overwing 0.1.0

Guardrails for LLM output, in one line.
OpenAI Agents SDK guardrails, LangChain runnables and callbacks, and a typed client. Every message gets a pass / fail / review verdict with calibrated confidence before it reaches your user.

pip install "overwing[agents]"      # OpenAI Agents SDK guardrails
pip install "overwing[langchain]"   # LangChain guard runnable + callbacks

Get a free API key at overwing.ai (250 evaluations a day), or let your agent sign itself up with one POST to /api/v1/signup. Try it first with no key: paste anything into the console at overwing.ai.

OpenAI Agents SDK guardrails

from agents import Agent, Runner, InputGuardrailTripwireTriggered, OutputGuardrailTripwireTriggered
from overwing.openai_agents import overwing_input_guardrail, overwing_output_guardrail

agent = Agent(
    name="Support",
    instructions="Help the customer.",
    input_guardrails=[overwing_input_guardrail()],     # scores the user's message
    output_guardrails=[overwing_output_guardrail()],   # scores the agent's final answer
)

try:
    result = await Runner.run(agent, "Reach me at dana@example.com to sort out the refund.")
except (InputGuardrailTripwireTriggered, OutputGuardrailTripwireTriggered) as exc:
    evaluation = exc.guardrail_result.output.output_info["evaluation"]
    print(evaluation.verdict, evaluation.failed_rules)   # "fail" ["pii_detected"]

Both accept rule_set, trip_on="fail" | "fail-or-review", metadata, on_verdict, and fail_open. Input guardrails run in parallel with the agent by default; pass run_in_parallel=False to block before the model is called. Reads OVERWING_API_KEY from the environment, or pass client=AsyncOverwing(api_key=...).

LangChain

Pipe a guard after your model. It scores the answer and acts on the verdict before anything downstream sees it.

from overwing.langchain import overwing_guard, OverwingGuardrailError

chain = prompt | llm | overwing_guard(on_fail="replace")   # or on_fail="raise" (default) / "annotate"
msg = chain.invoke({"question": "..."})
msg.response_metadata["overwing"]   # {"verdict": "pass", "confidence": 0.97, "failed_rules": [], ...}

Or observe every LLM call with a callback handler, which aborts the run on fail:

from overwing.langchain import OverwingCallbackHandler

handler = OverwingCallbackHandler(check_input=True)   # also scores the user's prompt
llm.invoke("...", config={"callbacks": [handler]})
handler.verdicts   # [("input", Evaluation), ("output", Evaluation), ...]

Both accept rule_set, metadata, on_verdict, and fail_open. There is an AsyncOverwingCallbackHandler too.

Client

from overwing import Overwing, AsyncOverwing

ow = Overwing()   # or Overwing(api_key="ow_live_...")

e = ow.evaluate("Reach me at dana@example.com to sort out the refund.")
e.verdict            # "fail"
e.failed_rules       # ["pii_detected"]
e.results[1]         # RuleResult(rule="pii_detected", answer=True, confidence=0.98, verdict="fail", ...)

batch = ow.evaluate_batch([{"id": "a", "input": "..."}, {"id": "b", "input": "..."}])
ow.create_rule_set(name="Support tone", slug="support-tone", rules=[...])
ow.usage()

async with AsyncOverwing() as aow:
    e = await aow.evaluate("...")

OverwingError carries status and retry_after_seconds. 429s with a short Retry-After and 5xx are retried automatically. Pass idempotency_key= to make retries safe. Python 3.10+.

How verdicts work

Each rule has a fail condition, an optional review threshold, and a weight. The prebuilt content-safety set checks toxicity, personal data, self-harm, sexual content, and severity. fail means a rule matched. review means a rule was unsure. pass is everything else. Full guide: overwing.ai/llms.txt. Reference: overwing.ai/docs.

Also from Overwing

• overwing on npm: the same client, a Vercel AI SDK middleware, and Agents SDK guardrails for JavaScript.
• overwing-mcp: the guardrails as MCP tools for Claude, Cursor, and any MCP client.

MIT © Overwing. Verdicts are produced by TypeSafe's Jev System One model; Overwing is not affiliated with TypeSafe.