future-agi/future-agi

Open-source, end-to-end platform for evaluating, observing, and improving LLM and AI agent applications. Tracing · Evals · Simulations · Datasets · Gateway · Guardrails. Self-hostable. Apache 2.0.

What it solves

AI agents often fail in production due to hallucinations and unpredictable behavior. Most teams struggle with fragmented workflows, having to stitch together separate tools for observability, evaluation, and guardrails. Future AGI collapses these into a single feedback loop to make agents reliable and self-improving.

How it works

The platform operates through six core pillars:

  • Simulate: Runs thousands of multi-turn conversations against realistic personas and edge cases (including text and voice).
  • Evaluate: Uses LLM-as-judge, heuristics, and ML to provide over 50 metrics like groundedness and tool-use correctness.
  • Protect: Provides built-in scanners and vendor adapters to prevent jailbreaks, injections, and PII leaks.
  • Monitor: Uses OpenTelemetry-native tracing to track latency, cost, and span graphs across various frameworks.
  • Command Center: Acts as an OpenAI-compatible gateway with high-performance routing and semantic caching.
  • Optimize: Employs six prompt-optimization algorithms to turn production traces into training data for better performance.

Who it’s for

  • Customer Support Teams: Building trustworthy support AI.
  • Voice AI Developers: Testing and improving end-to-end voice agents.
  • Enterprise Developers: Building reliable internal copilots and autonomous agents.
  • RAG Developers: Ensuring grounded answers and verified citations.
  • Coding & Computer-Use Agent Developers: Building agents that can code or interact with interfaces with confidence.

Highlights

  • Open-source (Apache 2.0) and self-hostable via Docker.
  • High-performance Go-based gateway capable of ~29k req/s.
  • Supports 50+ agent frameworks (LangChain, LlamaIndex, CrewAI, etc.).
  • Integrates with 100+ LLM providers and major vector databases.