Integrations¶

Each integration lives in its own optional submodule and pulls in its own optional dependencies. Importing ai_audit.integrations itself does not load any optional package.

FastAPI / Starlette¶

from fastapi import FastAPI
from ai_audit import AuditConfig, init_audit_config, ReceiptStore
from ai_audit.integrations.fastapi import AuditMiddleware

init_audit_config(AuditConfig.from_env())
store = ReceiptStore()

app = FastAPI()
app.add_middleware(
    AuditMiddleware,
    store=store,
    tenant_id="acme",
    path_prefix="/v1/ai/",        # only audit AI endpoints
    capture_body=True,            # include request body in input hash
    max_body_bytes=64 * 1024,
    trace_header="x-trace-id",    # propagate from upstream
    session_header="x-session-id",
)

The middleware emits one DecisionReceipt per matched request, mapping HTTP status codes to actions:

2xx / 3xx → ALLOW
4xx / 5xx → REJECT
exception during call_next → FAIL_RETRY

Install: pip install "ai-audit-trail[fastapi]"

LangChain¶

from langchain_openai import ChatOpenAI
from ai_audit import AuditConfig, init_audit_config, ReceiptStore
from ai_audit.integrations.langchain import AuditCallbackHandler

init_audit_config(AuditConfig.from_env())
store = ReceiptStore()

handler = AuditCallbackHandler(store=store, tenant_id="acme")
llm = ChatOpenAI(callbacks=[handler])

One receipt per LLM call (on_llm_start / on_llm_end / on_llm_error). Errors map to FAIL_RETRY.

Install: pip install "ai-audit-trail[langchain]"

OpenAI SDK¶

from ai_audit.integrations.openai import AuditedOpenAI

client = AuditedOpenAI(store=store, tenant_id="acme")
response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Hello"}],
)

finish_reason maps to actions:

content_filter → REJECT
length → ALLOW + truncated_by_length reason code
stop (and others) → ALLOW

For manual control over the receipt:

from ai_audit.integrations.openai import emit_chat_completion_receipt

response = openai_client.chat.completions.create(...)
emit_chat_completion_receipt(
    store, tenant_id="acme", model="gpt-4o-mini",
    messages=messages, response=response,
)

Install: pip install "ai-audit-trail[openai]"

Anthropic SDK¶

from ai_audit.integrations.anthropic import AuditedAnthropic

client = AuditedAnthropic(store=store, tenant_id="acme")
response = client.messages.create(
    model="claude-opus-4-7",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Hello"}],
)

stop_reason maps to actions:

refusal → REJECT
max_tokens → ALLOW + truncated_by_length reason code
end_turn (and others) → ALLOW

Install: pip install "ai-audit-trail[anthropic]"

Build your own¶

Every integration is ~100 lines of glue using the same three primitives:

from ai_audit import ReceiptCollector, ReceiptStore

collector = ReceiptCollector(trace_id=..., tenant_id=...)
collector.set_input(prompt_text)
collector.set_output(response_text)
collector.set_action("allow")
collector.emit(store)
collector.cleanup()

There is no framework coupling — tenant_id, trace_id, and session_id are opaque strings to the protocol.