Future AGI: An End-to-End Platform for Evaluating and Improving LLM Applications
Future AGI is an open-source platform designed to address the challenges of deploying AI agents in production environments. It consolidates evaluation, observability, guardrails, and optimization into a single feedback loop, enabling teams to simulate edge cases, evaluate performance, protect users in real-time, and continuously improve agents based on production data. The platform is available as a self-hosted solution or as a managed cloud service, adhering to Apache 2.0 licensing for its core components.
The platform’s development began in 2026, with a focus on providing a unified alternative to fragmented toolchains that often fail to integrate seamlessly. Future AGI aims to eliminate the need for stitching together disparate tools like Langfuse, Braintrust, Helicone, and Guardrails AI by offering a cohesive lifecycle management system for AI agents.
Future AGI is built on Python 3.11+, Go 1.23+, and React 18, with a backend powered by Django 5.1, Channels, and a Go-based gateway. The architecture relies on PostgreSQL for metadata, ClickHouse for spans and time-series data, Redis for state management, and RabbitMQ with Temporal for job orchestration. OpenTelemetry (OTel) is central to its observability model, ensuring compatibility with existing tracing infrastructures.
The platform’s components are modular, allowing users to integrate custom stacks at any layer. For instance, the OpenTelemetry-native tracing system supports 50+ AI frameworks, while the OpenAI-compatible gateway handles routing, caching, and guardrails with low latency—achieving up to 29,000 requests per second and a P99 latency of ≤21 milliseconds even with guardrails enabled.
Each pillar replaces a specialized tool, reducing complexity and improving feedback loops. The platform supports multi-turn conversations, adversarial inputs, and edge-case simulations, including voice interactions via LiveKit, VAPI, Retell, and Pipecat.
How It Works
Future AGI operates as a closed-loop system where data flows from simulation and production back into optimization. Users can deploy the platform via Docker Compose, Kubernetes (with upcoming Helm support), or cloud providers like AWS, GCP, and Azure. The self-hosted option ensures data sovereignty, while the cloud service offers a free tier with SOC 2 Type II and HIPAA compliance. Installation requires Docker Desktop or Docker Engine with Docker Compose, and the platform provides scripts for production-grade deployments, including secret generation and image version pinning.
Instrumentation is straightforward, with SDKs for Python and TypeScript enabling zero-config OpenTelemetry tracing for 50+ AI frameworks, including LangChain, LlamaIndex, CrewAI, and DSPy. The fi-instrumentation-otel and @traceai/fi-core libraries allow developers to trace existing OpenAI or custom LLM code with minimal changes. For example, adding two lines of Python or TypeScript code integrates tracing into an agent’s workflow, capturing spans for latency analysis, token usage, and error detection.
The platform’s evaluation system includes 50+ metrics, such as groundedness, hallucination detection, tool-use correctness, PII compliance, and tone analysis. These metrics combine LLM-as-judge evaluations with heuristic and machine learning-based assessments. Users can define custom rubrics or leverage pre-built scanners for PII, jailbreak attempts, and injection attacks. The protection layer integrates with 15 vendor adapters, including Lakera, Presidio, and Llama Guard, for real-time content filtering.
Monitoring is powered by OpenTelemetry-native traces, providing span graphs, latency metrics, and live dashboards. The Agent Command Center serves as an OpenAI-compatible gateway, supporting 100+ LLM providers (e.g., OpenAI, Anthropic, Google Gemini, Mistral) and 15 routing strategies. It includes features like semantic caching, virtual keys, and multi-cloud provider (MCP) support, ensuring efficient and secure LLM interactions.
Optimization is driven by six algorithms: GEPA, PromptWizard, ProTeGi, Bayesian, Meta-Prompt, and Random Search. These algorithms analyze production traces to refine prompts, reduce hallucinations, and improve agent performance iteratively. The platform’s feedback loop ensures that insights from simulations and live deployments directly inform future iterations, fostering self-improving agents.
Use Cases
One key use case is simulating edge cases before deployment. For example, the platform can generate thousands of multi-turn conversations with realistic personas, adversarial inputs, and voice interactions to test agent resilience.
In evaluation, Future AGI assesses agent performance across metrics like factual grounding, privacy compliance, and adherence to instructions. The platform’s LLM-as-judge system cross-references responses against context, identifying hallucinations or unsafe advice.
Built-in scanners detect PII, jailbreak attempts, and prompt injections, while vendor integrations (e.g., Llama Guard) add layers of defense. The platform’s real-time filtering can even interrupt unsafe interactions, such as redirecting suicidal callers to mental health resources.
