Unlock the power of Agentic Systems at Scale

Blue goes beyond one-off agentic prototypes by integrating seamlessly with existing enterprise infrastructure to create next-gen agentic enterprise applications.

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More than

Agentic AI

While prompt engineering and agentic workflows might get you started, real-world use cases demand more. Blue’s design as a compound AI system facilitates structured orchestration with reliability, reusability, and provenance—all essential for enterprise use.

AI system that thinks, plans, and integrates
Blue treats agents as components within a larger architecture—coordinated by planners, connected to real services, and governed by structured data and logic. It blends LLMs with APIs, deterministic code, task decomposition for analytical thinking.
From Prompt Chains to Real Orchestration
Blue’s advanced orchestration facilitates orchestration of data and work in a structured manner, resulting in reliability, reusability, and provenance—essential for enterprise use.
Built to operationalize compound AI
Blue is an open-source framework for building and deploying compound AI systems. It gives you streams, planners, registries, and orchestration layers to coordinate intelligent agents with real data and services—at scale, and with trust.
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Features

Streams DB

Streams distribute data and tasks among agents, making complex workflows scalable and easy to debug.

Data Planner(s)

Break complex queries into several operators across structured and unstructured data to improve speed, cost, and accuracy.

Task Planner(s)

Decompose and prioritize tasks, coordinate agents, and explain results so workflows execute like expert teams.

Data Registry

Seamlessly connect agents to private and public multimodal data sources.

Agent Registry

Find and reuse vetted agents and tools across your projects to build reliable, reproducible workflows at any scale.

Tool Registry

Register and integrate custom or public tools—including MCP servers—so agents can access resources across workflows.

Streams DB

Streams distribute data and tasks among agents, making complex workflows scalable and easy to debug.

Data Planner(s)

Break complex queries into several operators across structured and unstructured data to improve speed, cost, and accuracy.

Task Planner(s)

Decompose and prioritize tasks, coordinate agents, and explain results so workflows execute like expert teams.

Data Registry

Seamlessly connect agents to private and public multimodal data sources.

Agent Registry

Find and reuse vetted agents and tools across your projects to build reliable, reproducible workflows at any scale.

Tool Registry

Register and integrate custom or public tools—including MCP servers—so agents can access resources across workflows.

Read the Research Papers
Orchestration

Where user experience meets system orchestration

Search and customize agents, data, and tools with registries

A holistic view of your applications with multi window view

Stay on top of system performance

Debug workflows with full transparency

read

The Blog

15 Jul '25

9 Min Read

Blue Streams: Orchestrating Work

Stream processing is a key ingredient when making “agentic workflows” enterprise-ready. Streams support a wide range of workflows and support complexity while at the same time bringing about the right abstractions and scope for facilitating accuracy, scalability, and ease of use.

8 May '25

7 Min Read

Blue: Designing “Agentic for Enterprise”

How can enterprise systems evolve to support agentic workflows? In this post, we explore the conceptual foundations of Blue—a framework designed to integrate AI agents, data, and services into scalable, observable, and controllable enterprise applications.

1 Apr '25

5 Min Read

Blue v0.9: “Agentic” for the Enterprise!

We present Blue v0.9, our open-source framework for building and deploying agentic workflows in enterprise environments. Unlike conventional AI frameworks, Blue is designed with enterprise-scale requirements in mind—scalability, observability, configurability, and seamless integration with existing infrastructure.

16 Dec '24

6 Min Read

Optimizing Compound AI Systems

We echo through this blog that the optimization framework for compound AI systems should achieve broader goals such as multi-objective (accuracy, cost, latency, etc.), multi-plan optimization and also handling constraints, especially the budget. Again, these optimization goals are not comprehensive by far but are important for enterprise scenarios.

A Compound AI System