If you are building AI agents, you probably use several tools at once. One tool for evaluations. Another for tracing. A third for guardrails. Maybe a fourth for alerts. Each tool has its own dashboard, its own config, its own billing.
Future AGI puts all four into one platform. It is open-source, self-hostable, and production-ready.
The Problem with Tool Sprawl
Here is a typical AI agent monitoring stack in 2026:
- LangSmith — tracing and evaluation
- Weights & Biases — experiment tracking
- A guardrails library — content safety, PII detection
- A custom alert system — when something breaks
Each tool adds latency. Each tool requires maintenance. And when something goes wrong, you switch between four dashboards to find the cause.
What Future AGI Does
Future AGI is a single platform that covers:
- Evaluation — 50+ built-in metrics for accuracy, toxicity, coherence, and more
- Observability — full request tracing across your LLM pipeline
- Guardrails — content safety and policy checks on every request
- Prompt optimization — test and improve prompts without code changes
You deploy one service. You open one dashboard. Your team works from one source of truth.
Performance Numbers
These numbers come from the official docs:
| Metric | Value |
|---|---|
| Throughput | ~29,000 requests/second on a t3.xlarge |
| P99 latency (guardrails ON) | ≤ 21ms |
| Eval metrics built in | 50+ |
| LLM providers supported | 28+ |
The guardrails are on-path — not a side-car. That P99 of 21ms includes the safety checks running inline.
It Is Apache 2.0
This is important. Apache 2.0 means:
- You can use it commercially
- Your data never leaves your infrastructure
- You can fork and modify it
No vendor lock-in. No usage-based pricing. You own your observability data.
Getting Started
Future AGI is Docker Compose ready. You can have a local instance running in minutes:
git clone https://github.com/future-agi/future-agi
cd future-agi
docker compose up
The default config starts the API server, the evaluation engine, and the dashboard. Open http://localhost:3000 to see the UI.
To connect your LLM calls, wrap them with the SDK:
from futureagi import FutureAGI
client = FutureAGI(api_url="http://localhost:8080")
response = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": "Hello"}],
guardrails=True,
trace=True,
)
Every call is now traced, evaluated, and checked by guardrails — automatically.
When to Use Future AGI
Use it if:
- You are building AI agents in production
- You want to monitor LLM behavior without paying for multiple SaaS tools
- Your team needs a shared view of eval results and traces
- You need guardrails but cannot afford latency from a third-party API
Skip it if:
- You are building a quick prototype (overkill for that)
- You need real-time alert integrations with PagerDuty or Slack (check their docs for current integrations)
Who Is It For
Future AGI is best for teams that are past the prototype stage. If you have AI agents running in production and you care about reliability and safety, this is worth evaluating. The self-hosting option makes it practical for companies with data privacy requirements.
For solo developers, the Docker Compose setup is simple enough to try locally before committing to anything.