AIAny
AI Infra2026
Icon for item

Future AGI

Provides an end-to-end platform to evaluate, observe, protect, and optimize LLM and AI agent deployments. Integrates OpenTelemetry tracing, 50+ evaluation metrics, agent simulations, an OpenAI‑compatible gateway, and guardrails; self‑hostable under Apache 2.0.

Introduction

Most teams stitch together separate tools for tracing, evaluation, guardrails, and routing — then struggle to close the loop between production failures and fixes. This platform treats those pieces as a single lifecycle: simulate → evaluate → protect → monitor → optimize, turning production traces into actionable signals that feed back into prompt and policy improvements.

What Sets It Apart
  • Unified feedback loop: traces, automated evaluations (50+ metrics), simulations, and guardrails feed into the same data plane so production failures can be reproduced, triaged, and used to generate improvements without manual stitching. This reduces time-to-fix for hallucinations and tool-use errors.
  • Built-in gateway + high-throughput routing: an OpenAI‑compatible command center that supports 100+ providers, semantic caching, virtual keys, and routing strategies — benchmarked at ~29k req/s with P99 ≤ 21 ms (with guardrails). This lets teams consolidate provider routing and inline protections in one component.
  • Production-grade observability and storage: OpenTelemetry-native tracing with span graphs and dashboards, and a stack tuned for traces/metrics (Postgres for metadata, ClickHouse for spans, Redis for state, RabbitMQ + Temporal for jobs). That design prioritizes large-scale tracing and offline evals.
  • Self-hostable, open code and metrics: Apache 2.0 core, inspectable evaluators and prompts, drop-in adapters for many vendors and vector DBs, plus SDKs for Python/TS/Go so you can run entirely on-prem for data sovereignty.
Who It's For and Trade-offs

Great fit if you run multi-step or voice agents in production and need integrated observability, automated evals, and real-time guardrails — especially teams that must self-host for compliance. It consolidates many infra pieces (gateway, tracing, evals, simulators) so you can stop building custom integrations.

Look elsewhere if you only need a lightweight client SDK or a single-purpose eval tool: the full platform is broad and assumes you can run Docker Compose / published images (or host the managed cloud). Some components (Kubernetes manifests, Helm charts, marketplace listings) were noted as coming soon, and the stack introduces operational overhead (ClickHouse, Postgres, Redis, RabbitMQ, Temporal) compared with single-binary alternatives. Telemetry pings exist by default but can be disabled for stricter air‑gap requirements.

More Items

Hugging Face
AI Model2026

Open-weights LLM fine-tuned for phone-based voice agents that prioritizes low latency and reliable tool/function calling. Based on NVIDIA Nemotron 3 Nano (30B total, 3.5B active), supports very long contexts (262,144 tokens) and recommends temperature=0 with thinking disabled for deployment.

GitHub
AI Infra2025

Measures generative AI inference performance with token-level metrics (TTFT, inter-token latency), latency, and throughput under realistic traffic patterns. Provides a multiprocess engine, real-time TUI dashboard, extensible plugins, and integrations for telemetry and result uploads, aimed at inference benchmarking and capacity planning.

GitHub
AI Agent2026

A curated collection of production-ready Agent Skills that turn tasks—presentation production, image generation, local KB retrieval, article assembly, and web-design—into agent-loadable skill folders. Uses a SKILL.md contract, supports multiple agent runtimes (Claude Code, Cursor, Codex), and offers modular install paths with pinned release zips.