OpenInference Together AI Instrumentation

pypi

Python auto-instrumentation library for the Together AI Python client.

Chat completion calls made with the together client (Together and AsyncTogether) are traced and exported as OpenInference LLM spans, capturing the input messages, output messages, invocation parameters, tool calls, streaming output, and token counts.

These traces are fully OpenTelemetry compatible and can be sent to an OpenTelemetry collector for viewing, such as Arize Phoenix or Arize AX.

Supported Features

  • Synchronous and asynchronous chat completions (Together and AsyncTogether)
  • Streaming (stream=True): the span stays open until the stream is consumed, and the accumulated output, tool calls, and token counts are recorded from the streamed chunks
  • Tool calls, captured on both requests and responses
  • Suppressing tracing via suppress_tracing()
  • Context attribute propagation (using_session, using_user, using_attributes, metadata, tags)
  • Sensitive-data masking via TraceConfig (e.g. hide_inputs)

Requires together >= 2.0.0.

Installation

pip install openinference-instrumentation-together

PyPI package: openinference-instrumentation-together

Quickstart

pip install openinference-instrumentation-together together arize-phoenix opentelemetry-sdk opentelemetry-exporter-otlp

Start Phoenix as a collector (default http://localhost:6006), then:

from openinference.instrumentation.together import TogetherInstrumentor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import SimpleSpanProcessor

endpoint = "http://127.0.0.1:6006/v1/traces"
tracer_provider = TracerProvider()
tracer_provider.add_span_processor(SimpleSpanProcessor(OTLPSpanExporter(endpoint)))

TogetherInstrumentor().instrument(tracer_provider=tracer_provider)

Run a chat completion. Set the TOGETHER_API_KEY environment variable with your key.

from together import Together

client = Together()
response = client.chat.completions.create(
    model="meta-llama/Llama-3.3-70B-Instruct-Turbo",
    messages=[{"role": "user", "content": "Why is the sky blue?"}],
)
print(response.choices[0].message.content)

Streaming works the same way — the span is finished when the stream is exhausted:

stream = client.chat.completions.create(
    model="meta-llama/Llama-3.3-70B-Instruct-Turbo",
    messages=[{"role": "user", "content": "Write a haiku about observability."}],
    stream=True,
)
for chunk in stream:
    if chunk.choices and chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)

Runnable examples — including async usage, streaming with a reasoning model, and tool calls — are in the examples/ directory.

More Info

Project links

Data verified by PyPI on
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Latest release

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for arizeai from gravatar.com arizeai

Credits

Author: OpenInference Authors

GitHub Statistics

Data verified by PyPI on 
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

Apache-2.0
View SPDX License List

Requires

Python <3.15, >=3.10

Provides Extra

instruments

Classifiers

Report project as malware

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

openinference_instrumentation_together-0.1.2.tar.gz (12.9 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

openinference_instrumentation_together-0.1.2-py3-none-any.whl (17.7 kB view details)

Uploaded Python 3

File details

Details for the file openinference_instrumentation_together-0.1.2.tar.gz.

File metadata

File hashes

Hashes for openinference_instrumentation_together-0.1.2.tar.gz
Algorithm Hash digest
SHA256 4dc334d126a6b6d6ea912734f9086e8fcf69ae3aceeb758429fe02088f0dbd5e
MD5 a77aedd7ce3d24b1a4e169fb0adeea4f
BLAKE2b-256 1dc8992356dcdb341cb56a35f5daed1ad0569173650924c32a9c6834e30ce433

See more details on using hashes here.

Provenance

The following attestation bundles were made for openinference_instrumentation_together-0.1.2.tar.gz:

Publisher: publish.yaml on Arize-ai/openinference

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file openinference_instrumentation_together-0.1.2-py3-none-any.whl.

File metadata

File hashes

Hashes for openinference_instrumentation_together-0.1.2-py3-none-any.whl
Algorithm Hash digest
SHA256 09bea1eba27645c177dc49a5ef0a04714b209cf2da9da734a0dca257db22807f
MD5 770f7b528a6a6b5518e76658bff144da
BLAKE2b-256 0cb13bbaba5195476dea585aebd69350b7d3649a98724fe7097e29fb55f2f078

See more details on using hashes here.

Provenance

The following attestation bundles were made for openinference_instrumentation_together-0.1.2-py3-none-any.whl:

Publisher: publish.yaml on Arize-ai/openinference

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page