faultbench

Fake, stateful worlds with fault injection for testing tool-using AI agents.

Declare your services in a YAML file. faultbench serves them as MCP tools your agent can call, makes them slow or broken on purpose, records every call, and lets you assert on the world's end state from pytest, twenty runs at a time.

Status: pre-alpha, but it works end to end. Write a world, serve it over MCP (stdio or HTTP), inject faults, and assert on state from pytest across N seeded runs. Guides: write a world · test an agent · examples: examples/shop (refund agent), examples/bank. Validated against Pydantic AI and the OpenAI Agents SDK (any MCP framework works).

The problem

Your support agent handles "return my order and refund me." It works when you test by hand. In production the refund API times out once, the agent retries, and a customer is refunded twice. You can't make the real payments API time out on command, so you never tested it.

Related MCP server: mcp-explorer

What it looks like

# world.yaml
services:
  orders:
    records: { order: { id: str, status: enum[placed, delivered, returned], total: float } }
    operations: { get_order: { kind: get, record: order } }
  payments:
    records: { refund: { id: str, order_id: str, amount: float } }
    operations: { issue_refund: { kind: create, record: refund } }
faults:
  payments.issue_refund: { errors: { timeout: 0.10 } }

from faultbench.integrations.pydantic_ai import run_agent  # or wire any MCP framework


@pytest.mark.world("world.yaml")
@pytest.mark.faults("world.yaml")  # inject the faults: block above
@pytest.mark.runs(20)
@pytest.mark.min_pass_rate(0.95)
async def test_refund_issued_exactly_once(world, mcp_server, trace):
    order = world.orders.pick(status="delivered")
    await run_agent(
        "openai:gpt-5-mini",
        f"Return order {order.id} and refund me",
        mcp=mcp_server,
        system_prompt="You are a refund agent.",
    )
    assert len(world.refunds.where(order_id=order.id)) == 1  # a timeout+retry breaks this

faultbench: pass rate over runs
test_refund_issued_exactly_once: 17/20 passed (85%)  min_pass_rate=95% -> FAIL
    run4:  get_order → create_return → issue_refund!timeout → issue_refund
    run11: get_order → create_return → issue_refund!timeout → issue_refund
    run18: get_order → create_return → issue_refund!timeout → issue_refund

The timeout fired after the refund was written, the agent retried, and the customer was refunded twice — the production bug you couldn't trigger on the real payments API, now a red test with the trace that explains it.

What it can and can't model

faultbench models services as flat records (fields: str/int/float/bool/datetime/enum/ref) with built-in CRUD plus custom Python operations for anything else.

• Fits well: entities with enums and ref relationships; CRUD and list-by-field; business rules, state machines, and multi-record writes as custom handlers; array inputs via a custom op that flattens into a related record type; money as integer minor-units. (See examples/stripe — a Stripe-style payments API with partial-refund rules.)

• Caveat: records are flat — there are no nested objects or array fields. Model a one-to-many as a related record type + a ref (invoice ← line items); a GET returns the parent without children inline, so if your agent's correctness depends on a nested response shape, the fake's shape differs.

• Not in 0.1: pagination/cursors, non-equality filters, auth, webhooks, per-request idempotency (that last is a bug faultbench helps you catch, not prevent). Generated seed values are type-correct but not domain-aware — set realistic values in a handler or your test.

Security

A world file can name Python to import and run (custom handlers, handler: module.func), so loading or serving one executes that code. Only use world files you trust, like any script.

Not in scope

Simulated users, LLM judges, dashboards. Use LangWatch Scenario / DeepEval for users and your own judge for scoring; faultbench is the environment.

Quickstart

uv sync --all-extras
uv run pytest                                             # the harness test suite (no API key)
uv run faultbench serve examples/shop/worlds/shop.yaml    # serve a world over MCP (stdio)
uv run faultbench serve examples/bank/worlds/bank.yaml --http   # ...or over HTTP

Then write your own: docs/WORLDS.md and docs/TESTING.md.

Development

See CLAUDE.md for the session protocol and docs/ for architecture, decisions and status.

uv sync --all-extras
uv run pytest
uv run pytest examples/shop     # the example agent test (needs a model API key)

License

Apache 2.0