convergent-sdk

Records the runs of your Python AI agents as OpenTelemetry spans and sends them to your Convergent workspace.

Install

pip install convergent-sdk

For the APIs in these docs, use convergent-sdk>=0.0.10,<0.1. While the SDK is on 0.x, a minor release may change the public API, and the upper bound keeps such a release out of a routine dependency update. Stability states the full policy.

Set your key and release

Mint an ingestion key at app.convergent.dev/workspace/settings.

export CONVERGENT_API_KEY="cvk_xxxxxxxxxxxxxxxx"

export GIT_SHA=$(git rev-parse --short HEAD)

CONVERGENT_API_KEY is required for network export. A local File(...) destination needs no API key. Store ingestion keys in your deployment's secret settings.

GIT_SHA is an example variable read by init(release=...) below. A release is required; use a Git SHA, build ID, or immutable image tag. You can also set it with CONVERGENT_RELEASE.

The session() helper requires SDK 0.0.9 or newer. Read the 0.0.8 upgrade guide before upgrading.

Instrument with a coding agent

If you're instrumenting Convergent for the first time, the fastest way to do so is give your coding agent this prompt.

Install the convergent-instrument and convergent-verify skills from the github repository convergent-technologies/convergent-sdk.

convergent-instrument is a skill to instrument your agent, and convergent-verify is a skill to inspect each recording. You will be given the agent file or directory to instrument, as well as the instructions or command that runs the agent for the representative run. Use both skills to instrument and verify your agent. Continue using both skills until the recording has no unresolved instrumentation issue.

Agent to instrument: <file or directory>
Representative run: <command or instructions that run the agent>

If you've already instrumented with Convergent, and are just looking to verify your setup, give your coding agent this prompt:

Install the convergent-instrument and convergent-verify skills from the github repository convergent-technologies/convergent-sdk.

convergent-instrument is a skill to instrument your agent, and convergent-verify is a skill to inspect each recording. You will be given the agent file or directory that is already instrumented, as well as the instructions or command that runs the agent for the representative run. Use both skills to verify the existing instrumentation, and fix each evidence-backed instrumentation issue. Rerun and verify until the recording has no unresolved instrumentation issue.

Agent to verify: <file or directory that is already instrumented>
Representative run: <command or instructions that run the agent>

Alternatively, you can also install the skills directly in your coding agent using:

npx skills add convergent-technologies/convergent-sdk

You'll have to restart your coding agent session to use the skills directly, but can do so with slash commands:

/convergent-instrument
/convergent-verify

Example: Trace a run

Call init() once at startup, and put agent() on the function that handles one request. The model call inside it is the one your app already makes, and this one is the OpenAI client. Save it as app.py.

import os

from openai import OpenAI

import convergent

convergent.init(release=os.environ["GIT_SHA"])

client = OpenAI()
MODEL = "gpt-5.5"


@convergent.agent(name="support-agent")
def answer(question: str) -> str:
    with convergent.span(name=MODEL, operation="model_call") as call:
        call.set_input({"question": question})
        completion = client.chat.completions.create(
            model=MODEL,
            messages=[{"role": "user", "content": question}],
        )
        reply = completion.choices[0].message.content
        call.set_output({"answer": reply})
        call.set_attribute("gen_ai.request.model", MODEL)
        call.set_attribute("gen_ai.usage.input_tokens", completion.usage.prompt_tokens)
        call.set_attribute("gen_ai.usage.output_tokens", completion.usage.completion_tokens)
    return reply


print(answer("Where is my invoice?"))
convergent.flush()

From your command line, run:

pip install openai
export OPENAI_API_KEY=sk-xxxxxxxxxxxxxxxx
export GIT_SHA=$(git rev-parse --short HEAD)
python app.py

That records one agent run with one model call inside it, carrying the prompt, the answer, the model, and the token counts. Any other model client goes in the same place, and the trace keeps the same shape.

Full Documentation

The full documentation is under python/docs/: confirming a run arrived with check(), instrumenting your agent by hand, attaching to an existing OpenTelemetry setup, the integration packages, configuration, the API reference, the attribute spellings Convergent reads, and troubleshooting.

skills/convergent-instrument/ and skills/convergent-verify/ are the skills from the coding-agent section above.

python/examples/ holds runnable examples: the run above as a self-contained file that needs no OpenAI key, and one trace recorded across a dispatcher and three worker processes.