Substrate AI Inference Platform

Substrate: Platform for Agentic AI (Documentation Summary)

Substrate is a platform designed to enable building and running complex, multi-step AI workloads by composing modular building blocks called nodes. It emphasizes high performance, automatic workload tuning, and a unified developer experience for creating compound AI systems.

What Substrate Is

• A platform and compute engine optimized for multi-step AI workloads.
• Provides elegant abstractions to describe and execute AI tasks as directed acyclic graphs (DAGs).
• Connects modular components (nodes) to form fast, end-to-end AI workflows.
• Includes high-performance pieces such as a vector database, code interpreter, and a model router.
• Aims to reduce round-trips and maximize parallelism for faster AI processing.

How It Works

1. Describe your task by connecting modular blocks called nodes to form a workflow.
2. Substrate analyzes the workload as a DAG and applies automatic workload tuning to optimize execution.

• Examples include merging nodes for batched execution to improve throughput.

1. Run the composed workflow; Substrate handles orchestration, optimization, and execution.

Core Concepts

• Nodes: Modular building blocks that perform specific AI tasks (e.g., text generation, transformation, or computation).
• Workflows: Compositions of nodes that define a complete AI task, expressed as a DAG.
• Vector DB: Integrated vector database component for similarity search and retrieval tasks.
• Code Interpreter: Mechanism to execute code within the workflow safely and efficiently.
• Model Router: Component responsible for routing requests to appropriate models or services.
• Python / TypeScript Support: Substrate exposes practical client libraries (e.g., Python, TypeScript) for building workflows.

Getting Started (Conceptual Example)

• Install the Substrate client:
• Python: pip install substrate
• Create a Substrate instance and connect to the API:
• Python example:
• from substrate import Substrate, ComputeText s = Substrate(api_key="SUBSTRATE_API_KEY") topic1 = "a magical forest" topic2 = "a futuristic city" story1 = ComputeText(prompt=f"Tell me a story about {topic1}") story2 = ComputeText(prompt=f"Tell me a story about {topic2}") summary = ComputeText(prompt=sb.format("Summarize these stories: Story 1: {story1} Story 2: {story2}", story1=story1.future.text, story2=story2.future.text)) response = s.run(summary)
• The exact syntax may vary by language, but the core idea is to build a DAG of nodes and execute it via the Substrate engine.

Features

• DAG-based workload modeling for multi-step AI tasks
• Automatic workload tuning and optimization
• Maximum parallelism and reduced round-trips
• Unified platform to build fast AI workflows by connecting modular nodes
• Built-in vector database, code interpreter, and model routing
• Client libraries for Python and TypeScript
• Fast, principled agent framework with a focus on developer experience
• Simple abstractions that enable complex, compound AI systems

How It Compares to Other Solutions

• Substrate emphasizes a principled, graph-based approach to orchestrating multiple AI components.
• It provides a high-performance compute engine specifically optimized for multi-step AI workloads, distinguishing it from single-model or single-task tools.

Usage Scenarios

• Building multi-model AI assistants that require retrieval, reasoning, generation, and execution steps.
• Creating complex data pipelines that combine NLP, reasoning, and computational tasks.
• Rapid prototyping of agent-driven systems with automatic workload optimization.

Notes

• The platform promotes fast iteration with a focus on performance and a clean developer experience.
• Access and usage typically require an API key and may include free credits for getting started.