Future AGI
Build self-improving AI agents that hallucinate less.
For teams building production AI agents that need to catch hallucinations early, Future AGI's simulation depth and multimodal eval are unmatched. Simpler chatbot projects should look elsewhere. Its free tier and transparent usage-based pricing make it a strong alternative to LangSmith for compliance-focused testing.
Verified 18d ago · liveness 95/100 · cite: rightaichoice.com/tools/future-agi
- Teams building AI agents in production who need to catch hallucinations early
- Enterprises requiring compliance testing with simulated scenarios (e.g., debt collection, healthcare)
- Developers iterating on agent behavior using automated evaluation scores
- Product managers monitoring agent performance with real-time dashboards
- Simple chatbot projects without complex agent behavior
- Teams solely needing LLM fine-tuning or training tools
- Users looking for a no-code/low-code agent builder (requires coding)
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Skip Future AGI if you're building a simple chatbot without complex agent behavior or compliance requirements.
Going past 2K monthly eval credits adds $10 per 1K credits, which adds up fast if you run many LLM-as-judge evaluations.
Future AGI's free tier is generous for small teams (50GB tracing, 2K eval credits). Mid-sized teams will likely stay within free limits or pay modest usage fees. Compared to LangSmith's usage-based pricing, Future AGI offers a more transparent cost structure with volume discounts. Startups with limited budgets benefit from the free tier; enterprises pay for scale.
In short
Future AGI — Build self-improving AI agents that hallucinate less. Best for Teams building AI agents in production who need to catch hallucinations early, Enterprises requiring compliance testing with simulated scenarios (e.g., debt collection, healthcare), Developers iterating on agent behavior using automated evaluation scores. Free to use.
What's new in Future AGI
Checked 17 days agoAcross the latest 8 updates: 1 feature update and 7 news mentions.
Perplexity contributes Sonar and gpt-5.1 to self-hosted Future AGI
Five Sonar variants and gpt-5.1 added to eval model picker and AI gateway for self-hosted users.
Multimodal LLM-as-a-Judge in 2026: How to Evaluate Images and Audio Without Ground Truth
Techniques for using multimodal LLMs as judges for image and audio outputs when ground truth is unavailable.
Your LLM Eval Failed. Which Input Broke It? Field-Level Eval Attribution in 2026
Introduces field-level evaluation attribution to pinpoint failing inputs across LLM evaluations.
Falcon AI in 2026: The Platform-Native Copilot That Operates Your Eval Stack
Describes Falcon AI, a copilot integrated into Future AGI for managing evaluation workflows.
DSPy Optimizers Explained in 2026: BootstrapFewShot, MIPROv2, COPRO, and GEPA
Tutorial on DSPy optimizers and their use cases for LLM program optimization.
Automatic Prompt Optimization in 2026: How Textual Gradients, Genetic Search, and Meta-Prompts Actually Work
Covers practical algorithms for automatic prompt optimization: ProTeGi, OPRO, GEPA, and meta-prompting.
Agent Runtime Guardrails in 2026: The Tool-Call Scanners Most Stacks Skip
Explains why PII/toxicity scanners miss tool calls and how agent runtime guardrails (tool permissions, MCP security) catch them.
How we redesigned futureagi.com: a starship for AI in production
Engineering deep-dive on the redesign of the Future AGI website, including the starship metaphor and hyperspace footer.
