_Plano is a models-native proxy and data plane for agents._<br><br>
Plano pulls out the rote plumbing work and decouples you from brittle framework abstractions, centralizing what shouldn’t be bespoke in every codebase - like agent routing and orchestration, rich agentic signals and traces for continuous improvement, guardrail filters for moderation, and smart LLM routing APIs for UX and DX agility. Use any language or AI framework, and deliver agents faster to production.
[](https://github.com/katanemo/plano/actions/workflows/static.yml)
Building agentic demos is easy. Shipping agentic applications safely, reliably, and repeatably to production is hard. After the thrill of a quick hack, you end up building the “hidden middleware” to reach production: routing logic to reach the right agent, guardrail hooks for safety and moderation, evaluation and observability glue for continuous learning, and model/provider quirks scattered across frameworks and application code.
Plano pulls rote plumbing out of your framework so you can stay focused on what matters most: the core product logic of your agentic applications. Plano is backed by [industry-leading LLM research](https://planoai.dev/research) and built on [Envoy](https://envoyproxy.io) by its core contributors, who built critical infrastructure at scale for modern worklaods.
**Jump to our [docs](https://docs.planoai.dev)** to learn how you can use Plano to improve the speed, safety and obervability of your agentic applications.
> Plano and the Arch family of LLMs (like Plano-Orchestrator-4B, Arch-Router, etc) are hosted free of charge in the US-central region to give you a great first-run developer experience of Plano. To scale and run in production, you can either run these LLMs locally or contact us on [Discord](https://discord.gg/pGZf2gcwEc) for API keys.
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.
Follow this quickstart guide to use Plano as a router for local or hosted LLMs, including dynamic routing. Later in the section we will see how you can Plano to build highly capable agentic applications, and to provide e2e observability.
> We recommend that developers create a new Python virtual environment to isolate dependencies before installing Plano. This ensures that plano and its dependencies do not interfere with other packages on your system.
Plano supports multiple powerful routing strategies for LLMs. [Model-based routing](https://docs.arch.com/guides/llm_router.html#model-based-routing) gives you direct control over specific models and supports 11+ LLM providers including OpenAI, Anthropic, DeepSeek, Mistral, Groq, and more. [Alias-based routing](https://docs.arch.com/guides/llm_router.html#alias-based-routing) lets you create semantic model names that decouple your application code from specific providers, making it easy to experiment with different models or handle provider changes without refactoring. For full configuration examples and code walkthroughs, see our [routing guides](https://docs.arch.com/guides/llm_router.html).
Preference-aligned routing provides intelligent, dynamic model selection based on natural language descriptions of tasks and preferences. Instead of hardcoded routing logic, you describe what each model is good at using plain English.
Plano uses a lightweight 1.5B autoregressive model to intelligently map user prompts to these preferences, automatically selecting the best model for each request. This approach adapts to intent drift, supports multi-turn conversations, and avoids brittle embedding-based classifiers or manual if/else chains. No retraining required when adding models or updating policies — routing is governed entirely by human-readable rules.
**Learn More**: Check our [documentation](https://docs.plano.com/concepts/llm_providers/llm_providers.html) for comprehensive provider setup guides and routing strategies. 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 Plano 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.
"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."
"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."
Plano is designed to support best-in class observability by supporting open standards. Please read our [docs](https://docs.plano.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)
