plano/README.md

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  <img src="docs/source/_static/img/arch-logo.png" alt="Arch Logo" width="75%" heigh=auto>
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_The proxy server and the universal data plane for AI-native apps._<br><br>
Arch handles the *pesky low-level work* in building AI agents like clarifying vague user inputs, routing prompts to the right agents, calling tools for simple tasks, and unifying access to large language models (LLMs) - all without locking you into a framework. Move faster by focusing on the high-level logic of your agents.


[Quickstart](#Quickstart) •
[Demos](#Demos) •
[Build agentic apps with Arch](#Build-AI-Agent-with-Arch-Gateway) •
[Route LLMs](#Use-Arch-as-a-LLM-Router) •
[Documentation](https://docs.archgw.com) •
[Contact](#Contact)

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# Overview
<a href="https://www.producthunt.com/posts/arch-3?embed=true&utm_source=badge-top-post-badge&utm_medium=badge&utm_souce=badge-arch&#0045;3" target="_blank"><img src="https://api.producthunt.com/widgets/embed-image/v1/top-post-badge.svg?post_id=565761&theme=dark&period=daily&t=1742359429995" alt="Arch - Build&#0032;fast&#0044;&#0032;hyper&#0045;personalized&#0032;agents&#0032;with&#0032;intelligent&#0032;infra | Product Hunt" style="width: 188px; height: 41px;" width="188" height="41" /></a>

AI demos are easy to build. But past the thrill of a quick hack, you are left building, maintaining and scaling low-level plumbing code for agents that slows down AI innovation. For example:

- You want to build specialized agents, but get stuck building **routing and handoff** code.
- You want use new LLMs, but struggle to **quickly and safely add LLMs** without writing integration code.
- You're bogged down with prompt engineering work to **clarify user intent and validate inputs**.
- You're wasting cycles choosing and integrating code for **observability** instead of it happening transparently.

With Arch, you can move faster by focusing on higher-level objectives in a language and framework agnostic way. **Arch** was built by the contributors of [Envoy Proxy](https://www.envoyproxy.io/) with the belief that:

>Prompts are nuanced and opaque user requests, which require the same capabilities as traditional HTTP requests including secure handling, intelligent routing, robust observability, and integration with backend (API) systems to improve speed and accuracy for common agentic scenarios  – all outside core application logic.*

**Core Features**:

  - `🚦 Routing to Agents`. Engineered with purpose-built [LLMs](https://huggingface.co/collections/katanemo/arch-function-66f209a693ea8df14317ad68) for fast (<100ms) agent routing and hand-off scenarios
  - `🔗 Routing to LLMs`: Unify access and routing to any LLM, including dynamic routing via [preference policies](#Preference-based-Routing).
  - `⛨ Guardrails`: Centrally configure and prevent harmful outcomes and ensure safe user interactions
  - `⚡ Tools Use`: For common agentic scenarios let Arch instantly clarify and convert prompts to tools/API calls
  - `🕵 Observability`: W3C compatible request tracing and LLM metrics that instantly plugin with popular tools
  - `🧱 Built on Envoy`: Arch runs alongside app servers as a containerized process, and builds on top of [Envoy's](https://envoyproxy.io) proven HTTP management and scalability features to handle ingress and egress traffic related to prompts and LLMs.

**High-Level Sequence Diagram**:
![alt text](docs/source/_static/img/arch_network_diagram_high_level.png)

**Jump to our [docs](https://docs.archgw.com)** to learn how you can use Arch to improve the speed, security and personalization of your GenAI apps.

> [!IMPORTANT]
> Today, the function calling LLM (Arch-Function) designed for the agentic and RAG scenarios is hosted free of charge in the US-central region. To offer consistent latencies and throughput, and to manage our expenses, we will enable access to the hosted version via developers keys soon, and give you the option to run that LLM locally. For more details see this issue [#258](https://github.com/katanemo/archgw/issues/258)

## Contact
To get in touch with us, please join our [discord server](https://discord.gg/pGZf2gcwEc). We will be monitoring that actively and offering support there.

## Demos
* [Sample App: Weather Forecast Agent](demos/samples_python/weather_forecast/README.md) - A sample agentic weather forecasting app that highlights core function calling capabilities of Arch.
* [Sample App: Network Operator Agent](demos/samples_python/network_switch_operator_agent/README.md) - A simple network device switch operator agent that can retrive device statistics and reboot them.
* [User Case: Connecting to SaaS APIs](demos/use_cases/spotify_bearer_auth) - Connect 3rd party SaaS APIs to your agentic chat experience.

## Quickstart

Follow this quickstart guide to use arch gateway to build a simple AI agent. Laster in the section we will see how you can Arch Gateway to manage access keys, provide unified access to upstream LLMs and to provide e2e observability.

