Future AGI alternatives

Why Teams Look Past Future AGI

Future AGI is a lot of platform, and most teams who look for an alternative are not unhappy with any single feature. They run into one of four specific things.

• The self-host footprint. Self-hosting uses Docker Compose with PostgreSQL, ClickHouse, Redis, Temporal, MinIO and a code executor, and the docs do not provide a Helm chart. The documented production size starts at 16 or more CPU cores and 32 GB of RAM. The details are in Future AGI self-hosting requirements.
• Enforcement tied to the gateway. Tool Permissions and MCP Security act on traffic routed through the Agent Command Center. Teams whose risky actions happen inside a coding agent on an engineer's machine want the control at the agent itself.
• More platform than the job. A team that needs tracing and evals still deploys, secures and upgrades the gateway, simulation and optimizer services when it self-hosts.
• Reference customers. Future AGI names no customers on its site, and its case studies are anonymized ("Fortune 50 Retailer", "Code Agent Startup"). That is common for a young company and says nothing about the product, but some procurement teams ask for a named reference.

Before moving, list what you would give up: Turing evaluation models, built-in agent metrics such as Tool Call Accuracy and Trajectory Match, voice simulation, prompt optimization and an Apache-2.0 core. Some repository code uses a separate enterprise licence. None of the options below matches the complete feature set.

The Shortlist

Failproof AI - For Automated Failure Discovery and Fixing

Failproof AI uses code-based and LLM-based evaluations to review production sessions, then automatically groups recurring failures into findings with evidence, severity and a recommended fix. Alerts route those findings to the team that owns them. After changing the agent, prompt, tool or workflow, teams can rerun the evaluations on the affected sessions and keep monitoring the behavior in production. A suitable high-risk finding can also become a tested behavioral policy.

• Limits. No simulation or prompt-optimization suite. Self-hosted Cloud is an Enterprise deployment.
• Best for. Teams that want automated failure intelligence, evaluations and alerts in one workflow, with tested policies available when a failure can be prevented through agent steering. See Failproof AI vs Future AGI.

Langfuse - If You Want Open-Source Tracing and Evals with Helm

Open-source LLM observability with tracing, prompt management and evaluations - LLM-as-a-judge, code evaluators, user feedback and manual labeling. MIT outside its ee folders, with about 34,600 GitHub stars. Self-host with Docker Compose, Helm on Kubernetes, or Terraform templates for AWS, Azure and GCP. Cloud runs from a free Hobby plan (50k units a month) to Core at $29 and Pro at $199 a month.

• Limits. The self-hosted stack still requires Postgres, ClickHouse, Redis and S3-compatible storage. Runtime enforcement requires another tool.
• Best for. Teams that want traces, prompts and evaluations on their own Kubernetes cluster.

Latitude - If You Want a Fully MIT Platform with Coding-Agent Handoff

Open-source agent observability, MIT since May 2026. It traces sessions over OpenTelemetry, evaluates with rules, an LLM judge or custom JavaScript, supports human annotation, and its Agent Dispatch sends Claude Code or Cursor to propose pull requests from failures. Self-host with Docker Compose or Swarm, Helm or Railway; cloud is free to start and Pro is $99 a month.

• Limits. It scores completed sessions and does not block actions. Its cloud runs in the EU, and the pricing page does not define what a credit measures.
• Best for. Teams that want free MIT self-hosting, unlimited seats and coding-agent proposals for fixing failures.

Galileo - If You Want Deep Evals and an Enterprise Vendor

An evaluation and observability platform with 20+ out-of-box evals, Luna-2 small evaluation models, and CLHF for tuning judges from as few as five labeled records; Cisco completed its acquisition in May 2026. For runtime control it points to Agent Control, an Apache-2.0 project with pre- and post-execution checks on LLM and tool inputs and outputs. Free, then Pro at $100 a month billed yearly.

• Limits. Hosted guardrails, Luna-2, VPC and on-premises deployment require Enterprise. You deploy and operate Agent Control yourself.
• Best for. Larger teams that want a deep evaluation catalog and a vendor with named enterprise customers.

Judgment Labs - If Judging Is the Whole Job

Agent tracing through the Apache-2.0 judgeval SDK, a rubric-driven Agent Judge and a Code Judge, and Behavior Discovery for failure patterns you did not know to look for. Agent Behavior Monitoring scores sampled traces after they run and alerts on what it finds.

• Limits. No published pricing, a demo-first buying process, no runtime blocking and platform self-hosting still listed as coming soon.
• Best for. Teams focused on judge quality and comfortable buying through sales.

Raindrop - If Production Signals Matter More than Offline Evals

Production monitoring for AI products: plain-language Signals that become yes/no classifiers, issue detection with a Triage Agent, and A/B Experiments on live traffic. A free Hobby plan covers 1,000 events a month; Pro is $299 a month plus per-event charges.

• Limits. It detects and alerts after the event, Slack is its only alert channel, and self-hosting is a partner beta.
• Best for. Consumer AI products with enough users to learn from production traffic.

Side by Side

How to Choose

• You want failures found and carried through verification: Failproof AI evaluates production sessions, groups recurring problems into findings, recommends fixes, reruns evaluations after the change and can turn suitable findings into tested behavioral policies.
• You want open source and a simpler self-host: Langfuse or Latitude, depending on whether you want prompt management (Langfuse) or fix PRs (Latitude).
• Judge quality is the product: Galileo or Judgment Labs, and budget for a sales conversation on the Enterprise features.
• You have live users and want to learn from them: Raindrop.

Several of these products can be combined. Evaluations and runtime controls sit at different points in the workflow. If you use more than one platform, decide which one owns traces, evaluation definitions, alerts and the final verification of a fix.

When Staying on Future AGI Is the Right Call

If the self-host stack is the only problem, Future AGI's managed cloud removes it, and its enterprise page lists deployment into your own AWS, GCP or Azure account. If your agents are applications that call models through the Agent Command Center, its Tool Permissions guardrail is already the control point for their tool calls. And if you use simulation, voice agents or prompt optimization, no single option above replaces them. In those cases the cheaper move is to stay and add a narrow tool only for the gap you actually have.