Most traders think an elite prop firm wants one thing on Day 1: make us millions of dollars. What we actually expect may surprise you. Over Sarabeth’s UWS breakfast, I was checking my inbox when I saw a response from @robertoclaroar1. Roberto is one of the most likable developing traders you will ever meet—you root for him the way a New Englander roots for the Patriots. After years of relentless training and building out an impressive Breaking News systematic strategy, he officially earned a seat on the SMB desk.👏👏👏👏👏 His response to the invite? "Thank you Mike. And I will show my gratitude with results and progress." In the past, new invites often came with vows to make the firm millions. Roberto’s response gave me that familiar gut check: it’s time to set the record straight. When you earn a seat on our desk, we do not expect immediate P&L fireworks. We expect you to: 1) Collaborate. Build an alert for a peer. Share an idea that fits a senior trader’s PlayBook. Dissect a missed setup openly with the desk. 2) Master the daily inputs. a) Build a PlayBook b) Daily Report Card c) Trading Journal d) Build technology e) Collaborate with colleagues f) Bring your best self to markets- eat, sleep, exercise, breathe. g) Backtest trading ideas 3) Make progress. By Year 1 build a PlayBook of your best setups, show PnL progress, and have real relationships on the desk. Do that, and you've earned your next year. 4) Be curious, open-minded, and resilient. If you do all of these and lose money we will NOT be disappointed. We will help you transition into another job where your experience with us will be valued. 5) Accept mentoring and coaching. Trust the process. We’ve been developing star traders since 2005. Lean on our capital, tech, and team. Giving your best professional effort is all that is expected. Doubling and tripling down on a solid process that has yielded results for many before you is all that is expected. The rest will take care of itself at SMB and in the markets. Amazing work Roberto! #PropTrading# #TradingProcess# #SMBCapital# #DayTrading#

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# Practical and Useful Patterns for OpenAI Agent SDK 🌍 Customize where your traces go. Send alongside OpenAI to Langfuse or Datadog, or replace the backend entirely -- seamlessly integrate with your existing observability stack. Custom processors give you full control over trace routing, transformation, and filtering. 📌 Title: Tracing -- Custom processors / Ecosystem 🔗 URL: 🧩 Overview The OpenAI Agent SDK's tracing is fully extensible through custom processors. Use `add_trace_processor()` to add custom processing alongside the OpenAI backend, or `set_trace_processors()` to completely replace the destination with Langfuse, Datadog, MLflow, Arize, or Braintrust. Even when using non-OpenAI models, `set_tracing_export_api_key()` enables free trace visualization. 🛠 How to Use Import `add_trace_processor` and `TracingProcessor` from `agents`. Define a `MyProcessor` class extending `TracingProcessor` with `on_trace_start`, `on_trace_end`, `on_span_start`, `on_span_end`, and `shutdown` methods. Access ` `trace.duration_ms`, and ` for trace data. Register with `add_trace_processor(MyProcessor())` to run custom processing alongside the OpenAI backend. 🏗 Practical Usage Patterns **Parallel Export with add_trace_processor()** Keep the OpenAI Traces dashboard while also sending data to your own observability platform. Extend `TracingProcessor` to create a `DatadogProcessor`. In `on_trace_start`, call `dd_client.start_span(operation_name="agent.trace", resource= In `on_trace_end`, call `dd_client.finish_span(metadata={"duration_ms": trace.duration_ms})`. In `on_span_start`, use `operation_name=f"agent.{span.type}"`. In `shutdown`, call `dd_client.flush()`. Register with `add_trace_processor(DatadogProcessor(dd_client))` to send traces to Datadog in parallel with OpenAI. **Complete Backend Replacement with set_trace_processors()** When you don't want traces going to OpenAI, replace the processor entirely. Import `set_trace_processors` from `agents` to completely replace the trace destination. Use `set_trace_processors([LangfuseTracingProcessor()])` for Langfuse, `[MLflowTracingProcessor()]` for MLflow, `[ArizeTracingProcessor()]` for Arize, or `[BraintrustTracingProcessor()]` for Braintrust. This replaces all existing processors including the OpenAI backend. **Free Traces for Non-OpenAI Models with set_tracing_export_api_key()** Use trace visualization for free even when running non-OpenAI models (Gemini, Claude, etc.). Import `set_tracing_export_api_key` from `agents` and call `set_tracing_export_api_key("your-tracing-export-key")`. This enables free trace visualization even when using non-OpenAI models like Gemini or Claude. **Combining Multiple Processors** In production, it's common to send traces to multiple observability tools. Pass multiple processors as a list to `set_trace_processors`: `LangfuseTracingProcessor()` for detailed LLM analytics, `DatadogProcessor(dd_client)` for infrastructure monitoring integration, and `CustomAuditProcessor()` for internal audit logging -- all running simultaneously. 💡 Use Cases 📡 Use add_trace_processor() to send traces to Datadog in parallel while keeping the OpenAI dashboard 🔀 Switch destinations entirely to Langfuse/MLflow/Arize/Braintrust with set_trace_processors() 🆓 Visualize non-OpenAI model (Gemini, Claude) traces for free with set_tracing_export_api_key() 🔧 Build production observability pipelines combining multiple processors (Langfuse + Datadog + audit logs) ⚠️ Caveats - `set_trace_processors()` **replaces all** existing processors, including the OpenAI backend. Use `add_trace_processor()` if you want to keep it. - Always implement the `shutdown()` method in custom processors to flush any buffered data. - Multiple processors add overhead to trace delivery. Consider asynchronous sending to minimize impact. ✨ Custom processors let you pipe trace data directly into your existing observability stack. Start with add for parallel export, then optimize destinations with set once you're confident! #OpenAIAgentSDK# #AIAgent#

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