### Prerequisites

Before you begin, ensure you have the following:

1. [Docker System](https://docs.docker.com/get-started/get-docker/) (v24)
2. [Docker compose](https://docs.docker.com/compose/install/) (v2.29)
3. [Python](https://www.python.org/downloads/) (v3.12)

Arch's CLI allows you to manage and interact with the Arch gateway efficiently. To install the CLI, simply run the following command:

> [!TIP]
> We recommend that developers create a new Python virtual environment to isolate dependencies before installing Arch. This ensures that archgw and its dependencies do not interfere with other packages on your system.

```console
$ python -m venv venv
$ source venv/bin/activate   # On Windows, use: venv\Scripts\activate
$ pip install archgw==0.3.3
```

### Build Agentic Apps with Arch Gateway

In following quickstart we will show you how easy it is to build AI agent with Arch gateway. We will build a currency exchange agent using following simple steps. For this demo we will use `https://api.frankfurter.dev/` to fetch latest price for currencies and assume USD as base currency.

#### Step 1. Create arch config file

Create `arch_config.yaml` file with following content,

```yaml
version: v0.1.0

listeners:
  ingress_traffic:
    address: 0.0.0.0
    port: 10000
    message_format: openai
    timeout: 30s

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

system_prompt: |
  You are a helpful assistant.

prompt_guards:
  input_guards:
    jailbreak:
      on_exception:
        message: Looks like you're curious about my abilities, but I can only provide assistance for currency exchange.

prompt_targets:
  - name: currency_exchange
    description: Get currency exchange rate from USD to other currencies
    parameters:
      - name: currency_symbol
        description: the currency that needs conversion
        required: true
        type: str
        in_path: true
    endpoint:
      name: frankfurther_api
      path: /v1/latest?base=USD&symbols={currency_symbol}
    system_prompt: |
      You are a helpful assistant. Show me the currency symbol you want to convert from USD.

  - name: get_supported_currencies
    description: Get list of supported currencies for conversion
    endpoint:
      name: frankfurther_api
      path: /v1/currencies

endpoints:
  frankfurther_api:
    endpoint: api.frankfurter.dev:443
    protocol: https
```

#### Step 2. Start arch gateway with currency conversion config

```sh

$ archgw up arch_config.yaml
2024-12-05 16:56:27,979 - cli.main - INFO - Starting archgw cli version: 0.1.5
...
2024-12-05 16:56:28,485 - cli.utils - INFO - Schema validation successful!
2024-12-05 16:56:28,485 - cli.main - INFO - Starting arch model server and arch gateway
...
2024-12-05 16:56:51,647 - cli.core - INFO - Container is healthy!

```

Once the gateway is up you can start interacting with at port 10000 using openai chat completion API.

Some of the sample queries you can ask could be `what is currency rate for gbp?` or `show me list of currencies for conversion`.

#### Step 3. Interacting with gateway using curl command

Here is a sample curl command you can use to interact,

```bash
$ curl --header 'Content-Type: application/json' \
  --data '{"messages": [{"role": "user","content": "what is exchange rate for gbp"}], "model": "none"}' \
  http://localhost:10000/v1/chat/completions | jq ".choices[0].message.content"

"As of the date provided in your context, December 5, 2024, the exchange rate for GBP (British Pound) from USD (United States Dollar) is 0.78558. This means that 1 USD is equivalent to 0.78558 GBP."

```

And to get list of supported currencies,

```bash
$ curl --header 'Content-Type: application/json' \
  --data '{"messages": [{"role": "user","content": "show me list of currencies that are supported for conversion"}], "model": "none"}' \
  http://localhost:10000/v1/chat/completions | jq ".choices[0].message.content"

"Here is a list of the currencies that are supported for conversion from USD, along with their symbols:\n\n1. AUD - Australian Dollar\n2. BGN - Bulgarian Lev\n3. BRL - Brazilian Real\n4. CAD - Canadian Dollar\n5. CHF - Swiss Franc\n6. CNY - Chinese Renminbi Yuan\n7. CZK - Czech Koruna\n8. DKK - Danish Krone\n9. EUR - Euro\n10. GBP - British Pound\n11. HKD - Hong Kong Dollar\n12. HUF - Hungarian Forint\n13. IDR - Indonesian Rupiah\n14. ILS - Israeli New Sheqel\n15. INR - Indian Rupee\n16. ISK - Icelandic Króna\n17. JPY - Japanese Yen\n18. KRW - South Korean Won\n19. MXN - Mexican Peso\n20. MYR - Malaysian Ringgit\n21. NOK - Norwegian Krone\n22. NZD - New Zealand Dollar\n23. PHP - Philippine Peso\n24. PLN - Polish Złoty\n25. RON - Romanian Leu\n26. SEK - Swedish Krona\n27. SGD - Singapore Dollar\n28. THB - Thai Baht\n29. TRY - Turkish Lira\n30. USD - United States Dollar\n31. ZAR - South African Rand\n\nIf you want to convert USD to any of these currencies, you can select the one you are interested in."

