Plano

Plano fills a real gap in the agentic stack. Most teams either build their own proxy layer or rely on framework-specific tools that lock them into a single ecosystem. Plano's framework-agnostic, Envoy-based architecture lets you keep using any LLM, any language, and any framework. The built-in guardrails, jailbreak detection, and context engineering hooks are practical for production. The recent DigitalOcean acquisition adds credibility and suggests the platform will have long-term support. Where it works best: multi-agent applications, teams needing centralized security policies, and regulated environments that require on-premises deployment. The observability features—rich traces and signal sampling—are particularly useful for debugging complex agentic interactions. When to pass: if you're building a simple chatbot that calls a single model, Plano is overkill. Non-developer teams will struggle with the YAML configuration. Teams that prefer all-in-one agent frameworks like LangChain or AutoGen may find the decoupled approach less convenient. Compared to alternatives: LangChain provides more built-in agents and tools but is framework-specific. Semantic Kernel is Microsoft-centric. Plano is more like an infrastructure layer—it doesn't compete on agent logic but on delivery and operations. For teams already frustrated by framework lock-in, Plano's approach is refreshing. The main caveat: cloud pricing is not transparent (contact sales), and the open-source version requires self-hosting. Small teams may find the managed tier cost-prohibitive. Also, the ecosystem of integrations is still growing—only major providers like OpenAI, Anthropic, and Hugging Face are documented.