For example, traces reveal redundant retries in tool calls, slow vector database queries, or misconfigured service names that obscure debugging. The platform’s span graphs help identify bottlenecks, such as excessive token usage or inefficient routing, enabling teams to optimize performance. Live dashboards track key metrics like CSAT scores, hallucination rates, and compliance violations, offering actionable insights for continuous improvement.
The Agent Command Center acts as a proxy for LLM interactions, supporting multi-provider routing, caching, and guardrails. This is critical for enterprises using diverse LLM providers (e.g., OpenAI for general tasks, Vertex AI for healthcare) or managing multi-agent workflows. The gateway’s low-latency routing (≤21ms P99) ensures responsive interactions, even under high load.
Optimization leverages production data to refine agent behavior. For instance, if an agent repeatedly fails to de-escalate hostile callers, Future AGI’s prompt optimization algorithms (e.g., GEPA) generate alternative responses, test them in simulations, and deploy the best-performing versions. This closed-loop process accelerates iteration, reducing the time from prototype to production.
Key Features & Capabilities
These include text-based conversations, voice interactions (via LiveKit and Silero VAD), and adversarial inputs designed to expose vulnerabilities. The platform’s persona-driven simulations ensure agents handle diverse user behaviors, from empathetic responses to compliance with legal requirements.
The evaluation framework supports 50+ metrics, categorized into groundedness, hallucination, tool correctness, PII, tone, and custom rubrics. Metrics are computed using a combination of LLM-as-judge evaluations, heuristic checks, and machine learning models. For instance, groundedness is measured by comparing responses to provided context, while hallucination detection flags unsupported claims.
Protection mechanisms include 18 built-in scanners for PII, jailbreaks, and injections, alongside 15 vendor adapters (e.g., Lakera, Presidio). These can be deployed inline within the gateway or as standalone SDKs. For example, a healthcare agent might use Presidio to redact patient data in real-time, while a customer support agent could block prompt injection attempts to manipulate responses.
Observability is enhanced by OpenTelemetry-native tracing, which captures spans for 50+ frameworks, including LangChain, LlamaIndex, and CrewAI. Traces include latency metrics, token costs, and error attribution, enabling teams to debug issues like redundant tool calls or slow vector database queries. The platform’s span graphs visualize agent workflows, highlighting inefficiencies such as excessive retries or misconfigured service names.
The Agent Command Center serves as an OpenAI-compatible gateway, supporting 100+ LLM providers and 15 routing strategies. It includes features like semantic caching, virtual keys, and multi-cloud provider (MCP) support, ensuring efficient and secure LLM interactions. The gateway’s performance benchmarks show it can handle up to 29,000 requests per second with a P99 latency of ≤21 milliseconds, even when guardrails are active.
Optimization algorithms include GEPA, PromptWizard, ProTeGi, Bayesian, Meta-Prompt, and Random Search, which analyze production traces to refine prompts and agent behavior. These algorithms are designed to work with minimal labeled data, making them adaptable to domain-specific challenges, such as improving technical accuracy in coding agents or empathy in healthcare interactions.
Deployment options include Docker Compose, Kubernetes (via Helm charts), and cloud providers like AWS, GCP, and Azure. The self-hosted path requires Docker Desktop or Docker Engine, while the cloud service offers a free tier with SOC 2 Type II and HIPAA compliance. Production deployments use ./deploy/setup.sh to generate secrets, pin image versions, and ensure data sovereignty.
Future AGI’s architecture is modular, with components like traceAI for OpenTelemetry instrumentation, agentcc-gateway for LLM routing, and agenticeval for evaluation metrics. The platform’s SDKs—such as fi-instrumentation-otel, ai-evaluation, and agent-opt—enable integration with existing stacks, while the simulate-sdk supports voice-agent testing via LiveKit and Silero VAD.
Recent updates, such as version 1.40.0 (2026-09-23), introduced enhancements to the error-feed system, including improved grouping of issues, severity assessment, and causal breadcrumbs. Version 1.39.0 added support for Vertex AI provider credentials and refined request logging, while version 1.38.4 addressed bugs in evaluation tasks to prevent redundant retries of failed operations.
The platform’s release cycle reflects its focus on production readiness. These migrations are applied through the deployment process, with explicit instructions for backfilling legacy scans. The platform’s commitment to reproducibility is demonstrated by its benchmark harness, which validates performance claims across different workloads.
Future AGI’s documentation and community resources include a comprehensive API reference, type-hinted SDK examples, and end-to-end tutorials for common workflows. The platform’s blog covers topics like AI observability, agent evaluation, and hallucination detection, with guides on selecting evaluation tools, optimizing prompts, and designing secure agent loops. Community engagement is facilitated through Discord, GitHub discussions, and a feedback-driven documentation process, ensuring continuous improvement based on user needs.