dev   # http://localhost:3000/builder

The demo

A broken retry loop that never drops its context, so each hop costs more than the last (examples/runaway_demo) — no API keys required:

==============================================================
  UNGUARDED runaway : ran 60 hops, spent $12.63
  GUARDED           : killed early, spent $0.82  (budget $0.90)
  AVERTED           : $11.81
==============================================================

It stops strictly under budget — because the model declares a real max_tokens, the reserve estimate is a true upper bound, so the call that would cross the line is blocked before it runs. Every run also writes a self-contained report.html:

────────────────────────────────────────────────────────
 breakerbox receipt · killed (budget)
 stopped at $0.8157   budget $0.9000   hops 13
 projected (naive linear extrapolation, likely an underestimate): $3.13
────────────────────────────────────────────────────────

(See sample_receipt.html for the full HTML.)

What it actually does

• Hierarchical budget escrow. Not a flat session counter. A parent sub-allocates budget to child agents; a child can never spend beyond its allocation; a parent's remaining is budget − Σ(child allocations) − own spend. Accounting is reserve → execute → reconcile (a credit-card hold), so parallel branches can't race past the ceiling. Concurrency-safe and property-tested (Σ spent + Σ reserved ≤ root budget, always).
• Graceful trip actions.
  • pause — checkpoints via LangGraph's native checkpointer and raises BudgetPaused; app.resume(checkpoint_id, extra_budget_usd=...) continues from where it stopped.
  • kill — stops, finalizes the receipt, raises BudgetKilled listing which side-effecting tools already fired (so you can compensate).
• Self-metering. Counts input tokens locally (tiktoken) and meters streamed output chunks, then reconciles against the provider's reported usage and flags discrepancies — never trusts a single usage field, never meters an unknown model as $0. If a call ran without max_tokens and the cap landed one hop late, the receipt flags that hop explicitly.
• The receipt. Terminal summary + single-file report.html (inline CSS/SVG, no JS, no external assets) + JSON. Leads with the indisputable number — stopped at $Y, budget $Z — with the projection as clearly-labelled fine print.

Build it visually

cloud/dashboard/app/builder is a drag-and-drop canvas (model / tool / router / start / end nodes) with a live Budget Tree — root budget → per-node allocations → unallocated remainder. Over-allocate and it turns red and blocks export. Hit Generate and you get the guarded Python above, ready to copy or download. Everything runs in your browser; the spec → Python codegen is shared with the breakerbox build spec.json CLI and locked to it by golden-fixture tests (Python and TS, enforced in CI).

Cost forecast (before you run). The canvas forecasts a run's cost while you design it — a p50–p95 band per node and for the whole graph, with a what-if loop slider and a per-node model swap, all computed in the browser with no API calls. It's a labelled estimate and advisory-only: it never changes the generated Python. Most builders show cost after a run; this shows it first.

AI suggest (bring your own key). Any model/tool/router node has an optional "Suggest code" helper: describe the step, and it drafts a Python body for you to copy in. It calls the model directly from your browser with your own API key — the key is stored only in your browser and sent only to the provider, never to a Breakerbox server (there is no server in this path). Anthropic supports browser-direct calls; OpenAI blocks browser CORS, so for OpenAI you point the base URL at your own CORS-enabled proxy. The suggestion is copy-paste scaffolding — it's never written into the saved graph, so codegen stays deterministic.

Why no Run button?

Server-side flow builders that run your graphs and hold your provider keys have been a repeated remote-code-execution target. In Langflow (the category leader — 150k+ GitHub stars, backed by DataStax, an IBM company) the pattern is well documented and public:

• CVE-2025-3248 (CVSS 9.8) — unauthenticated RCE via the /api/v1/validate/code endpoint that passed user input to exec(); on CISA's KEV (added 2025-05-05), exploited by the Flodrix botnet.
• CVE-2025-34291 (CVSS 9.4 v4.0 / 8.8 v3.1) — a CORS/CSRF account-takeover chain (a victim visits a malicious page) that exfiltrates the API keys and tokens stored in a workspace; on CISA KEV (added 2026-05-21). Reportedly used by the MuddyWater APT for initial access (Ctrl-Alt-Intel via The Hacker News; not confirmed by CISA).
• CVE-2026-33017 (CVSS 9.8) — unauthenticated RCE via the public flow-build endpoint (/api/v1/build_public_tmp/…); on CISA KEV (added 2026-03-25), exploited within ~20 hours of disclosure (Sysdig) and separately used to drop Monero/XMRig cryptominers (Trend Micro).
• CVE-2026-5027 (CVSS 8.8) — path-traversal arbitrary file write via /api/v2/files, escalatable to RCE; ~7,000 instances exposed (Censys, incl. historical scan data) with active exploitation observed (VulnCheck). Not on CISA KEV.

