Open-Source Observability · future-agi

future-agi

Future AGI is an open-source platform for building, evaluating, and monitoring AI agents in production. It combines tracing, evals, simulations, guardrails, and a gateway into one self-hostable system with Apache 2.0 licensing.

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Source: GitHub — github.com/future-agi/future-agi

1.3k

GitHub stars

343

Forks

Python

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	future-agi/future-agi
Owner	future-agi
Primary language	Python
License	Apache-2.0 — OSI-approved
Stars	1.3k
Forks	343
Open issues	414
Latest release	0.5.10 (2026-06-23)
Last updated	2026-07-08
Source	https://github.com/future-agi/future-agi

What future-agi is

Python/TypeScript platform with Go-based gateway (~29k req/s, P99 ≤21ms), OpenTelemetry-native tracing across 50+ frameworks, 50+ evaluation metrics (LLM-as-judge, heuristic, ML), 18 built-in security scanners, and 6 prompt-optimization algorithms. Self-hostable via Docker or managed Cloud.

Quickstart

Get the future-agi source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/future-agi/future-agi.git2cd future-agi3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Production LLM/Agent Observability & Closed-Loop Optimization

Teams shipping AI agents need end-to-end visibility from simulation through live traces. Future AGI eliminates the need to stitch Langfuse + Braintrust + custom simulators by unifying tracing, evals, and optimization in one feedback loop.

Real-Time Guardrails & Compliance at Scale

Organizations requiring PII redaction, jailbreak detection, or injection filtering can deploy 18 built-in scanners (or 15 vendor adapters like Lakera/Presidio) inline in the gateway or via standalone SDK, with no separate infrastructure.

Multi-Agent Simulation & Edge-Case Testing Pre-Launch

Before deploying agents to users, teams can run thousands of synthetic multi-turn conversations (text and voice via LiveKit/VAPI/Retell) against adversarial personas and edge cases, then evaluate outcomes against 50+ metrics in a single platform.

Implementation considerations

Self-hosting requires Docker and orchestration setup; production deployment via ./deploy/setup.sh to generate secrets and pin image versions. Cloud option available for faster onboarding.
Instrumentation is SDK-based (Python `fi_instrumentation`, TypeScript `@traceai/fi-core`). Requires code changes to register project and enable framework instrumentors (50+ supported frameworks).
Traces flow to either managed Cloud (SOC 2 Type II, HIPAA mentioned) or self-hosted database. Data residency and compliance implications must align with org policy before adoption.
Gateway routing (~9.9 ns weighted routing, 50+ providers, 15 strategies) adds a hop; verify P99 latency (≤21ms claimed with guardrails) meets your SLA before production rollout.
Security scanners (PII, jailbreak, injection) integrate inline or standalone; configure and test thresholds/vendor integrations (Lakera, Presidio, Llama Guard) for your threat model.

When to avoid it — and what to weigh

Greenfield Startups with No LLM Agent Workload Yet — This platform is built for observing and improving existing AI agents. If you're not actively instrumenting LLM calls or running agents in production, the complexity overhead is not justified.
Teams Requiring Stable, Long-Term LTS Guarantees — README explicitly flags the project as 'nightly release for early testing' with 'rough edges.' Stable version promised but not yet released. Production use carries adoption risk until v1.0+ stability is demonstrated.
Organizations with Vendor Lock-In Sensitivity — While Apache 2.0 licensed and self-hostable, tight coupling to their SDK, instrumentors, and gateway means migrating traces/configs to another system is non-trivial. Evaluate lock-in tolerance upfront.
Teams Needing Non-Python/TypeScript Language Support — Instrumentors documented for Python and TypeScript via OpenAI-compatible HTTP. No native SDKs mentioned for Go, Rust, Java, or other languages; integration via HTTP gateway required.

License & commercial use

Apache License 2.0. Permissive OSI-approved license permitting commercial use, modification, and distribution with attribution and liability disclaimer. No patent indemnity clause present in standard AL 2.0.

Apache 2.0 explicitly permits commercial use. You may use, modify, and redistribute the software in proprietary products. However, do not assume commercial support, SLAs, or liability coverage without negotiating a separate commercial agreement with future-agi maintainers. Cloud offering (app.futureagi.com) operates separately under their terms.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	Moderate
DEV.co fit	Strong
Assessment confidence	Medium

Security considerations

18 built-in scanners (PII, jailbreak, injection, etc.) and 15 vendor adapters available; deploy in gateway or SDK to intercept malicious/sensitive outputs. Self-hosting gives you control over data location and access. Cloud tier claims SOC 2 Type II and HIPAA compliance; verify current attestations. Gateway and scanner configurations require careful tuning; misconfiguration could allow bypass. No independent security audit details provided.

Alternatives to consider

Langfuse (OSS tracing + evals)

Langfuse is mature, widely adopted, and focused on observability + evals. Lacks integrated simulation, guardrails gateway, and prompt optimization; you'll still need separate tools for those.

Braintrust (evals + dataset management)

Braintrust excels at eval workflows and dataset curation. Not a tracing/observability platform; lacks integrated gateway and production monitoring. Complementary, not a replacement.

LangSmith (LangChain native, observability + evals)

Tight integration with LangChain agents. Narrower framework coverage than Future AGI, no built-in gateway or guardrails, but simpler if you're all-in on LangChain ecosystem.

Software development agency

Build on future-agi with DEV.co software developers

Start with the free Cloud tier (no install) or self-host in 60 seconds. Review docs, join Discord, and test against your current observability/eval stack to assess lock-in and stability risk before production migration.

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future-agi FAQ

Can I use Future AGI without modifying my LLM code?

Partially. If you route through the OpenAI-compatible gateway, minimal changes needed (just swap endpoint). Full tracing requires SDK instrumentation (Python/TS). HTTP passthrough via gateway works for non-instrumented calls but loses native span richness.

What happens if I self-host? Do I get the same features as Cloud?

Unknown from provided data. Self-hosted likely has feature parity for core (tracing, evals, guardrails, gateway), but Cloud may offer managed backups, scaling, and compliance attestations (SOC 2, HIPAA) that self-host requires you to provide.

Is this production-ready?

README explicitly states 'nightly release for early testing' with 'rough edges' and 'stable version coming soon.' Gateway benchmarks (29k req/s, P99 ≤21ms) suggest production-capable infra, but stability and support guarantees are unclear until v1.0+ released.

Can I extend or customize evaluators and scanners?

Likely yes (Apache 2.0, inspectable code), but detailed extensibility patterns not provided in excerpt. Refer to docs and source for SDK/plugin architecture.

Software developers & web developers for hire

Need help beyond evaluating future-agi? DEV.co is a software development agency offering software development services and web development for teams of every size. Our software developers and web developers build custom software, web applications, APIs, and open-source observability integrations — and maintain them long-term.

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