metergraph (Python)

Zero-runtime-dependency capture for OpenAI, Anthropic, Gemini, and Python Vercel AI Gateway clients. Initialize Metergraph once, then wrap each provider client. init() reads the token and other omitted options from the environment.

Initialization is process-wide. The first init() configuration remains active; later explicit calls are ignored and produce one generic warning without option names, token values, or other secrets.

import metergraph
from openai import OpenAI

metergraph.init(repository="owner/repository")
# Anthropic() and google-genai's genai.Client() wrap the same way.
client = metergraph.wrap(OpenAI())

with metergraph.context(
    session_id="ticket-123",
    tags={"customer": "acme"},
):
    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,
)

Use metergraph.context() for request or job identity. It follows async work created inside the scope and is restored afterward, so concurrent and reused workers cannot leak session IDs or tags into one another. The narrower metergraph.session() and metergraph.tags() scopes compose with it. metergraph.set_default_tags() sets process-wide service metadata. Legacy set_session() and set_tags() calls only update an active Metergraph scope; outside one they warn once and do nothing.

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

metergraph.init(repository="owner/repository")
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.

OpenTelemetry GenAI export

MetergraphGenAIExporter is a standard OpenTelemetry span exporter for GenAI semantic-convention spans. The currently qualified integration is LiteLLM: install the optional integration and configure MeterGraph as LiteLLM's custom exporter. Existing LiteLLM call sites remain unchanged and must not also be wrapped.

python -m pip install 'metergraph[otel]' 'litellm[proxy]>=1.96.2,<2'

import litellm
from litellm.integrations.opentelemetry import OpenTelemetry, OpenTelemetryConfig
from metergraph.opentelemetry import MetergraphGenAIExporter

litellm.callbacks.append(OpenTelemetry(OpenTelemetryConfig(
    exporter=MetergraphGenAIExporter(),
    capture_message_content="SPAN_ONLY",
)))

The exporter preserves OpenTelemetry trace identity, model/provider metadata, token usage, latency, system instructions, ordered messages, and text output. Message content is explicitly enabled because it may be sensitive. Text parts are retained and replayable in the current POC pipeline. Calls containing other part types retain their model, usage, timing, and status metadata, but those parts are not replayable yet. See the runnable python-litellm-otel example.

Configuration:

• METERGRAPH_APP_TOKEN — required bearer token
• METERGRAPH_INGEST_URL — optional override; defaults to the hosted HTTPS endpoint
• METERGRAPH_REPOSITORY — optional owner/repository identity; used by MeterGraph Bot
• METERGRAPH_CAPTURE_TEXT=0 — opt out of content capture globally
• METERGRAPH_TEXT_MAX_BYTES — per-field content limit; defaults to 1 MiB
• METERGRAPH_DISABLED=1 — process kill switch
• METERGRAPH_QUEUE_SIZE, METERGRAPH_BATCH_SIZE, METERGRAPH_FLUSH_SECONDS

Repository identity enables repository-level attribution and MeterGraph Bot. Choose any one of these options; each is sufficient on its own:

• Pass repository="owner/repository" to metergraph.init().
• Set METERGRAPH_REPOSITORY=owner/repository in the environment.
• Store the identity with the source code in .metergraph/config.json:

{"repository":"owner/repository"}

Resolution order is the explicit option, environment variable, then configuration file. The SDK treats the file as read-only. Without repository identity, it warns once and continues capture without repository attribution.

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 4 MiB after optional gzip. By default, Metergraph captures the scrubbed provider request and a normalized response envelope, including assistant content and tool calls. Provider credentials and transport headers are removed. Request and response are each limited to 1 MiB of UTF-8 by default with an explicit truncation marker. Set METERGRAPH_TEXT_MAX_BYTES or initialize with text_max_bytes=... to raise the per-field limit for larger prompts and responses. 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.
Before wrapping, call metergraph.init(repository="owner/repository") using the
actual GitHub owner and repository name. METERGRAPH_APP_TOKEN is required; the
SDK warns and disables capture when it is missing. METERGRAPH_INGEST_URL is
only for self-hosted servers. Document variable names with placeholders in
.env.example, and put real values only in deployment configuration. Metergraph
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.