metergraph (Python)

Zero-runtime-dependency capture for OpenAI, Anthropic, Gemini, and Python Vercel AI Gateway clients. wrap() initializes capture from the environment, so setup is one line per client; call metergraph.init(...) before the first wrap() only to pass options in code.

import metergraph
from openai import OpenAI

# Anthropic() and google-genai's genai.Client() wrap the same way.
client = metergraph.wrap(OpenAI())
metergraph.set_session("ticket-123")

with metergraph.trace("ticket-workflow"):
    with metergraph.route("ticket-classifier", unit="answer"):
        model = metergraph.model_for("ticket-classifier", default="gpt-4.1-mini")
        client.chat.completions.create(model=model, messages=[...])

# Emit this after the user-visible task resolves. It shares the bounded async
# transport and contains no prompt or output content.
metergraph.record_outcome(
    "ticket-classifier",
    model=model,
    task_completed=True,
    feedback_score=1,
    turns_to_resolution=2,
    escalated=False,
)

Vercel's supported Python surface is AI Gateway through the OpenAI or Anthropic SDK. Point either client at the public gateway and wrap() detects it automatically:

import os
import metergraph
from openai import OpenAI

gateway = metergraph.wrap(OpenAI(
    api_key=os.getenv("AI_GATEWAY_API_KEY") or os.getenv("VERCEL_OIDC_TOKEN"),
    base_url="https://ai-gateway.vercel.sh/v1",
))

gateway.chat.completions.create(
    model="anthropic/claude-sonnet-4.6",
    messages=[{"role": "user", "content": "Hello"}],
)

Creator-qualified model IDs are normalized for gateway catalog pricing. Sync, async, streaming, tool calls, and OpenAI Responses API calls are captured. Use metergraph.wrap(client, provider="vercel") only when a compatible client is behind a custom gateway URL that cannot be detected automatically.

Configuration:

• METERGRAPH_APP_TOKEN — required bearer token
• METERGRAPH_INGEST_URL — optional override; defaults to the hosted HTTPS endpoint
• METERGRAPH_CAPTURE_TEXT=0 — opt out of content capture globally
• METERGRAPH_DISABLED=1 — process kill switch
• METERGRAPH_QUEUE_SIZE, METERGRAPH_BATCH_SIZE, METERGRAPH_FLUSH_SECONDS

SDK 0.4 associates traces with their GitHub repository automatically. On the first init() in a Git checkout, it reads the origin remote and creates .metergraph/config.json at the repository root if that file is absent. Commit this non-secret file so production can use repository-aware ingest without Git metadata. An existing file is authoritative and is never changed by the SDK. If discovery or creation is unavailable, ingest remains compatible with protocol v1.

Delivery is bounded and off the request path. Queue overflow or a collector outage drops capture and increments internal counters; it never changes the provider call. Each wire batch is bounded to 512 KiB after optional gzip. SDK 0.4 captures the scrubbed provider request and a normalized response envelope, including assistant content and tool calls, by default. Provider credentials and transport headers are removed. Request and response are each limited to 100 KiB of UTF-8 with an explicit truncation marker. capture_text=False on route() or trace() overrides the global content policy for a sensitive operation. The equivalent initialization option is metergraph.init(capture_text=False). The public open-source server continues to discard content even when the SDK sends it; the hosted dashboard retains content under the workspace retention period.

metergraph.trace(name, trace_id=..., parent_span_id=...) is a sync/async context manager and decorator. Calls inside one trace share a trace ID and receive distinct span IDs. Calls outside a trace become deterministic single-span traces after ingestion. Manual IDs can join work across process boundaries; automatic W3C HTTP propagation is not included.

Config reads are ETag-aware and fail open to the default model. record_outcome requires a stable session ID and the model actually used so a session-sticky canary can compare task completion and optional feedback, turn-count, escalation, abandonment, edit-distance, and regeneration signals.

OpenAI Batch API output JSONL is captured per inference when a wrapped client.files.content() / retrieve_content() result is read. Anthropic message batches are captured per inference while iterating a wrapped client.messages.batches.results() result. Run result consumption inside a route() context so the asynchronous batch retains its product route. Batch rows carry real per-result usage and the batch pricing flag; job-management polls themselves are not miscounted as model calls.

