Show HN: archgw: open-source, intelligent proxy for AI agents, built on Envoy (opens in new tab)

Regarding support for wasm runtime in envoy. We believe wasm support in envoy is not going anywhere and it will continue to become more and more stable over time. Envoy has heathy community and in case of any security vulnerability we will hope that envoy will ship fix quite fast which it has done in the past. See here for details of security patch rollout [2]

[1] https://github.com/proxy-wasm/spec/blob/main/docs/WebAssembl...

[2] https://github.com/envoyproxy/envoy/blob/main/SECURITY.md

> Can I just use arch for routing between LLMs

Yes, you can use arch_config.yaml file to select between LLMs. In fact we have a demo on llm_routing [1] that you can try. Here how you can specify different LLMs in our config,

  llm_providers:
    - name: gpt-4o-mini
      access_key: $OPENAI_API_KEY
      provider: openai
      model: gpt-4o-mini
      default: true

    - name: gpt-3.5-turbo-0125
      access_key: $OPENAI_API_KEY
      provider: openai
      model: gpt-3.5-turbo-0125

    - name: gpt-4o
      access_key: $OPENAI_API_KEY
      provider: openai
      model: gpt-4o

    - name: ministral-3b
      access_key: $MISTRAL_API_KEY
      provider: mistral
      model: ministral-3b-latest

We currently support mistral and openai. And for both of them we support streaming interface. We do expose openai complaint v1/chat interface so any chat UI that works with openai should work with us as well. We do ship demos with gradio sample application.

> And what about key management? Do I manage access keys myself?

None of your clients need to manage access keys. Upon receipt of request our filter will appropriate LLM from arch_config and pick relevant access_key and modify request with access_key from arch_config before sending request to upstream LLM [2].

[1] https://github.com/katanemo/archgw/tree/main/demos/llm_routi...

[2] https://github.com/katanemo/archgw/blob/main/crates/llm_gate...

> do you have to use envoyproxy to use archgw

Yes, 100%. Our gateway is implemented as rust filter which runs inside envoy process.

> Can archgw be used for LLM routing without using envoyproxy?

Unfortunately no. Since we are built in top of envoyproxy.

The same model is currently being updated to handle complex multi-turn intent and parameter extraction scenarios, so that the dreaded follow-up, clarifying RAG use case can be effortlessly handled by developers without having to resort to complex LLM pre-processing. In essence, if the user's follow-up question is "remove X", their RAG endpoints get structured information about the prompt and refined parameters against which developers simply have to retrieve the right chunks for summarization.

> Can I use just the model itself?

yes - our models are on huggingface. You can use them directly.

> Do you have models hosted somewhere or they run locally? If they run locally what are the system requirements?

arch gateway does bunch of processing locally for example for intent detection and hallucination we use nli model. For function calling we use hosted version of our 1.5B function calling model [1]. We use vllm to host our model, But vllm is not supported on mac. There are other issues too running model locally on mac. For example docker doesn't support giving gpu access on mac to containers. We tried using ollama in the past to host model but ollama doesn't support exposing logprobs. But we do have an issue on this [2] and we will improve it soon.

> Can I build RAG based applications on arch?

Yes you can. You would need to host vector db. In arch we don't host vector db, we wanted to keep our infra simple and clean. Do do have a default target that you can use to build RAG application. See this demo for example see insurance agent demo [3]. We do have an open issue on building a full RAG demo here [4], +1 to it to show your support.

> How does parameter gathering work, is the model capable of conversing with the user to gather parameters?

Our model is trained to engage in dialogue if a parameter is missing because our model has seen examples of missing parameters during training. During our evals and tests we found out that our model could still hallucinate e.g. for the question "how is the weather" model could hallucinate city as "LA" even though LA was not specified in query. We handle hallucination detection in arch using nli model to establish entailment of parameters from input query. BTW we are currently working on to improve that part by quite a lot. More on that in next release.

[1] https://huggingface.co/katanemo/Arch-Function-1.5B.gguf

[2] https://github.com/katanemo/archgw/issues/286

[3] https://github.com/katanemo/archgw/blob/main/demos/insurance...

[4] https://github.com/katanemo/archgw/issues/287