The short version
Three sentences.
Fuze is the only one of these built around the EU AI Act as a product surface. Every trace span, every approval decision, every retention setting maps to a specific Article — so when a regulator asks for the Annex IV file or an Article 73 incident report, the document compiles from the same stream the SDK already emits.
The observability tools (Langfuse, LangSmith, Helicone) record what happened. They don’t produce regulator-facing artifacts. The agent runtimes (Mastra, OpenAI Agents SDK, Vercel AI SDK) execute the agent. They don’t carry the audit trail.
If you don’t need the AI Act framing, several of these are more mature than Fuze on their core competence. We say so explicitly below.
Observability tools
Trace recorders, eval suites, prompt management.
These products record what your LLM app or agent did. Excellent at that job. Fuze does it differently: the evidence is tamper-evident by default and keyed to specific Articles, so the same stream that drives debugging also drives the compliance file.
Fuze vs Langfuse
langfuse.com ↗Open-source LLM observability — trace recording, evals, prompt management. Mature and well-loved.
Where Fuze wins
Langfuse stores traces. Fuze stores tamper-evident evidence, keyed to specific EU AI Act Articles. If a regulator asks you for the Annex IV file or an Article 73 incident report, Langfuse hands you raw spans; Fuze Control compiles the document. The legal framing is the product, not an add-on.
Where Langfuse wins
Eval suites, prompt-management UI, and the broader ecosystem (integrations, datasets, playground) are deeper in Langfuse today. If your top priority is offline evaluation and prompt iteration rather than compliance evidence, Langfuse is more product-mature.
Fuze vs LangSmith
smith.langchain.com ↗LangChain's hosted observability + eval platform. Tightly bound to the LangChain / LangGraph stack.
Where Fuze wins
LangSmith is closed-source, US-hosted, and not framed around regulator-facing artifacts. Fuze is open-source on the runtime side, EU-hosted on the managed side, and ships the Annex IV / FRIA / Art. 73 compilers. If you need data to stay in the EU, or you need documents a regulator will accept, LangSmith doesn't try to solve that problem.
Where LangSmith wins
If you're already deep on LangChain or LangGraph, LangSmith's integration depth is hard to beat — evals, hub, monitoring, prompt iteration, all native. Excellent product for the LangChain-native team.
Fuze vs Helicone
helicone.ai ↗Open-source proxy for LLM calls — logs, costs, errors. Lightweight, drop-in.
Where Fuze wins
Helicone is a proxy: it sees the LLM call but not the agent loop, the tool calls, the side-effect, or the human-oversight decision. Fuze instruments at the agent layer, so the evidence covers everything the runtime did, not just the model call. The Article framing exists; Helicone is a cost-and-latency lens, not a compliance one.
Where Helicone wins
Helicone is the lightest possible integration. If you have a thin LLM-call wrapper and you just want a quick read on cost and latency without changing your architecture, Helicone is fewer lines of code to install. Fuze asks you to instrument the agent runtime, not just the model call.
Agent runtimes
Frameworks that execute the agent loop.
These products are runtimes — they own the agent loop, the tool dispatch, the orchestration. Fuze Agent is also a runtime, but ships compliance baked in. The runtimes in this section don’t.
Fuze vs Mastra
mastra.ai ↗Open-source TypeScript agent framework — workflows, agents, tools, RAG. EU-based, well-engineered.
Where Fuze wins
Mastra is a clean agent framework but doesn't sell itself as a compliance product. Fuze Agent ships the same primitives — agents, tools, workflows — plus the compliance layer wired in from the first run: hash-chained evidence, durable approvals, Annex IV / FRIA compilers. If you're building in the EU and you'll need the regulator-facing documents anyway, Fuze Agent gets you both at once.
Where Mastra wins
Mastra is further along as a general-purpose agent framework. Richer workflow primitives, broader integration set, larger community. If you don't need the compliance layer, Mastra is the more battle-tested choice today.
Fuze vs OpenAI Agents SDK
platform.openai.com/docs/guides/agents ↗OpenAI's first-party agent runtime — handoffs, tools, sessions. Python and TypeScript.
Where Fuze wins
Tied to OpenAI providers; no compliance layer; closed source. Fuze Agent is provider-neutral (OpenAI + Anthropic + Mistral + Scaleway + OVH), open source, and ships with the AI Act evidence stream baked in. If you need EU-hosted models or you need an audit trail a regulator will accept, the OpenAI SDK isn't designed for that job.
Where OpenAI Agents SDK wins
If you've committed to OpenAI as your provider and you don't need to leave that ecosystem, the official SDK is the lowest-friction path. Built-in tracing, sessions, and handoffs are tightly integrated with the OpenAI platform.
Fuze vs Vercel AI SDK
sdk.vercel.ai ↗TypeScript SDK for streaming UI + tool calls. Closest to a frontend-first LLM SDK.
Where Fuze wins
The Vercel AI SDK is excellent at the UI-and-streaming surface but doesn't position as an agent-runtime or compliance layer. Fuze Agent operates a layer below: it owns the agent loop, the tool dispatch, the budget, the oversight surface. The two compose — use Vercel AI SDK for the UI, Fuze Agent for the runtime evidence.
Where Vercel AI SDK wins
For shipping a polished chatbot UI on Next.js or React quickly, nothing matches it. Streaming, edge runtime, React Server Components — all native.
When not Fuze
The honest list.
We don’t want you using Fuze if it’s not the right fit. Here’s when it isn’t.
You’re not in scope of the EU AI Act.
If your system is minimal-risk and you don’t sell into the EU market, the regulator-facing artifacts are dead weight. Langfuse or Helicone are likely a better fit for pure observability.
You need deep eval suites today.
Evals + datasets + prompt playground is on the Fuze roadmap but not shipping. LangSmith and Langfuse are years ahead here.
You’ve standardised on a single closed ecosystem.
If you’re fully OpenAI or fully LangChain and that ecosystem covers your needs, the first-party SDK is lower friction. Fuze’s wedge is provider-neutrality + EU residency; if you don’t value those, you’re paying for something you won’t use.
You want a frontend SDK.
Vercel AI SDK is excellent at streaming UI; Fuze isn’t in that game. The two compose — use both — but if streaming UI is your whole need, the Vercel SDK alone is enough.
Next
Try the wedge first.
The classifier walks you through the EU AI Act scope decision in five minutes. If you come out in scope, the rest of Fuze answers the second question — “what evidence do I need to produce” — without changing the agent you’ve already built.