plano/README.md

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_The AI-native proxy server for agentic applications._<br><br>
Arch handles the **pesky**, low-level details like routing user prompts to the right agents or specialized Model Context Protocol (MCP) tools, providing unified access and observability to large language models (LLMs), and quickly clarifying vague user inputs. With Arch, you build faster by focusing on the high-level logic of agents.

[Quickstart](#Quickstart) •
[Demos](#Demos) •
[Build agentic apps with Arch](#Build-AI-Agent-with-Arch-Gateway) •
[Use Arch as an LLM router](#Use-Arch-Gateway-as-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>

Past the thrill of an AI demo, have you found yourself hitting these walls? You know, the all too familiar ones:

- You go from one BIG prompt to specialized prompts, 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 just to **clarify user intent and validate inputs** effectively?
- You're wasting cycles choosing and integrating code for **observability** instead of it happening transparently?

And you think to yourself, can't I move faster by focusing on higher-level objectives in a language/framework agnostic way? Well, you can! **Arch Gateway** 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`. Engineered with purpose-built [LLMs](https://huggingface.co/collections/katanemo/arch-function-66f209a693ea8df14317ad68) for fast (<100ms) agent routing and hand-off scenarios
  - `⚡ Tools Use`: For common agentic scenarios let Arch instantly clarify and convert prompts to tools/API calls
  - `⛨ Guardrails`: Centrally configure and prevent harmful outcomes and ensure safe user interactions
  - `🔗 Access to LLMs`: Centralize access and traffic to LLMs with smart retries for continuous availability
  - `🕵 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==0.3.2
```

### Build AI Agent 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 Gateway as LLM Router

#### Step 1. Create arch config file

Arch operates based on a configuration file where you can define LLM providers, prompt targets, guardrails, etc. Below is an example configuration that defines openai and mistral LLM providers.

Create `arch_config.yaml` file with following content:

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

#### Step 2. Start arch gateway

Once the config file is created ensure that you have env vars setup for `MISTRAL_API_KEY` and `OPENAI_API_KEY` (or these are defined in `.env` file).

Start arch gateway,

```
$ archgw up arch_config.yaml
2024-12-05 11:24:51,288 - cli.main - INFO - Starting archgw cli version: 0.1.5
2024-12-05 11:24:51,825 - cli.utils - INFO - Schema validation successful!
2024-12-05 11:24:51,825 - cli.main - INFO - Starting arch model server and arch gateway
...
2024-12-05 11:25:16,131 - cli.core - INFO - Container is healthy!
```

### Step 3: Interact with LLM

#### Step 3.1: Using OpenAI python client

Make outbound calls via Arch gateway

```python
from openai import OpenAI

# Use the OpenAI client as usual
client = OpenAI(
  # No need to set a specific openai.api_key since it's configured in Arch's gateway
  api_key = '--',
  # Set the OpenAI API base URL to the Arch gateway endpoint
  base_url = "http://127.0.0.1:12000/v1"
)

response = client.chat.completions.create(
    # we select model from arch_config file
    model="None",
    messages=[{"role": "user", "content": "What is the capital of France?"}],
)

print("OpenAI Response:", response.choices[0].message.content)

```

#### Step 3.2: Using curl command
```
$ curl --header 'Content-Type: application/json' \
  --data '{"messages": [{"role": "user","content": "What is the capital of France?"}], "model": "none"}' \
  http://localhost:12000/v1/chat/completions

{
  ...
  "model": "gpt-4o-2024-08-06",
  "choices": [
    {
      ...
      "messages": {
        "role": "assistant",
        "content": "The capital of France is Paris.",
      },
    }
  ],
...
}

```

You can override model selection using `x-arch-llm-provider-hint` header. For example if you want to use mistral using following curl command,

```
$ curl --header 'Content-Type: application/json' \
  --header 'x-arch-llm-provider-hint: ministral-3b' \
  --data '{"messages": [{"role": "user","content": "What is the capital of France?"}], "model": "none"}' \
  http://localhost:12000/v1/chat/completions
{
  ...
  "model": "ministral-3b-latest",
  "choices": [
    {
      "messages": {
        "role": "assistant",
        "content": "The capital of France is Paris. It is the most populous city in France and is known for its iconic landmarks such as the Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral. Paris is also a major global center for art, fashion, gastronomy, and culture.",
      },
      ...
    }
  ],
  ...
}

```

## [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