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What is Plano?

Plano is a comprehensive AI delivery infrastructure platform designed to simplify the development, deployment, and operation of agentic applications. Built as an AI-native data plane and proxy layer, the platform handles the complex infrastructure components required to support modern AI agents at scale. Developers can leverage Plano to manage agent orchestration, intelligent model routing, observability, security controls, context engineering, and policy enforcement through a centralized architecture. The platform enables seamless integration with more than one language model provider through a unified API, reducing the complexity of multi-model deployments. Detailed tracing and monitoring capabilities provide deep insights into agent interactions, helping teams troubleshoot issues, improve reliability, and support reinforcement learning workflows. Plano’s architecture allows developers to continue using their preferred programming languages, frameworks, and development tools without introducing restrictive dependencies. Built-in guardrails help detect and mitigate risks such as jailbreak attempts while enforcing organizational policies across applications. The platform also supports reusable context engineering filters that improve agent responses and operational consistency. Organizations operating in regulated industries can deploy Plano on-premises to maintain greater control over sensitive data and infrastructure. Its configuration-driven approach simplifies deployment by allowing teams to define agent behavior, model preferences, and integrations through a streamlined setup process. By handling the underlying plumbing of AI systems, Plano empowers organizations to focus on innovation, accelerate development cycles, and deliver production-ready AI agents with greater confidence.

What is Model Context Protocol (MCP)?

The Model Context Protocol (MCP) serves as a versatile and open-source framework designed to enhance the interaction between artificial intelligence models and various external data sources. By facilitating the creation of intricate workflows, it allows developers to connect large language models (LLMs) with databases, files, and web services, thereby providing a standardized methodology for AI application development. With its client-server architecture, MCP guarantees smooth integration, and its continually expanding array of integrations simplifies the process of linking to different LLM providers. This protocol is particularly advantageous for developers aiming to construct scalable AI agents while prioritizing robust data security measures. Additionally, MCP's flexibility caters to a wide range of use cases across different industries, making it a valuable tool in the evolving landscape of AI technologies.

Media

Media

Integrations Supported

OpenAI

Integrations Supported

OpenAI
AgentaOS
Artyfile
CData Connect AI
Claude Haiku 5.5
Claude Opus 5.5
CrowdStrike Falcon AIDR
FluentDB
GridCraft
Higgsfield Explainer
Hindsight
Metaflow AI
Mirelo AI
Muse Spark 1.3
Portia
ReplyAtlas
Routebase
Rube
SOCRadar Extended Threat Intelligence
Wistia

API Availability

Has API

API Availability

Pricing Information

Free
Free Version

Pricing Information

Free
Open source
Free Version

Supported Platforms

SaaS
On-Prem

Supported Platforms

Windows
Mac
On-Prem
Linux

Customer Service / Support

Web-Based Support

Customer Service / Support

Not specified

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

Katanemo Labs

Company Location

United States

Company Website

planoai.dev/

Company Facts

Organization Name

Anthropic

Date Founded

2021

Company Location

United States

Company Website

modelcontextprotocol.io

Categories and Features

Agentic AI

Not specified

Agentic Orchestration

Not specified

AI Gateways

Not specified

Categories and Features

Agentic AI

Not specified

Agentic Orchestration

Not specified

AI Development

Not specified

AI Orchestration

Not specified

Context Engineering

Not specified

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