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

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 0.3 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.

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 0.3 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.