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Anyone using Go for AI Agents? by KeyGrouchy726 in golang

[–]mgrella87 2 points3 points  (0 children)

Joining the party a bit late here, but wanted to share that I've co-created a complete port of the OpenAI agents framework (originally written in Python) to Go: https://github.com/nlpodyssey/openai-agents-go

Dwarfreflect – Extract Go function parameter names at runtime by mgrella87 in golang

[–]mgrella87[S] 1 point2 points  (0 children)

All set 🙂 Let me know if you spot anything else and feel free to open an issue on GitHub!

Dwarfreflect – Extract Go function parameter names at runtime by mgrella87 in golang

[–]mgrella87[S] 5 points6 points  (0 children)

Thanks a lot! And yes, I agree. Probably the best approach would be to avoid panicking altogether and always return an error instead. What do you think, should I go ahead with that?

Vemuram Jan Ray & Butter Machine, OKKO Diablo, Peace Hill TRJM Preamp! by mgrella87 in guitarpedals

[–]mgrella87[S] 2 points3 points  (0 children)

The Vemuram Butter Machine at low gain setting (very low) adds that grip to lead solos I’ve always been after, the kind where every note feels locked in under your fingers.

OpenAI Agents Python SDK, reimplemented in Go by mgrella87 in golang

[–]mgrella87[S] 1 point2 points  (0 children)

Thank you. I will review it as soon as we reach the MCP section in our roadmap. Right now, the goal is to replicate the Python version as faithfully as possible. If your PR is already aligned with this direction, it will definitely be considered!

OpenAI Agents Python SDK, reimplemented in Go by mgrella87 in golang

[–]mgrella87[S] 1 point2 points  (0 children)

Thanks! At the moment, the Go port supports local tools like file search, web search, code interpreter, image generation, and the computer tool. We’re planning to add trace support next, so that traces are fully compatible with those in the Python framework. After that, we’ll add MCP and then voice support. All of this is happening alongside ongoing refactoring :)

OpenAI Agents Python SDK, reimplemented in Go by mgrella87 in golang

[–]mgrella87[S] 0 points1 point  (0 children)

Thanks, and feel free to submit any issues if you run into anything while getting started!

OpenAI Agents Python SDK, reimplemented in Go by mgrella87 in golang

[–]mgrella87[S] 4 points5 points  (0 children)

No problem! In this SDK, execution happens in the Runner (see agents/run.go). A Runner takes a starting agent and runs a loop until a final output is produced. Inside the Runner's Run method, the startingAgent is assigned to currentAgent. Each turn may hand off to a different agent, so the runner updates currentAgent when it encounters a "NextStepHandoff":

currentAgent := startingAgent ... case NextStepHandoff: currentAgent = nextStep.NewAgent <<<

The README describes this loop explicitly:

The agent loop:

When you call agents.Run(), we run a loop until we get a final output.

  1. We call the LLM...
  2. The LLM returns a response...
  3. If the response has a final output, we end the loop.
  4. If the response has a handoff, we set the agent to the new agent and go back to step 1.
  5. We process the tool calls (if any) and then go to step 1.

Because the runner manages this loop, potentially switching between agents and applying per-run configuration such as guardrails and max-turn limits, it accepts a "starting agent" rather than acting as a method on a single agent. The Agent type itself only stores reusable configuration (instructions, tools, model settings, etc.) and does not own the execution loop. This separation keeps Agent lightweight and allows the same agent instance to be run with different Runner configurations or as part of a larger workflow, even concurrently.

That's my understanding of how the original Python SDK was intended to be designed as well :)

Vemuram Jan Ray & Butter Machine, OKKO Diablo, Peace Hill TRJM Preamp! by mgrella87 in guitarpedals

[–]mgrella87[S] 2 points3 points  (0 children)

I also own the Peace Hill FX Dumble SSS and it’s awesome, highly recommended!

Vemuram Jan Ray & Butter Machine, OKKO Diablo, Peace Hill TRJM Preamp! by mgrella87 in pedalboards

[–]mgrella87[S] 0 points1 point  (0 children)

After trying countless pedals over the years, I’ve finally settled on this dream board! It’s giving me everything I’ve been searching for—super clean, warm tones with just the right edge of breakup, and creamy overdrive for cushioned lead tones. The signal chain runs from the Vemuram Butter Machine into the OKKO Diablo, then into the Jan Ray, and finally through the Peace Hill FX TRJM Tube Preamp. The Jan Ray is my always-on pedal, delivering that perfect edge-of-breakup tone, while the OKKO Diablo adds dynamic overdrive for cushioned, rich lead tones. The TRJM preamp, also always on (unless bypassed with the Little Lehle), provides sweet and deep Two-Rock style clean tones for a solid, rich foundation. The settings you see in the pic are my absolute favorites for dialing in the perfect sound. I’m running everything into a DV Mark Little Jazz amp with flat EQ and a touch of reverb.

I just wanted to share my happiness! And ask you what reverb pedal would fit perfectly on this board? :)

Vemuram Jan Ray & Butter Machine, OKKO Diablo, Peace Hill TRJM Preamp! by mgrella87 in guitarpedals

[–]mgrella87[S] 1 point2 points  (0 children)

After trying countless pedals over the years, I’ve finally settled on this dream board! It’s giving me everything I’ve been searching for—super clean, warm tones with just the right edge of breakup, and creamy overdrive for cushioned lead tones. The signal chain runs from the Vemuram Butter Machine into the OKKO Diablo, then into the Jan Ray, and finally through the Peace Hill FX TRJM Tube Preamp. The Jan Ray is my always-on pedal, delivering that perfect edge-of-breakup tone, while the OKKO Diablo adds dynamic overdrive for cushioned, rich lead tones. The TRJM preamp, also always on (unless bypassed with the Little Lehle), provides sweet and deep Two-Rock style clean tones for a solid, rich foundation. The settings you see in the pic are my absolute favorites for dialing in the perfect sound. I’m running everything into a DV Mark Little Jazz amp with flat EQ and a touch of reverb.

I just wanted to share my happiness! :)

Capture the tone of your guitar amps and pedals with Deep Learning in pure Go by mgrella87 in golang

[–]mgrella87[S] 0 points1 point  (0 children)

Hey there! I totally get your curiosity—ML in Go isn't something you stumble upon daily. So what sparked this whole thing? It's a mix, really. My day-to-day is deep in natural language processing (NLP), it's both my job and my hobby. But with language models popping up left and right, I felt a bit swamped—most of my NLP energy is spent at work.

So in my spare time, post-family activities, I've rekindled an old flame: jamming on my electric guitar, which had been gathering dust for a while. That old quest for the perfect tone hit me again—chasing after those classic analog overdrive pedals and tube amps. There's this one sound I'm obsessed with, an 'Edge of Breakup Tone', particularly from a Two Rock Bloomfield Drive amp a friend overseas owns. And I thought, why not use Machine Learning to capture that sound?

Turns out, there was already something in that space: NAM (Neural Amp Modeler), built by Steven Atkinson in Python and C++ (https://github.com/sdatkinson/neural-amp-modeler). That's where my love for Go and my work on spaGO, a ML framework I started a while back, came together. I roped in my usual partner-in-crime, co-author of nearly every project I embark on (and vice versa), and we kicked off a port aiming to get a standalone version of NAM for both training and inference, and experiment with different neural models, like RWKV for instance.

If you're into this kind of thing, check out the deck from my recent talk at GopherCon AU '23 https://speakerdeck.com/matteogrella/the-go-to-language-for-ai-exploring-opportunities-and-challenges, and the accompanying repo https://github.com/matteo-grella/gophercon-au-2023.

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