future-agi vs Tracely-ai
Self-hostable platform for the whole agent-quality loop: tracing, evals, simulations, datasets, guardrails and an LLM gateway — one feedback loop from prototype to production. Apache 2.0. — versus — Trace-native CI/CD for agents: OTLP traces are graded on arrival, failures cluster into issues, and one click freezes a failing run into a hermetic regression case that blocks the PR.
Both are self-hostable platforms for the whole agent-quality loop. Future AGI is broader — simulations, datasets, guardrails and an LLM gateway; Tracely is narrower and sharper: everything derives from the trace and the output is a PR verdict, not a score.
| future-agi | Tracely-ai | |
|---|---|---|
| Stars | 1.8k | 1.1k |
| Forks | 539 | 86 |
| Language | Python | Python |
| License | Apache-2.0 | MIT |
| Last activity | today | today |
| Topics | evals | evals, agents |
| Curated connections | 3 | 4 |
future-agi — the curator's take
The consolidation bet: evals + observability + guardrails + simulation in one loop instead of Langfuse-plus-four-friends, with every evaluator inspectable — no black-box scoring. When NOT: its own banner says nightly, expect rough edges — pin versions and file issues; all-in-one platforms trade each component being second-best for the loop being closed; teams already invested in a tracing stack get more from adding evals to it than migrating.
Tracely-ai — the curator's take
The claim that separates it from every other eval tool: no hand-authored dataset. The recorded production trace is the test — promoted with its tool and LLM responses as fixtures, so CI replays it for $0 with zero live model calls, under a fail-to-pass contract that refuses the promotion unless the case fails on the old code and passes on the fix. Failure clustering is the other real feature: 31 broken runs collapse into one issue instead of 31 rows. Use it when agents are already in production emitting OTLP and your problem is regressions. NOT a lightweight library — self-hosting means Postgres, ClickHouse, Redis and MinIO — and with no traffic there is nothing to grade, so pre-production teams should start with a dataset-first tool.