fx vs Hyper: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of fx and Hyper — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
fx
Vercel Labs
Vercel Labs' tiny open-source coding agent — a ~6 MB native CLI written in Zig, built for speed, embeddability and Unix-style ergonomics.
Key features
- Tiny Native Binary: The whole agent ships as a roughly 6 MB executable designed for instant installation and for embedding in resource-constrained environments and agent sandboxes.
- Instant Time to Prompt: fx cold starts in about 10 microseconds and performs no unnecessary work or I/O before accepting user input, which matters for programmatic invocation.
- Minimal Memory Footprint: A single-digit-megabyte memory baseline lets you pack many concurrent instances onto one machine.
- Shell-Like Ergonomics: Scroll history is preserved by default and output is deliberately sparse, so the CLI composes like a Unix tool instead of taking over the terminal.
- Context Efficiency: A minimal system prompt and tool surface reduce token spend and improve time-to-first-token performance.
- WebAssembly Builds: Optimal fx.wasm builds from the Zig toolchain shrink the binary further and make the network stack pluggable, enabling the in-browser demo.
- Model and Provider Agnostic: Works with local models, LLM gateways, direct provider API access or existing subscriptions rather than locking you to one vendor.
- Extensible Small Core: Capabilities are added through skills, plugins and MCPs, following a Unix-like philosophy of a small core with composable extensions.
Best for
- Sandboxed Agent Execution: Ship a full coding agent inside a container or sandbox where a large runtime would not fit.
- Embedding in Larger Systems: Use fx as the agent harness inside your own product or internal platform rather than building a loop from scratch.
- CI and Scripted Automation: Invoke a coding agent from pipelines and scripts where fast cold starts and quiet output matter more than an interactive UI.
- Agent Harness Research: Experiment with system prompt and tool design on a deliberately minimal, readable Apache-2.0 codebase.
- Local-Model Coding: Run agentic coding against a locally hosted model without any dependency on a specific cloud provider.
- Browser-Based Demos and Playgrounds: Compile to WebAssembly and run the agent client-side with networking delegated to browser fetch.
Hyper
Hyper
A company knowledge layer that learns from Docs, Slack, Email and Calendar to power smarter, context-aware AI across teams.
Key features
- Unified Knowledge Ingestion: Continuously imports and indexes data from Docs, Slack, Email, and Calendar to build a central, searchable company knowledge graph.
- Contextual AI Plug-ins: Provides an interface and connectors so teams can inject company-specific context into external or internal AI models, improving accuracy and relevance of responses.
- Persistent Institutional Memory: Retains historical context across conversations and workflows so the system remembers past decisions, preferences, and policies without manual re-entry.
- Real-time Sync and Updates: Keeps ingested sources up to date with near real-time synchronization so answers reflect the latest documents, messages, and schedule changes.
- Access Controls & Security: Enables role-based access and privacy controls to ensure sensitive documents and communications are only used where permitted.
- Searchable Knowledge Retrieval: Offers semantic search and retrieval of relevant docs, messages, and calendar events to surface precise context for queries and automations.
- Workflow Automation: Leverages stored knowledge to trigger or assist with routine tasks (e.g., follow-ups, meeting summaries) and reduce manual work.
- Integration Framework: Supports connectors and APIs to integrate with common productivity tools and plug the company brain into existing AI assistants or platforms.
- Ingests and learns from Docs, Slack, Email and Calendar
- Creates a centralized, searchable company knowledge layer
- Integrates/"plugs into" existing AI systems to provide context and memory
- Context enrichment for downstream AI responses and workflows
- Connectors to common collaboration sources (Docs, Slack, Email, Calendar)
Best for
- Onboarding Acceleration: New hires query the company brain to get accurate, contextual answers about processes, past decisions, and team norms without repeatedly asking colleagues.
- Customer Support Enablement: Support agents retrieve up-to-date product docs, past tickets, and policy notes to craft faster, consistent responses to customers.
- Meeting Summaries & Action Items: Automatically summarize calendar events and linked documents, then surface follow-ups and owners based on historical context.
- Internal Knowledge Discovery: Employees search across Slack, emails, and docs to find precedents, design decisions, or technical notes relevant to current projects.
- Automated Follow-ups: Use contextual knowledge to draft or schedule follow-up emails and tasks after meetings, ensuring continuity and reducing manual tracking.
- Compliance & Audit Readiness: Aggregate and index communications and documents to simplify internal audits and demonstrate policy adherence with searchable records.
- Developer and Product Support: Engineers and PMs query past architecture decisions, bug histories, and release notes to speed troubleshooting and planning.
- Provide company-specific context to LLMs and AI assistants
- Centralized knowledge retrieval and enterprise search across Docs, Slack, Email and Calendar
- Faster onboarding by surfacing institutional knowledge
- Automated summarization and context-aware drafting for email and meetings
- Enriching customer-support or internal automation agents with up-to-date company info