Adrian Cole codefromthecrypt

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

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

	#!/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*)

	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.

	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

	# 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"

	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"
	}
	],

	# 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",