
archgw
The smart edge and AI gateway for agents. Arch is a high-performance proxy server that handles the low-level work in building agents: like applying guardrails, routing prompts to the right agent, and unifying access to LLMs, etc. Natively designed to process prompts, it's framework-agnostic and helps you build agents faster.
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Arch is an intelligent Layer 7 gateway designed to protect, observe, and personalize AI agents with APIs. It handles tasks related to prompts, including detecting jailbreak attempts, calling backend APIs, routing between LLMs, and managing observability. Built on Envoy Proxy, it offers features like function calling, prompt guardrails, traffic management, and observability. Users can build fast, observable, and personalized AI agents using Arch to improve speed, security, and personalization of GenAI apps.
README:
Arch is a smart proxy server designed as a modular edge and AI gateway for agents.
Arch handles the pesky low-level work in building agentic apps — like applying guardrails, clarifying vague user input, routing prompts to the right agent, and unifying access to any LLM. It’s a language and framework friendly infrastructure layer designed to help you build and ship agentic apps faster.
Quickstart • Demos • Route LLMs • Build agentic apps with Arch • Documentation • Contact
AI demos are easy to hack. But once you move past a prototype, you’re stuck building and maintaining low-level plumbing code that slows down real innovation. For example:
- Routing & orchestration. Put routing in code and you’ve got two choices: maintain it yourself or live with a framework’s baked-in logic. Either way, keeping routing consistent means pushing code changes across all your agents, slowing iteration and turning every policy tweak into a refactor instead of a config flip.
- Model integration churn. Frameworks wire LLM integrations directly into code abstractions, making it hard to add or swap models without touching application code — meaning you’ll have to do codewide search/replace every time you want to experiment with a new model or version.
- Observability & governance. Logging, tracing, and guardrails are baked in as tightly coupled features, so bringing in best-of-breed solutions is painful and often requires digging through the guts of a framework.
- Prompt engineering overhead. Input validation, clarifying vague user input, and coercing outputs into the right schema all pile up, turning what should be design work into low-level plumbing work.
- Brittle upgrades. Every change (new model, new guardrail, new trace format) means patching and redeploying application servers. Contrast that with bouncing a central proxy—one upgrade, instantly consistent everywhere.
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 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:
-
🚦 Route to Agents
: Engineered with purpose-built LLMs for fast (<100ms) agent routing and hand-off -
🔗 Route to LLMs
: Unify access to LLMs with support for dynamic routing. Model aliases coming soon -
⛨ 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 proven HTTP management and scalability features to handle ingress and egress traffic related to prompts and LLMs.
Jump to our docs 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
To get in touch with us, please join our discord server. We will be monitoring that actively and offering support there.
- Sample App: Weather Forecast Agent - A sample agentic weather forecasting app that highlights core function calling capabilities of Arch.
- Sample App: Network Operator Agent - A simple network device switch operator agent that can retrive device statistics and reboot them.
- User Case: Connecting to SaaS APIs - Connect 3rd party SaaS APIs to your agentic chat experience.
Follow this quickstart guide to use Arch as a router for local or hosted LLMs, including dynamic routing. Later in the section we will see how you can Arch to build highly capable agentic applications, and to provide e2e observability.
Before you begin, ensure you have the following:
- Docker System (v24)
- Docker compose (v2.29)
- Python (v3.13)
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.
$ python3.12 -m venv venv
$ source venv/bin/activate # On Windows, use: venv\Scripts\activate
$ pip install archgw==0.3.10
Arch supports two primary routing strategies for LLMs: model-based routing and preference-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 on how to get working.
version: v0.1.0
listeners:
egress_traffic:
address: 0.0.0.0
port: 12000
message_format: openai
timeout: 30s
llm_providers:
- access_key: $OPENAI_API_KEY
model: openai/gpt-4o
default: true
- access_key: $MISTRAL_API_KEY
model: mistral/mistral-3b-latest
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:
version: v0.1.0
listeners:
egress_traffic:
address: 0.0.0.0
port: 12000
message_format: openai
timeout: 30s
llm_providers:
- model: openai/gpt-4.1
access_key: $OPENAI_API_KEY
default: true
routing_preferences:
- name: code generation
description: generating new code snippets, functions, or boilerplate based on user prompts or requirements
- model: openai/gpt-4o-mini
access_key: $OPENAI_API_KEY
routing_preferences:
- name: code understanding
description: 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:
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.
Create arch_config.yaml
file with following content,
version: v0.1.0
listeners:
ingress_traffic:
address: 0.0.0.0
port: 10000
message_format: openai
timeout: 30s
llm_providers:
- access_key: $OPENAI_API_KEY
model: openai/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
$ archgw up arch_config.yaml
2024-12-05 16:56:27,979 - cli.main - INFO - Starting archgw cli version: 0.3.10
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
.
Here is a sample curl command you can use to interact,
$ 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,
$ 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."
Arch is designed to support best-in class observability by supporting open standards. Please read our docs on observability for more details on tracing, metrics, and logs. The screenshot below is from our integration with Signoz (among others)
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
We would love feedback on our Roadmap 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 for more details
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