Future AGI is an open-source, end-to-end AI agent engineering platform that covers the full lifecycle: simulate, evaluate, protect, monitor, and optimize. Instead of stitching together separate vendors for evals, observability, and guardrails, you get one platform with a closed feedback loop. Every production trace feeds back into your next version. It supports 50+ frameworks, 100+ LLM providers, and works for customer support bots, voice agents, RAG pipelines, coding agents, and more. Self-hostable under Apache 2.0 or available as a managed cloud with SOC 2 Type II compliance.

• 50+ Evaluation Metrics: Run groundedness, hallucination, tool-use correctness, PII detection, tone analysis, and custom rubrics in a single evaluate() call using LLM-as-judge, heuristic, and ML approaches.
• Real-Time Guardrails: 18 built-in scanners for PII, jailbreak, prompt injection, and toxicity plus 15 vendor adapters (Lakera, Presidio, Llama Guard), all running at sub-100ms latency.
• Multi-Turn Simulation: Simulate thousands of conversations against realistic personas, adversarial inputs, and edge cases for both text and voice agents before they reach production.
• OpenTelemetry-Native Tracing: Zero-config tracing across 50+ frameworks including LangChain, LlamaIndex, CrewAI, and DSPy with span graphs, latency tracking, token cost, and live dashboards.
• Prism Gateway: OpenAI-compatible gateway supporting 100+ providers with 15 routing strategies, semantic caching, virtual keys, and fallbacks at ~29k req/s with P99 under 21ms.
• Prompt Optimization: Six built-in algorithms (GEPA, PromptWizard, ProTeGi, Bayesian, Meta-Prompt, Random) to automatically improve your prompts based on production data.
• Voice Agent Testing: Simulate diverse personas and accents, evaluate STT/TTS/LLM independently, fine-tune with reinforcement learning, and monitor production regressions.
• Purpose-Trained Evaluation Models: Avoids the verbosity and self-enhancement biases of generic LLM judges by using models built specifically for scoring AI outputs.

• All-in-One Platform: Combines evaluation, observability, guardrails, gateway, and optimization so you don't need to stitch together multiple vendors.
• Free Tier: The free plan includes logging, tracing, monitoring, datasets, annotations, unlimited experiments, and API access, making it easy to start without commitment.
• Open Source and Self-Hostable: Apache 2.0 licensed core with full self-hosting support for teams that need data sovereignty.
• Automated QA for AI: Users report the Critique Agents save significant time by automating output review, catching hallucinations and policy violations before they hit production.
• Broad Ecosystem Support: Works with 50+ frameworks and 100+ LLM providers out of the box, including OpenAI, Anthropic, Google Gemini, AWS Bedrock, and more.

• Steep Learning Curve for Non-Engineers: Users report the platform is intuitive for engineers but not very accessible for non-technical team members.
• Initial Setup Requires Effort: Getting started with the full platform takes some trial and error, especially for self-hosted deployments.
• Documentation Gaps: Users note the docs could benefit from more real-world case studies and practical walkthroughs.
• Limited Export and BI Integration: The dashboard currently lacks some export options and integrations with business intelligence tools that enterprise teams expect.
• Classic ML Model Limitations: Some users report difficulty tracing classic (non-LLM) machine learning models on the platform.

• Customer Support Bot QA: You're deploying a support chatbot that answers policy questions. Future AGI lets you simulate thousands of edge-case conversations before launch, catch hallucinated policies in real time with guardrails, and continuously improve accuracy from production traces.
• Voice Agent Reliability: Your voice agent speaks before anyone can review its output, and one hallucination gets recorded forever. Use Future AGI to simulate diverse personas and accents, evaluate speech-to-text and text-to-speech independently, and monitor production regressions in a continuous loop.
• RAG Pipeline Optimization: You've built a retrieval-augmented generation system but aren't sure if answers are grounded in your source documents. Future AGI's groundedness and hallucination metrics let you evaluate retrieval quality and catch confabulated responses automatically.
• Multi-Agent System Monitoring: You're running autonomous agents that coordinate on complex tasks. Future AGI's OpenTelemetry-native tracing gives you full visibility into each agent's decisions, tool calls, and handoffs, so you can debug failures across the entire chain.
• Pre-Production Safety Testing: Before shipping any AI feature, run it through Future AGI's simulation engine with adversarial inputs, jailbreak attempts, and PII probes to identify vulnerabilities and set up guardrails that block risky outputs at sub-100ms latency.
• LLM Cost and Performance Management: Use the Prism Gateway to route requests across 100+ providers, apply semantic caching to reduce redundant calls, set budget limits per team, and track token costs across all your AI applications from a single dashboard.