metergraphrelay

Try metergraph without installing its SDK: export the chat completions OpenAI already stores for you, and push them into metergraph.

1. Your app calls OpenAI with store=True — a one-line addition if it doesn't already (see below). OpenAI keeps the completion server-side.
2. metergraphrelay pull openai lists those stored completions via OpenAI's own API and writes them as JSONL, already shaped to metergraph's native trace schema.
3. metergraphrelay push uploads that file to metergraph.

No SDK, no instrumentation beyond the store=True flag — which is also what OpenAI's own dashboard and evals features use.

This only reads what's associated with the API key you provide; it can't see or export anyone else's data. Built on OpenAI's List Chat Completions API — see the API reference for the underlying data model.

Setup

pip install metergraphrelay

Create a .env file in your working directory:

OPENAI_API_KEY=sk-...
METERGRAPH_APP_TOKEN=...

Quickstart

metergraphrelay pull openai -n 25 --output traces.jsonl
metergraphrelay push traces.jsonl

Or do both in one step:

metergraphrelay sync openai -n 25 --output traces.jsonl

No stored completions yet? Generate a couple first:

metergraphrelay demo openai

Enabling storage on your own calls

pull openai only finds completions created with store=True. Add it to calls you're already making:

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "..."}],
    store=True,
    metadata={"source": "my-app"},  # optional, filterable later
)

Before enabling this in production:

• Stored completions include full request/response content by default.
• There's no automatic expiry from OpenAI's side — delete via their Delete chat completion API; metergraphrelay doesn't do this for you.

Commands

metergraphrelay pull openai -n 25 --output my-traces.jsonl --stdout --include-content --route my-app/support-bot
metergraphrelay demo openai --model gpt-4o-mini
metergraphrelay push traces.jsonl
metergraphrelay sync openai -n 25 --output my-traces.jsonl --route my-app/support-bot

sync openai accepts the same flags as pull openai. It pulls to --output and immediately pushes that same file, checking both OPENAI_API_KEY and METERGRAPH_APP_TOKEN upfront so it fails fast instead of pulling data it can't push.

pull anthropic accepts the same shape but isn't implemented yet — it checks for ANTHROPIC_API_KEY and reports accordingly. pull langfuse is implemented — see Pull from Langfuse below.

All subcommands accept --env-file PATH.

Trace record shape

Each line of pull openai's output is a JSON object already shaped for metergraph's ingest API:

{
  "ts": "2026-07-30T12:00:00+00:00",
  "provider": "openai",
  "model": "gpt-4o-mini",
  "status": "success",
  "endpoint": "chat.completions",
  "input_tokens": 12,
  "output_tokens": 34,
  "error": false,
  "error_type": null,
  "request_id": "chatcmpl-...",
  "tags": {},
  "route": "openai/backfill",
  "content_opted_in": false,
  "request_json": null,
  "response_text": null,
  "sdk": "metergraphrelay",
  "sdk_version": "0.1.3"
}

request_json/response_text are populated only when --include-content is passed.

Every completion returned by the stored-completions list already succeeded, so status is always "success". error/error_type flag a partial record: --include-content was requested but the follow-up message fetch failed, so token counts are still real while the content is missing.

Pull from Langfuse

Import Langfuse GENERATION observations (the LLM call records Langfuse captures) into the same metergraph-native JSONL shape as pull openai. Only GENERATION observations are imported — Langfuse SPAN/EVENT observations and Scores/evals are never imported. Requires Langfuse Cloud or self-hosted v4+ (the version serving the v2 Observations API); older self-hosted deployments are not supported.

Setup: add your Langfuse keys to .env:

LANGFUSE_PUBLIC_KEY=pk-lf-...
LANGFUSE_SECRET_KEY=sk-lf-...

By default this talks to Langfuse Cloud. For a self-hosted instance, set LANGFUSE_BASE_URL in .env (or pass --base-url per-command):

LANGFUSE_BASE_URL=https://your-langfuse-instance.example.com

Quickstart:

metergraphrelay pull langfuse -n 25 --output traces.jsonl
metergraphrelay push traces.jsonl

With no other flags, this imports the latest 100 GENERATION observations overall (not 100 distinct traces).

Narrowing what gets pulled, beyond -n/--count:

metergraphrelay pull langfuse --since 2026-08-01T00:00:00Z --until 2026-08-07T00:00:00Z
metergraphrelay pull langfuse --trace-name support-bot-reply --trace-name billing-bot-reply --tag prod --tag tier-1

• --trace-name matches Langfuse's trace name — the closest Langfuse concept to a workflow or use case (e.g. "support-bot-reply"). It's repeatable; multiple --trace-name values are OR'd together (any match).
• --tag matches Langfuse trace tags — commonly used as customer-defined categories (a tenant, an experiment cohort, a priority tier); this is a convention, not something Langfuse enforces. It's repeatable; multiple --tag values require all of them to be present (AND). --tag only matches tags that already exist on your historical data — it can't require a tag that was never set, and if you don't pass --tag at all, there's no tag-based narrowing (not "untagged only").
• --trace-name, --tag, --environment, and --since/--until all combine with each other using AND.
• --count is always a cap on the number of GENERATION observations imported, never a count of distinct traces.

Before running this against your own data: pull langfuse transfers every matched generation's prompt/response content from Langfuse into your local JSONL file, and from there into metergraph via push, with no separate opt-in step — unlike pull openai's --include-content flag, there is no way to pull Langfuse generations without their content.

Full flag reference: metergraphrelay pull langfuse --help.

Development

git clone https://github.com/VasiliyRad/metergraphrelay
cd metergraphrelay
pip install -e ".[dev]"
pytest