AI Agents hallucinate. Fix it faster.
The open-source platform for shipping self-improving AI agents. Evaluations, tracing, simulations, guardrails, gateway, optimization. Everything runs on one platform and one feedback loop, from first prototype to live deployment.
Why Future AGI?
Most AI agents fail in production, and teams end up stitching together evals, observability, and guardrails that never close the loop.
Future AGI collapses all of it into one platform and one feedback loop. Simulate edge cases before launch, evaluate what happens in production, protect users in real time, and turn every trace into signal for the next version.
The result: agents that don't just get monitored, they self-improve.
All-in-one
No more stitching Langfuse + Braintrust + Helicone + Guardrails AI + a custom simulator. One platform covers the lifecycle: simulate → evaluate → protect → monitor → optimize, with data flowing back as a loop.
Open & self-hostable
Apache 2.0 core. Every evaluator, every prompt, every trace is inspectable — no black-box scoring. Self-host for data sovereignty or use our managed Cloud. Drop in your own stack at any layer via OTel / OpenAI-compatible HTTP.
Built for production
Go-based gateway with ~9.9 ns weighted routing, ~29 k req/s on t3.xlarge, P99 ≤ 21 ms with guardrails on. OpenTelemetry-native traces. 50+ framework instrumentors. Every claim reproducible via the committed benchmark harness.
🚀 Quickstart
You need Docker Desktop or Docker Engine with Compose v2.24 or newer, and
for the default Standalone setup 2 vCPUs and 4 GB of memory given to Docker.
No installation required, get started immediately.
Generous free-tier, no credit card required.
SOC 2 Type II · HIPAA · data stays in your region.
One command, full stack. Published images, no source build.
git clone https://github.com/future-agi/future-agi.git
cd future-agi
./bin/install # Windows (PowerShell): .\bin\install.ps1
The installer checks your machine, writes this install's secrets to .env,
downloads the images (about 800 MB) and waits until everything answers. The
first boot sets up the database and takes a few minutes; http://localhost:3000
shows its progress meanwhile.
2. Sign in and copy your keys
Open http://localhost:3000, create your account (or sign in with the one the
installer made), then copy the API key and secret key from Keys in the
sidebar.
3. Send your first trace
pip install fi-instrumentation-otel
export FI_API_KEY="<your API key>" FI_SECRET_KEY="<your secret key>" FI_BASE_URL="http://localhost:4318"
from fi_instrumentation import register
from fi_instrumentation.fi_types import ProjectType
tracer_provider = register(project_name="my-first-project", project_type=ProjectType.OBSERVE)
with tracer_provider.get_tracer("quickstart").start_as_current_span("hello-future-agi") as span:
span.set_attribute("input.value", "Hello, Future AGI")
tracer_provider.force_flush()
Open Tracing in the sidebar: my-first-project holds your first span.
http://localhost:4318 is this install's trace collector; it listens on this
machine only.
| Standalone (default) | Distributed (at scale) |
|---|
| Install | ./bin/install | ./bin/install --distributed |
| Runs | one app container, Postgres, ClickHouse | one container per service, PeerDB, Kafka |
| Docker resources | 2 vCPUs, 4 GB | 4+ vCPUs, 12–16 GB |
| Kubernetes | | Helm chart: about 4 CPUs and 8 GiB free for an evaluation |
Choose before you add data: there is no supported way to move a Standalone
install's data to Distributed or Helm later
(Switching).
- Configure: every variable in
.env is described in
the configuration reference. Nothing is required for a
local install; add LLM provider keys, a public URL or email when you need them.
- Manage:
docker compose logs -f app (Distributed: backend); stop with
docker compose down or ./bin/uninstall (both keep your data); upgrade
with git pull && ./bin/install; remove everything, data included, with
./bin/uninstall --purge.
- Develop: on a branch other than
main, add --from-source to build the
images from your checkout; ./bin/dev runs it with hot reload
(Local development).
- More: the self-hosting guide,
INSTALLATION.md (every option and troubleshooting) and
deploy/README.md (production).
Instrument your first agent
Swap the hand-made span for an instrumentor to trace a real app, here OpenAI
(pip install traceai-openai). Against your own install, keep the FI_*
variables from step 3 set.
Python
from fi_instrumentation import register
from traceai_openai import OpenAIInstrumentor
register(project_name="my-agent")
OpenAIInstrumentor().instrument()
# Your existing OpenAI code is now traced.
response = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": query}],
)
TypeScript
import { register } from "@traceai/fi-core";
import { OpenAIInstrumentation } from "@traceai/openai";
register({ projectName: "my-agent" });
new OpenAIInstrumentation().instrument();
// Your existing OpenAI code is now traced.
const response = await openai.chat.completions.create({
model: "gpt-4o",
messages: [{ role: "user", content: query }],
});
Full docs → · Cookbooks → · API reference →
Core features
Six pillars. Each one replaces a tool you probably have.
🧪 Simulate
Thousands of multi-turn conversations against realistic personas, adversarial inputs, and edge cases. Text and voice (LiveKit, VAPI, Retell, Pipecat).
Docs →
This is the beginning of the README. Read the rest at the original. Read the full README ↗
