Guides & How-tos

Multimodal AI models need video data aligned with text, audio, and sensor inputs, but managing those datasets is far harder than managing images or text alone. This guide covers storage formats, metadata alignment strategies, versioning approaches, and tooling for teams building multimodal training pipelines at scale.

LangGraph lets you build stateful, cyclic AI agents, but the framework alone isn't enough. You need tools for visualizing graph topologies, tracing execution, persisting state, searching the web, and deploying to production. This guide breaks down the five tools that most LangGraph developers rely on and when to reach for each one.

Dust is an AI workspace that lets teams build custom assistants connected to company knowledge and tools. This guide breaks down what Dust actually does well, where it falls short, and how alternatives like Fast.io approach the same problem differently, especially for teams running autonomous agents that need persistent file storage and MCP tooling.

A document processing pipeline built on the Fast.io API listens for new file uploads, automatically routes them for AI extraction, and stores structured metadata back in the workspace. This guide walks through each stage, from workspace setup and webhook configuration to LLM-powered extraction and metadata tagging, with practical code examples you can adapt for invoices, contracts, and forms.

Guide to tools autogen agents: AutoGen tools enable multi-agent conversations to execute code, browse the web, and persist data. This guide ranks seven tools that solve the most common gaps in the AutoGen ecosystem, from sandboxed code execution to long-term file storage and agent observability. Each entry includes strengths, limitations, and pricing so you can pick the right stack for your project.

LLM observability platforms give you visibility into what happens between a user's prompt and your model's response. This guide compares seven leading platforms across pricing models (per trace vs per seat vs usage-based), tracing depth, evaluation frameworks, and production monitoring. Each entry includes real pricing, honest limitations, and guidance on which teams benefit most.

Revit files for construction projects regularly exceed 500 MB, making email and basic cloud drives impractical for sharing with general contractors, subs, and field crews. This guide covers the main approaches to Revit model collaboration, from Autodesk's native tools to general-purpose platforms, and walks through setting up secure multi-team workflows that keep every trade working from the current model revision.

4K video files range from 3GB for a short clip to over 100GB per hour of professional footage. Most transfer methods either compress the video, cap file sizes, or force recipients to download everything before watching. This guide covers five methods that preserve original quality, from free cloud storage to professional transfer services built for production workflows.

Programmatic file sharing replaces manual uploads with API calls that move files between systems automatically. This guide walks through authentication, chunked uploads, webhook-driven pipelines, multi-agent coordination, and security patterns that keep automated transfers reliable at scale.

Frame.io is excellent for human review, but AI-augmented video production needs a storage layer where agents can trigger events, extract metadata, and move files between stages programmatically. This guide compares Frame.io's limitations for automated workflows against developer-first alternatives built for agent access.

Devin AI generates dozens to hundreds of files per session, but its workspace resets between runs. This guide covers practical strategies for persisting Devin's output, backing up generated code, sharing results with your team, and building a file management workflow around autonomous coding agents.

Claude multi-agent coworking is an architectural pattern where several specialized Claude agents share a single workspace to collaboratively solve complex tasks. This guide covers the three dominant coordination patterns, concurrency controls for shared files, context-sharing strategies that keep token costs manageable, and how to wire everything together with persistent storage and event-driven triggers.

Building a custom Fast.io MCP tool lets developers inject proprietary business logic directly into an agent's file workspace. This guide covers the three mandatory components of a tool definition, authentication patterns for autonomous agents, implementation in Python and TypeScript, and how to deploy your tool alongside Fast.io's 19 built-in MCP tools.

Most AI agent tutorials skip the hardest part: giving your agent reliable, searchable file storage that works across sessions. This guide walks through building a complete AI file manager on the Fast.io API, from authentication and uploads to semantic search and ownership transfer.

No-code AI agent builders let you design agentic workflows with drag-and-drop interfaces instead of writing Python from scratch. This guide compares seven platforms across pricing, hosting options, and real-world strengths so you can pick the right one for your team.