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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.
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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.
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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. |
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. |
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. |
Run the whole platform on your own machine in three steps. Rather not run anything? Try Cloud free.
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.
1. Install
git clone https://github.com/future-agi/future-agi.git
cd future-agi
./bin/install # Windows (PowerShell): .\bin\install.ps1The 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
.envis 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 withdocker compose downor./bin/uninstall(both keep your data); upgrade withgit pull && ./bin/install; remove everything, data included, with./bin/uninstall --purge. - Develop: on a branch other than
main, add--from-sourceto build the images from your checkout;./bin/devruns it with hot reload (Local development). - More: the self-hosting guide, INSTALLATION.md (every option and troubleshooting) and deploy/README.md (production).
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.
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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 →
Six pillars. Each one replaces a tool you probably have.
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Thousands of multi-turn conversations against realistic personas, adversarial inputs, and edge cases. Text and voice (LiveKit, VAPI, Retell, Pipecat). |
50+ metrics under one |
18 built-in scanners (PII, jailbreak, injection, …) + 15 vendor adapters (Lakera, Presidio, Llama Guard, …). Inline in gateway or standalone SDK. |
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OpenTelemetry-native tracing across 50+ frameworks (LangChain, LlamaIndex, CrewAI, DSPy…). Span graphs, latency, token cost, live dashboards. Zero-config. |
OpenAI-compatible gateway. 100+ providers, 15 routing strategies, semantic caching, virtual keys, MCP, A2A. ~29k req/s, P99 ≤ 21ms with guardrails on. |
Six prompt-optimization algorithms (GEPA, PromptWizard, ProTeGi, Bayesian, Meta-Prompt, Random). Production traces feed back as training data. |
| Target | Status | Notes |
|---|---|---|
| Docker Compose: Standalone | ✅ | ./bin/install: one app container next to Postgres and ClickHouse, for a laptop or a single VM |
| Docker Compose: Distributed | ✅ | ./bin/install --distributed: one container per service, for scale on one host |
| Production Compose overlay | ✅ | ./deploy/setup.sh on the Distributed setup: --skip-up writes deploy/.env.production (generating only the secrets you do not supply; you give every image version), then, once the databases are initialized, --confirm-initialized pulls the images and starts the stack (deploy/README.md) |
| Kubernetes / Helm | ✅ | Distributed on Kubernetes: helm install futureagi oci://ghcr.io/future-agi/charts/futureagi --version X.Y.Z, one signed chart for the open-source and Enterprise editions (chart README) |
| AWS / GCP / Azure | ✅ | Docker Compose on a VM, or the Helm chart on a Kubernetes 1.27+ cluster |
| AWS Marketplace | ⏳ | Coming soon |
| Air-gapped / on-prem | ✅ | Mirror the images, set FUTURE_AGI_TELEMETRY_DISABLED=true and block outbound traffic (Telemetry); on Helm, set global.airgap=true and mirror the images the release lists (chart README); contact sales for support |
Every image, tag and size: Container images. Every setting: Configuration reference.
Every arrow is an open, documented interface: OpenTelemetry OTLP for traces, OpenAI-compatible HTTP for the gateway, Postgres / ClickHouse SQL for storage. Drop in your own stack at any layer.
Runtime: Python 3.11+ (Django 5.1 + Channels) · Go 1.25+ (gateway), 1.24+ (trace collector) · React 18 + Vite · Node 22.18+. Data: PostgreSQL (metadata) · ClickHouse (spans + time-series) · Redis (state, live updates) · Temporal (jobs).
Component breakdown (per-package)
| Layer | Component | Code |
|---|---|---|
| Edge | traceAI — OpenTelemetry instrumentation | future-agi/traceAI |
| Edge | Agent Command Center — OpenAI-compatible proxy | agentcc-gateway/ |
| Platform | tracer — OTLP ingest, span graph | futureagi/tracer/ |
| Platform | agentic_eval — 50+ metrics, LLM-as-judge | futureagi/agentic_eval/ |
| Platform | simulate — persona-driven scenario generation | futureagi/simulate/ |
| Platform | model_hub — LLM routing, embeddings, datasets | futureagi/model_hub/ |
| Platform | accounts · usage · integrations — auth, orgs, metering, connectors | futureagi/accounts/ |
| Data | PostgreSQL · ClickHouse · Redis · Temporal | — |
Future AGI is an open-source ecosystem — each SDK is independently usable, independently packaged, Apache/MIT-licensed.