Batch-first execution (opt-in)

metergraph.batch_first() is a separate, explicitly opt-in code path from wrap()/capture: submit one request through a provider's Batch API, wait up to a caller-chosen deadline, and fall back to exactly one direct call if the batch hasn't finished in time. It is synchronous/blocking, matching this SDK's own background-work model — a daemon thread, not asyncio.

import metergraph
from openai import OpenAI

client = OpenAI()  # unwrapped — batch_first() drives it directly, not through wrap()

outcome = metergraph.batch_first(
    client, "openai",
    {"model": "gpt-5-mini", "input": "Summarize this document."},
    deadline_seconds=60,
    accept_duplicate_provider_execution=True,  # required: a missed deadline can execute the request twice
    on_late_batch_settled=lambda info: None,   # fires later, in the background, only for a losing batch
)

outcome.source                  # "batch" | "direct"
outcome.result                  # the provider response
outcome.metadata.batch_outcome  # "completed" | "failed" | "expired" | "pending_at_deadline"

provider is "openai" | "anthropic" | "google", matching wrap()'s own explicit-provider option — never inferred from the client instance. A request with stream=True is rejected before any provider call. A request carrying tools is rejected unless allow_duplicate_tool_call_plans=True is also set, acknowledging that the batch result and the direct fallback are independent provider executions that may each choose a different tool-call plan. accept_duplicate_provider_execution must be exactly True — there is no default and no environment-variable override, and a missed deadline can execute (and bill) the same request twice. Neither the batch nor the direct path executes a tool call automatically; the caller receives the tool-call plan in outcome.result and is responsible for executing it, exactly as with a normal (non-batch-first) provider response.

batch_first() is not integrated with wrap()'s capture/telemetry pipeline; its result and metadata are returned directly to the caller, never enqueued for delivery. The background poll that watches a losing batch for late telemetry runs on a daemon thread, which does not keep the process alive — a short-lived script may exit before on_late_batch_settled ever fires, silently dropping that signal. This milestone's adapters call their provider client's methods synchronously; AsyncOpenAI, AsyncAnthropic, and google-genai's .aio namespace are not supported.

OpenAI, Anthropic, and Google Gemini all have adapters (create_openai_batch_adapter, create_anthropic_batch_adapter, create_google_batch_adapter), built against each provider's real SDK method signatures — verified by inspecting openai, anthropic, and google-genai as installed from this package's own dev extras, not by a live call — but none has been exercised against a live provider Batch API from this SDK. Treat this as a beta-quality, code-reviewed-but-not-live-verified surface.

Set up with an AI coding agent

Paste this into Claude Code, Codex, Cursor, or any coding agent inside the codebase you want instrumented:

Instrument this codebase's LLM API costs with the `metergraph` PyPI package
(https://github.com/PioneerSquareLabs/metergraphsdk): pip install metergraph,
then wrap every OpenAI()/AsyncOpenAI(), Anthropic()/AsyncAnthropic(), and
genai.Client() construction in place, e.g. client = metergraph.wrap(OpenAI()).
OpenAI or Anthropic clients pointed at https://ai-gateway.vercel.sh are Vercel
AI Gateway clients and are detected automatically; keep their creator/model ID
and AI_GATEWAY_API_KEY / VERCEL_OIDC_TOKEN configuration unchanged.
wrap() returns the same client and initializes itself from the environment:
METERGRAPH_APP_TOKEN is required (capture is silently off without it) and
METERGRAPH_INGEST_URL is only for self-hosted servers. Add both to
.env.example, and never commit a real token. SDK 0.4 captures scrubbed provider
requests and normalized responses by default for the hosted dashboard; use
METERGRAPH_CAPTURE_TEXT=0 or capture_text=False around sensitive operations.
Provider credentials and transport headers must never be captured. Capture is
fail-open, so do not change call sites, arguments, or error handling; sync,
async, and streaming work unchanged. Use metergraph.trace("stable-name") to
group multi-call workflows. Attribution to the calling function is automatic;
optionally pin stable names on key LLM-calling functions with
@metergraph.track. On
serverless, call metergraph.flush() before the handler returns. When done,
list every client you wrapped and flag LLM calls made outside the official
openai / anthropic / google-genai SDKs, since those are not captured.