Future AGI
Open-source evaluation, tracing and guardrails for LLM and agent applications, with the whole loop in one place.
Highlights
- 70+ built-in evaluation templates spanning quality, safety, factuality, RAG retrieval, format, bias, and audio/image checks
- Simulations and multi-turn, persona-based scenarios to test agents against realistic edge cases before launch
- OpenTelemetry-based tracing with end-to-end spans, timing, and an error feed that triages agent failures
- Real-time guardrails that block harmful output, PII exposure, and compliance violations in production
- AI optimization that feeds production and evaluation signals back into the next version of an agent
- An LLM gateway with budgets, webhooks, and MCP tool support, plus dashboards and anomaly alerting
- Synthetic data generation and dataset management that grow evaluation sets from tests and live traffic
- Apache-2.0 licensed and self-hostable, with SDKs, an API, and a hosted free tier
External link — opens futureagi.com in a new tab. Future AGI is a third-party product; we are not affiliated with it.
About Future AGI
What it is
Future AGI is a platform for finding out whether an LLM application actually works and why it stops working. It bundles the pieces that are usually four separate purchases: simulation, so you can run an agent against realistic multi-turn scenarios before launch; evaluation, with seventy-odd built-in templates covering factuality, safety, RAG retrieval quality, bias and format; OpenTelemetry-based tracing with an error feed that triages failures; and runtime guardrails that block harmful output, PII and compliance breaches. It is Apache-2.0 licensed and self-hostable, with a hosted tier and SDKs.
Why it's different
The real distinction is the licence. Most of this category is closed and priced per trace, which gets expensive precisely when you are debugging heavily and sending the most data. Future AGI being Apache-2.0 and self-hostable means the evaluation of your prompts and your users' inputs can stay on infrastructure you control, which matters if that data is regulated. What you give up is maturity: LangSmith and Langfuse have far larger user bases, more integrations and more people who have already hit the bug you are about to hit. Seventy evaluation templates is also a number to treat sceptically, since a generic template scoring your domain is usually worse than three you wrote yourself.
How people use it
Teams use it at two moments. Before shipping, to run an agent through simulated conversations and catch the failure modes that only appear on turn four. After shipping, to trace real sessions and answer the question every team eventually asks, which is why a thing that worked in testing does not work now. The guardrails are the production half, sitting in the request path to stop the output nobody wants to explain. The honest starting point is to wire up tracing first, look at real traffic for a fortnight, and write evaluations against the failures you actually see rather than the ones a template suggests.
Written by the n3os team. We are not affiliated with Future AGI.
This listing was written from public information, without Future AGI’s involvement. If you own it and something here is wrong — or you would rather not be listed at all — email us and we will correct or remove it.
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