| Repo | Install | Languages | Purpose |
|---|---|---|---|
| traceAI | pip install fi-instrumentation-otelnpm i @traceai/fi-core |
Python · TS · Java · C# | Zero-config OTel tracing for 50+ AI frameworks |
| ai-evaluation | pip install ai-evaluationnpm i @future-agi/ai-evaluation |
Python · TS | 50+ evaluation metrics + guardrail scanners |
| futureagi | pip install futureagi |
Python | Platform SDK — datasets, prompts, KB, experiments |
| agent-opt | pip install agent-opt |
Python | 6 prompt-optimization algorithms (GEPA, PromptWizard, …) |
| simulate-sdk | pip install agent-simulate |
Python | Voice-agent simulation via LiveKit + Silero VAD |
| agentcc | pip install agentccnpm i @agentcc/client |
Python · TS (+ LangChain · LlamaIndex · React · Vercel) | Gateway client SDKs |
| LLM providers (100+) | OpenAI · Anthropic · Google Gemini · Vertex AI · AWS Bedrock · Azure OpenAI · Mistral · Groq · Cohere · Together · Perplexity · OpenRouter · Fireworks · xAI · Replicate · HuggingFace · + self-hosted Ollama · vLLM · LM Studio · TGI · Llamafile |
| Agent frameworks | LangChain · LangGraph · LlamaIndex · CrewAI · AutoGen · Phidata · PydanticAI · Claude SDK · LiteLLM · Haystack · DSPy · Instructor · Smol-agents |
| Voice platforms | VAPI · Retell · LiveKit · Pipecat |
| Vector DBs | Pinecone · Weaviate · Chroma · Milvus · Qdrant · pgvector |
| Tools & infra | Vercel AI SDK · n8n · MongoDB · MCP · A2A · Guardrails AI · Langfuse · HuggingFace Smol-agents |
| Future AGI | Langfuse | Phoenix | Braintrust | Helicone | |
|---|---|---|---|---|---|
| Open source | ✅ Apache 2.0 | ✅ MIT | ✅ Elastic v2 | ❌ | ✅ Apache 2.0 |
| Self-host | ✅ | ✅ | ✅ | ❌ | ✅ |
| LLM tracing (OpenTelemetry) | ✅ | ✅ | ✅ | ✅ | via OpenLLMetry |
| Evaluation suites | ✅ 50+ metrics | ✅ | ✅ | ✅ | Limited |
| Agent simulation | ✅ | ❌ | ❌ | ❌ | ❌ |
| Voice agent eval | ✅ | ❌ | Cookbook | ❌ | ❌ |
| LLM gateway built in | ✅ 100+ providers | ❌ | ❌ | ✅ | ✅ |
| Guardrails built in | ✅ 18 + 15 adapters | ❌ | ❌ | ❌ | ❌ |
| Prompt optimization | ✅ 6 algorithms | ❌ | ❌ | ❌ | ❌ |
| Prompt management | ✅ | ✅ | ✅ | ✅ | ✅ |
| Datasets & experiments | ✅ | ✅ | ✅ | ✅ | ✅ |
| No-code eval builder | ✅ |
Based on publicly-documented features as of April 2026. Corrections welcome — open a PR.
- Customer Support: Ship support AI that customers actually trust
- Voice Agents: Test, evaluate, and improve voice AI end-to-end
- Internal Tools: AI copilots your whole org can rely on
- RAG & Search: Every answer grounded, every citation verified
- Autonomous Agents: Multi-step agents you can actually trust in production
- Computer-Use Agents (CUA): Agents that click with confidence
- Coding Agents: AI that writes code you can actually ship
Vote on the public roadmap → · GitHub Discussions · Releases · Changelog
| Recently shipped | In progress | Coming up | Exploring |
|---|---|---|---|
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We love contributions — bug fixes, new evaluators, framework integrations, docs, examples, anything.
- Browse
good first issue - Read the Contributing Guide
- Say hi on Discord or Discussions
- Sign the CLA on your first PR (automatic bot)
| 💬 Discord | Real-time help from the team and community |
| 🗨️ GitHub Discussions | Ideas, questions, roadmap input |
| 🐦 Twitter / X | Release announcements |
| 📝 Blog | Engineering & research posts |
| 📺 YouTube | Walkthroughs & demos |
| 📊 Status | Cloud uptime + incident history |
| 📧 support@futureagi.com | Cloud account / billing |
| 🔐 security@futureagi.com | Private vulnerability disclosure (24h ack on weekdays — see SECURITY.md) |
Self-hosted Future AGI sends deployment telemetry, on by default, so we can count installs and size release testing: one registration with the email addresses and email domains of the install's owner, admin and staff/superuser accounts, then usage counts on a schedule. No trace data, no prompts, no completions, no datasets, no API keys, ever.
To opt out, install with ./bin/install --no-telemetry, or set FUTURE_AGI_TELEMETRY_DISABLED=true in .env (deploy/.env.production for the production overlay, config.telemetry=false for Helm) and run docker compose up -d. Opting out still sends one registration, without email addresses; block api.futureagi.com to send nothing. Everything else that could leave your install (HubSpot, Slack, Mixpanel, PostHog, reCAPTCHA, Sentry, Mailgun) is off until you set its key.
Telemetry and outbound connections has the exact payloads, what the opt-out still sends, every setting, and every outbound connection, with what an install that allows no outbound traffic must also set.
Future AGI is licensed under the Apache License 2.0. See LICENSE and NOTICE.
You own your evaluation logic and your data. Inspect every evaluator, every prompt, every trace — no black-box scoring, no vendor lock-in.
Built with ❤️ by the Future AGI team and contributors worldwide.
If Future AGI helps you ship better AI, a ⭐ helps more teams find us.
🌐 futureagi.com · 📖 docs.futureagi.com · ☁️ app.futureagi.com · 📊 status.futureagi.com