This isn't a knock on Langflow's product — it's an architectural fact: a server that runs your flows and holds your keys is a high-value target. Breakerbox removes the target. The canvas only ever produces a Python string you run yourself, and your API keys live in your own environment — never in a dashboard, database, or edge function. There's no endpoint to exploit because no endpoint executes anything. That's the trade: you give up one-click cloud runs, and in exchange there is nothing to breach. Prototype in a tool like Langflow if you like; ship the production-safe version here.

How it compares (facts only)

	Langflow	LiteLLM budgets	Breakerbox
Visual graph building	✅ rich	—	✅ budget-first
Server executes your flows	✅ (attack surface)	n/a	❌ by design
Stores your provider keys	✅	✅ (proxy)	❌ never
Hierarchical per-agent dollar escrow	—	— (flat, per-key)	✅
Graceful pause/resume at a hop boundary	—	— (hard error)	✅
Catches a runaway run under the org ceiling	—	— (never fires)	✅
Output is plain, editable Python	partial (export)	n/a	✅ core promise

LiteLLM/Portkey/Kong-style gateways solve a different layer (org-wide per-key spend) and compose fine alongside this — the gateway caps the org, Breakerbox governs one workflow's internal structure.

Behind a gateway (LiteLLM / Portkey): point your model at the gateway's base URL and wrap the compiled app in guard(). Metering is a LangChain callback, not a network hook, so it composes with any OpenAI-compatible proxy with no extra wiring:

model = ChatOpenAI(model="openai/gpt-4o", base_url="http://localhost:4000", api_key="sk-litellm-…")
app = guard(compiled_app, budget_usd=5.00)  # gateway routes + holds the key; guard budgets the run

The catch most gateway users miss: a key limit has to sit high enough not to block real work, so a single runaway run can burn $180 while still under a $500 ceiling — the key never fires until the damage is already account-wide, and then it 429s everyone. Breakerbox trips that one run at $2, at a hop boundary. A key limit is the fuse box for the building; Breakerbox is the breaker on this circuit.

vs LiteLLM / vs Langflow, in one line: a gateway like LiteLLM guards the key — one flat ceiling, blind to a single run's internal structure. A builder like Langflow runs your flow — server-side, holding your keys. Breakerbox guards the workflow and runs nothing: a hierarchical per-agent budget baked into plain Python you own. Prototype with either; ship the guarded version here.

Every claim above maps to a public, verifiable fact.

Notes & limitations (read before you rely on it)

• on_trip="pause" needs a checkpointer. Compile your graph with one (.compile(checkpointer=MemorySaver())); guard() raises if it's missing.
• The overshoot rule: the guard never interrupts a call mid-flight. If your model has no max_tokens, the reserve estimate can under-count and the cap is enforced one hop late (overshoot bounded by a single call) — the receipt flags that hop. Set max_tokens and it stops strictly under budget.
• Prices (prices.json, ~2000 models) are sourced from LiteLLM's community-maintained price table. Refresh them any time with breakerbox update-prices (bundled table is the offline fallback). Still spot-check the models you care about. Override per-model or set unknown_model="default_rate" to meter unknown models at a conservative rate instead of failing.

Cloud dashboard (optional)

→ Open the dashboard — watch runs stream live, and sign in to keep runs private to your account.

Everything above works with zero cloud. If you want a shared, live view of runs, point the guard at a Supabase-backed dashboard:

app = guard(my_app, budget_usd=5.00, report_to="https://YOUR_REF.functions.supabase.co/ingest")

Events stream to the dashboard as the run executes (best-effort and non-blocking — a cloud outage never affects the run); each run gets a shareable, unlisted URL with a live timeline.

To keep runs private to your account, sign in to the dashboard, create a key under Settings → Your ingest key, and set it where your agents run:

export BREAKERBOX_INGEST_KEY="abk_…"   # from the dashboard; runs become private to you

Without a personal key the legacy shared key still works and runs stay public (shareable link). Deploy runbook and code in cloud/ (Supabase + Next.js on Vercel).

Roadmap

Where this is headed — horizons, what we will and won't build — is public in ROADMAP.md. The codegen-only, no-stored-keys architecture is a permanent constraint, not a phase.

Contributing

Issues and PRs welcome. Good first stops: the open issues and the ROADMAP.md horizons. To work on it locally:

pip install -e ".[dev]"
pytest -q          # pricing, ledger (+ hypothesis), tripwire, meter, guard, report, sink
ruff check .

The dashboard (cloud/dashboard) has its own checks: npm run typecheck, npm run lint, npm test (codegen + price-table parity), npm run build. CI runs both suites on every push.

License

MIT