aitracer-sdk 0.3.0

Official Python SDK for AITracer — tracing, verification, governance, cost intelligence, and integrations

AITracer Python SDK

This repository is a monorepo (github.com/no1rstack/aitracer-sdk): the official Python package (this document), the TypeScript SDK at packages/sdk (npm install @aitracer/sdk), and starter templates under starters/.

Official Python entrypoint for AITracer — tracing, verification, governance signals, cost intelligence, and first-party integrations (OpenAI Agents, OTLP/JSON, Guardrails).

Install from PyPI (package name aitracer-sdk, import as aitracer):

pip install aitracer-sdk

Current version: 0.3.0

---

Configure

Use an workspace API key (akt_…) and your app base URL (same origin as the AITracer web app).

from aitracer import AITracerClient, configure

configure(api_key="akt_xxx", base_url="https://your-app.example.com")

# Or construct a client explicitly:
client = AITracerClient(api_key="akt_xxx", base_url="https://your-app.example.com")

The client uses retries, timeouts, and Authorization: Bearer on all requests.

---

Basic tracing

Native ingest maps to POST /api/traces.

from aitracer import trace, configure

configure(api_key="akt_xxx", base_url="https://your-app.example.com")

trace.record(
    workflow="customer-support-agent",
    model="gpt-4.1",
    input_data={"query": "Status?"},
    output_data={"answer": "Shipped."},
    metrics={"promptTokens": 50, "completionTokens": 20, "latencyMs": 400},
)

---

Async tracing

For FastAPI, async agents, and event loops, install httpx:

pip install 'aitracer-sdk[async]'

import asyncio
from aitracer import AsyncAITracerClient, configure_async, trace

async def main():
    configure_async(api_key="akt_xxx", base_url="https://your-app.example.com")
    await trace.arecord(
        workflow="async-job",
        model="gpt-4.1",
        input_data={"q": "hello"},
        output_data={"a": "world"},
    )

asyncio.run(main())

AsyncAITracerClient uses async retries with exponential backoff on timeouts, connection errors, and 429 / 502 / 503 / 504.

---

Batch ingestion

Sync (thread pool, preserves order):

from aitracer import trace, configure

configure(api_key="akt_xxx", base_url="https://your-app.example.com")

trace.batch_record(
    [
        {"workflow": "bulk-1", "model": "gpt-4o-mini", "input_data": {"i": 1}},
        {"workflow": "bulk-2", "model": "gpt-4o-mini", "input_data": {"i": 2}},
    ],
    max_workers=8,
)

Async (bounded concurrency):

await trace.batch_arecord(
    [{"workflow": "a", "model": "gpt-4o"}, {"workflow": "b", "model": "gpt-4o"}],
    concurrency=8,
)

---

CLI

After install, the aitracer command is available:

aitracer init                    # print starter AITRACER_* env vars
export AITRACER_API_KEY=akt_...
export AITRACER_BASE_URL=https://your-app.example.com
aitracer test-ingest             # POST a minimal trace
aitracer verify <trace_id>       # best-effort lookup (depends on auth mode)

---

HTTP errors & observability

AITracerHTTPError includes request_id, correlation_id, url, attempts_made, and a response_body excerpt. Use exc.diagnostics() for structured logging.

---

Verification

Many deployments embed verification records on the trace ingest response (there is often no standalone public verification URL).

from aitracer import verify

records = verify.records_from_response(ingest_response)
digest = verify.hash("your-trace-id")

---

Governance (Guardrails / policy)

Normalized guardrail and policy outcomes → POST /api/integrations/openai-guardrails.

from aitracer import governance, configure

configure(api_key="akt_xxx", base_url="https://your-app.example.com")

governance.report_policy_event(
    guardrail_name="pii",
    trigger_type="pii_detected",
    trigger_source="user_input",
    action_taken="blocked",
    trace_id="optional-public-trace-slug",
)

---

Cost intelligence

There is often no standalone cost HTTP API; token and spend fields are carried on trace / execution payloads. Helpers aggregate what you already ingested or listed:

from aitracer import cost

summary = cost.from_ingest_response(ingest_response)
rollup = cost.aggregate(list_of_trace_dicts)
spend_by_model = cost.get_model_spend(list_of_trace_dicts)

---

OpenAI Agents integration

POST /api/integrations/openai-agents

from aitracer.integrations.openai_agents import register_tracing, export_spans

cfg = register_tracing(
    base_url="https://your-app.example.com",
    api_key="akt_xxx",
)
export_spans({"traceId": "...", "spans": [...]}, config=cfg)

---

OTLP integration

POST /api/integrations/otlp/v1/traces (JSON encoding).

from aitracer.integrations.otlp import export_resource_spans, collector_exporter_yaml_snippet
from aitracer import AITracerClient

client = AITracerClient(api_key="akt_xxx", base_url="https://your-app.example.com")
export_resource_spans({"resourceSpans": [...]}, client=client)

print(collector_exporter_yaml_snippet(base_url="https://your-app.example.com", api_key="akt_xxx"))

---

Documentation

• Product docs: https://aitracer.app/docs
• Developer reference (OpenAPI): https://aitracer.app/developers (or /developers on your deployment)

---

Examples

Runnable samples live in examples/ (set AITRACER_API_KEY and optional AITRACER_BASE_URL).

---

Local smoke install

./scripts/smoke-install-test.sh local

---

Roadmap (toward “production hardening”)

Shipped in 0.3.0: async client, batch ingest, richer HTTP errors, CLI stubs, trace.astream / webhooks placeholders, contrib/ for framework adapters.

Still the main leverage points for enterprise adoption:

Area	Status
Async (AsyncAITracerClient, trace.arecord, batch_arecord)	Implemented ([async] extra)
Batch (trace.batch_record)	Implemented
Streaming (trace.astream)	Not implemented — needs platform ingest contract
Framework wrappers (LangChain, LlamaIndex, CrewAI, FastAPI middleware, …)	contrib/ placeholder only
CLI	init, test-ingest, verify
Error observability	Request/correlation IDs, URL, attempts, diagnostics()
Pydantic models	Optional future extra (validation / IDE ergonomics)
Webhooks	handle_verification_webhook placeholder
MCP / enterprise policy	Future layers on top of stable client / trace APIs

---

License

MIT — see repository for details.