```

### Use Arch as a LLM Router
Arch supports two primary routing strategies for LLMs: model-based routing and preference-based routing.

#### Model-based Routing
Model-based routing allows you to configure static model names for routing. This is useful when you always want to use a specific model for certain tasks, or manually swap between models. Below an example configuration for model-based routing, and you can follow our [usage guide](demos/use_cases/README.md) on how to get working.

```yaml
version: v0.1.0

listeners:
  egress_traffic:
    address: 0.0.0.0
    port: 12000
    message_format: openai
    timeout: 30s

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

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

#### Preference-based Routing
Preference-based routing is designed for more dynamic and intelligent selection of models. Instead of static model names, you write plain-language routing policies that describe the type of task or preference — for example:

```yaml
version: v0.1.0

listeners:
  egress_traffic:
    address: 0.0.0.0
    port: 12000
    message_format: openai
    timeout: 30s

llm_providers:
  - name: code_generation
    access_key: $OPENAI_API_KEY
    provider_interface: openai
    model: gpt-4.1
    usage: generating new code snippets, functions, or boilerplate based on user prompts or requirements

  - name: code_understanding
    provider_interface: openai
    access_key: $OPENAI_API_KEY
    model: gpt-4o-mini
    usage: understand and explain existing code snippets, functions, or libraries
```

Arch uses a lightweight 1.5B autoregressive model to map prompts (and conversation context) to these policies. This approach adapts to intent drift, supports multi-turn conversations, and avoids the brittleness of embedding-based classifiers or manual if/else chains. No retraining is required when adding new models or updating policies — routing is governed entirely by human-readable rules. You can learn more about the design, benchmarks, and methodology behind preference-based routing in our paper:

<div align="left">
  <a href="https://arxiv.org/abs/2506.16655" target="_blank">
    <img src="docs/source/_static/img/arch_router_paper_preview.png" alt="Arch Router Paper Preview">
  </a>
</div>

## [Observability](https://docs.archgw.com/guides/observability/observability.html)
Arch is designed to support best-in class observability by supporting open standards. Please read our [docs](https://docs.archgw.com/guides/observability/observability.html) on observability for more details on tracing, metrics, and logs. The screenshot below is from our integration with Signoz (among others)

![alt text](docs/source/_static/img/tracing.png)

## Debugging

When debugging issues / errors application logs and access logs provide key information to give you more context on whats going on with the system. Arch gateway runs in info log level and following is a typical output you could see in a typical interaction between developer and arch gateway,

```
$ archgw up --service archgw --foreground
...
[2025-03-26 18:32:01.350][26][info] prompt_gateway: on_http_request_body: sending request to model server
[2025-03-26 18:32:01.851][26][info] prompt_gateway: on_http_call_response: model server response received
[2025-03-26 18:32:01.852][26][info] prompt_gateway: on_http_call_response: dispatching api call to developer endpoint: weather_forecast_service, path: /weather, method: POST
[2025-03-26 18:32:01.882][26][info] prompt_gateway: on_http_call_response: developer api call response received: status code: 200
[2025-03-26 18:32:01.882][26][info] prompt_gateway: on_http_call_response: sending request to upstream llm
[2025-03-26 18:32:01.883][26][info] llm_gateway: on_http_request_body: provider: gpt-4o-mini, model requested: None, model selected: gpt-4o-mini
[2025-03-26 18:32:02.818][26][info] llm_gateway: on_http_response_body: time to first token: 1468ms
[2025-03-26 18:32:04.532][26][info] llm_gateway: on_http_response_body: request latency: 3183ms
...
```

Log level can be changed to debug to get more details. To enable debug logs edit (supervisord.conf)[arch/supervisord.conf], change the log level `--component-log-level wasm:info` to `--component-log-level wasm:debug`. And after that you need to rebuild docker image and restart the arch gateway using following set of commands,

```
# make sure you are at the root of the repo
$ archgw build
# go to your service that has arch_config.yaml file and issue following command,
$ archgw up --service archgw --foreground
```

## Contribution
We would love feedback on our [Roadmap](https://github.com/orgs/katanemo/projects/1) and we welcome contributions to **Arch**!
Whether you're fixing bugs, adding new features, improving documentation, or creating tutorials, your help is much appreciated.
Please visit our [Contribution Guide](CONTRIBUTING.md) for more details