Adrian Cole codefromthecrypt

rename elasticsearch to elasticsearch_real and change its port to 9201 rename otel-collector to otel-collector_real and change its port to 4319

add this container with the below flow.py. Then make sure you are sending with javascript as python doesn't support OTEL_EXPORTER_OTLP_PROTOCOL=http/json yet.

then, you can do docker compose logs elasticsearch and see the POSTs.

  elasticsearch:
    image: mitmproxy/mitmproxy

Source diagram of this LI post on goose https://www.linkedin.com/posts/adrianfcole_genai-opensource-activity-7315217199844003840-sX7Q

sequenceDiagram
    participant CLI as Goose CLI
    participant MCP as MCP Server (developer)
    participant LLM

    Note over CLI: User runs `goose run -t "list the files in this directory"`
    Note right of CLI: Forks own binary to start MCP server for "developer" via stdio

Check out the PR branch, then update the tree of deps in one of the examples like this in requirements.txt

openai~=1.63.2

opentelemetry-api@git+https://github.com/open-telemetry/opentelemetry-python.git#egg=opentelemetry-api&subdirectory=opentelemetry-api
opentelemetry-sdk@git+https://github.com/open-telemetry/opentelemetry-python.git#egg=opentelemetry-sdk&subdirectory=opentelemetry-sdk
opentelemetry-semantic-conventions@git+https://github.com/open-telemetry/opentelemetry-python.git#egg=opentelemetry-semantic-conventions&subdirectory=opentelemetry-semantic-conventions
opentelemetry-proto@git+https://github.com/open-telemetry/opentelemetry-python.git#egg=opentelemetry-proto&subdirectory=opentelemetry-proto
opentelemetry-exporter-otlp-proto-common@git+https://github.com/open-telemetry/opentelemetry-python.git#egg=opentelemetry-exporter-otlp-proto-common&subdirectory=exporter/opentelemetry-exporter-otlp-proto-common

	# Add OpenAI and OpenTelemetry ENV variables to .env and run like this:
	# uv run -q --env-file .env main.py
	#
	# Note: Use a larger model like qwen3b:14b, if you are hosting your own models.
	#
	# /// script
	# requires-python = ">=3.13"
	# dependencies = [
	# "openai-agents",
	# "elastic-opentelemetry",

	curl -s -X POST https://api.openai.com/v1/responses \
	-H "Content-Type: application/json" \
	-H "Authorization: Bearer ${OPENAI_API_KEY}" \
	-d '{
	"input": [
	{
	"content": "Which language is this repo written in?",
	"role": "user"
	}
	],

	# e.g. to run with ollama, do this:
	# OPENAI_BASE_URL=http://localhost:11434/v1 OPENAI_API_KEY=unused CHAT_MODEL=qwen3:1.7b uv run a2a.py
	#
	# /// script
	# requires-python = ">=3.13"
	# dependencies = [
	# "a2a-sdk",
	# "uvicorn",
	# "openai",
	# "httpx"

	import os

	import httpx
	from google.adk.agents import Agent
	from google.adk.models.lite_llm import LiteLlm
	from google.adk.runners import Runner
	from google.adk.sessions import InMemorySessionService
	from google.genai import types

	# TODO: native openai https://github.com/google/adk-python/issues/27

	import os

	import httpx
	import logfire
	from agents import Agent, ModelSettings, OpenAIProvider, RunConfig, Runner, function_tool
	from agents.tracing import GLOBAL_TRACE_PROVIDER

	# Shut down the global tracer as it sends to the OpenAI "/traces/ingest"
	# endpoint, which we aren't using and doesn't exist on alternative backends
	# like Ollama.

	#!/bin/sh
	set -e

	# guess OS_TYPE if not provided
	if [ -z "$OS_TYPE" ]; then
	case "$(uname -s \| tr '[:upper:]' '[:lower:]')" in
	cygwin_nt\|mingw\|msys_nt*)
	OS_TYPE="windows"
	;;
	linux*)

	ARG DOTNET_VERSION=9.0

	FROM mcr.microsoft.com/dotnet/sdk:${DOTNET_VERSION}-alpine AS edot
	ARG EDOT_VERSION=1.0.0-beta.1
	ARG EDOT_INSTALL=https://github.com/elastic/elastic-otel-dotnet/releases/download/${EDOT_VERSION}/elastic-dotnet-auto-install.sh
	ENV OTEL_DOTNET_AUTO_HOME=/edot
	WORKDIR /edot
	RUN sh -c "$(curl -fsSL ${EDOT_INSTALL})"

	FROM mcr.microsoft.com/dotnet/sdk:${DOTNET_VERSION}-alpine AS app