bighub-openai 3.0.1

OpenAI adapter for decision learning for AI agent actions with BIGHUB.

bighub-openai

OpenAI adapter for decision learning on tool calls.

bighub-openai connects the OpenAI Responses API to BIGHUB so tool calls can be evaluated before execution and learned from after execution.

OpenAI Responses API  ->  bighub-openai  ->  BIGHUB
tool call             ->  evaluate       ->  execute / block / approval
real outcome          ->  report         ->  learn from similar cases

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Install

pip install bighub-openai

Requires Python 3.9+.

Dependencies:

• bighub>=3.0.0,<4.0.0
• openai>=2.0.0,<3.0.0

---

Quickstart

import os
from bighub_openai import BighubOpenAI

def refund_payment(order_id: str, amount: float) -> dict:
    return {"ok": True, "order_id": order_id, "amount": amount}

guard = BighubOpenAI(
    openai_api_key=os.getenv("OPENAI_API_KEY"),
    bighub_api_key=os.getenv("BIGHUB_API_KEY"),
    actor="AI_AGENT_001",
    domain="customer_transactions",
)

guard.tool(
    "refund_payment",
    refund_payment,
    value_from_args=lambda a: a["amount"],
)

response = guard.run(
    messages=[{"role": "user", "content": "Refund order ord_123 for 199.99"}],
    model="gpt-4.1",
)

print(response["execution"]["last"]["status"])
# executed | blocked | approval_required

guard.tool(...) auto-generates a strict JSON schema from the Python function signature. Use parameters_schema=... only when you need custom constraints.

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How It Works

For every tool call, the adapter follows the same loop:

1. The model proposes a tool call
2. The adapter captures action + arguments + actor + domain
3. BIGHUB evaluates the action in context
4. A decision is returned (allowed, blocked, requires_approval)
5. If allowed, the tool executes
6. The action can later be linked to its real outcome
7. Outcome feedback means future similar tool calls are judged with more experience

---

Response Shape

{
  "llm_response": {...},
  "execution": {
    "events": [...],
    "last": {
      "tool": "refund_payment",
      "status": "executed",   # executed | blocked | approval_required
      "decision": {
        "allowed": True,
        "result": "allowed",
        "reason": "Matched positive outcomes from similar refund decisions",
        "request_id": "act_abc123",
        "requires_approval": False,
        "risk_score": 0.21
      }
    }
  }
}

The exact decision payload can include additional backend fields. In most integrations, the key signals are:

• whether execution is allowed now
• whether human approval is required
• how the action was judged from past experience

Decision vs execution naming:

• Decision result: allowed / blocked / requires_approval
• Execution status: executed / blocked / approval_required

---

Streaming

for event in guard.run_stream(
    messages=[{"role": "user", "content": "Refund order ord_123 for 199.99"}],
    model="gpt-4.1",
):
    if event["type"] == "llm_delta":
        print(event["delta"], end="")
    elif event["type"] == "execution_event":
        print("\n[decision]", event["event"]["tool"], event["event"]["status"])
    elif event["type"] == "final_response":
        print("\nDone:", event["response"]["output_text"])

Event type	Description
llm_delta	Incremental text token
llm_text_done	Complete text segment
execution_event	Tool decision or execution result
final_response	Final payload, same shape as run()
response_done	Response finished
response_failed	Response error

---

Async

from bighub_openai import AsyncBighubOpenAI

guard = AsyncBighubOpenAI(
    openai_api_key=os.getenv("OPENAI_API_KEY"),
    bighub_api_key=os.getenv("BIGHUB_API_KEY"),
    actor="AI_AGENT_001",
    domain="customer_transactions",
)

response = await guard.run(
    messages=[{"role": "user", "content": "Refund order ord_123 for 199.99"}],
    model="gpt-4.1",
)

---

Human-in-the-Loop Approvals

result = guard.run_with_approval(
    messages=[{"role": "user", "content": "Refund order ord_123 for 5000"}],
    model="gpt-4.1",
    on_approval_required=lambda ctx: {
        "resolution": "approved",
        "comment": "approved by on-call",
    },
)

Run approval callbacks server-side, not in clients, to avoid exposing approval credentials.

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Links

• bighub.io
• GitHub — bighub-io/bighub
• PyPI — bighub-openai
• PyPI — bighub

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License

MIT