Viability Score
How likely is Future AGI to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- Scenario-based testing with synthetic personas
- Agent IDE for iterative refinement and debugging
- Automated evaluations: factuality, relevance, safety, completeness
- Field-level eval attribution to identify breaking inputs
- Automatic prompt optimization (textual gradients, genetic search, meta-prompts)
- DSPy optimizers: BootstrapFewShot, MIPROv2, COPRO, GEPA
- Multimodal LLM-as-a-Judge for images and audio
- Falcon AI copilot for operating eval stack
- Real-time tracing, dashboards, and alerting
- Agent runtime guardrails with tool-call scanners
- Scoring spans, traces, sessions with any eval
- Dead Air Detection and Conversation Hallucination evals
- Eval inputs up to 200K characters
- Voice simulation for phone agents
- Self-hosted deployment option
About Future AGI
Future AGI is a platform for testing, evaluating, and optimizing AI agents in production. It combines simulation environments for scenario-based testing with synthetic personas, an agent IDE for iterative debugging, automated evaluations (factuality, relevance, safety, completeness), production monitoring with real-time tracing and dashboards, and an optimization loop using techniques like textual gradients and DSPy optimizers. Key features include field-level eval attribution, multimodal LLM-as-a-Judge for images and audio, and Falcon AI copilot for operating the eval stack. Recent additions: scoring spans/traces/sessions with any eval, Dead Air Detection, Conversation Hallucination evals, eval inputs up to 200K characters, voice simulation for phone agents, and a new built-in eval for customer agent task completion. Future AGI stands out for its depth in hallucination detection and compliance testing, making it ideal for regulated industries compared to general-purpose observability tools like LangSmith.
Behind the Verdict
Future AGI shines for teams that need to simulate complex, regulated conversations—think debt collection, healthcare, or finance. The scenario builder with synthetic personas lets you stress-test edge cases (suicide threats, hostile callers) without putting real users at risk. The automatic prompt optimization and DSPy evaluators save hours of manual tuning. Where it bites: the platform requires coding; there's no visual agent builder. If you're building a simple FAQ bot, LangSmith's lighter tracing might be overkill but easier to set up. In practice, the free tier (50GB traces, 2K eval credits, etc.) is generous enough to evaluate viability before committing. The recent addition of customer_agent_task_completion eval and regex PII scanning shows the team is focused on production compliance.
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Real-world workflow fit
Concrete scenarios for the personas Future AGI actually fits — and what changes day-one when you adopt it.
You need to test a debt collection agent's behavior across hostile caller scenarios.
Outcome: You create synthetic personas and global rules for suicide/hostile escalation. Running simulations reveals compliance gaps, and you fix prompts before production.
You want to monitor agent performance in production and catch regressions after each deploy.
Outcome: You set up real-time tracing dashboards and alert monitors. New built-in eval customer_agent_task_completion scores each conversation. A CI/CD gate catches a regression before it reaches users.
You need to evaluate retrieval quality and LLM response completeness without ground truth.
Outcome: You use LLM-as-judge with 200K char inputs, compare runs across prompt versions, and apply DSPy optimizers to boost factuality scores by 24%.
Use Cases
- Continuously evaluate and improve a customer support agent's factuality and completeness.
- Simulate hundreds of debt collection call scenarios to guard against hostile or suicidal user prompts.
- Monitor production RAG pipelines with trace-level insight into retrieval quality and LLM response.
- Gate CI/CD deployments with automated LLM evaluation runs to catch regressions before release.
- Optimize voice agent latency by instrumenting and tracing each stage from STT to TTS.
- Red-team LLM agents by injecting adversarial scenarios and scoring safety responses.
Models Under the Hood
as of 2026-07-06
Limitations
- Self-host option requires Docker and some infrastructure know-how.
- Free tier has caps (50GB tracing, 2K eval credits) that may bind heavy users.
- Voice evaluation currently focuses on LiveKit/Retell/Vapi/Pipecat, with narrower support for other telephony stacks.
- No native SOC2 certification, though self-host can address some compliance needs.
as of 2026-07-01
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Future AGI tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0/mo
Ideal for
Small teams or solo developers exploring AI agent evaluation with low volume. Generous caps: 50GB tracing, 2K eval credits, 100K gateway requests, 1M text simulation tokens, 60 min voice simulation.
What this tier adds
Starting tier with no credit card required. Includes 15 built-in guardrails, unlimited team members, community support, and 30-day data retention.
Pay-as-you-go
Usage-based
Ideal for
Growing teams that outgrow free caps. Pay only for what you use with volume discounts. Suitable for startups to mid-market.
What this tier adds
Usage-based pricing after free tier: $2/GB tracing, $10/1K eval credits, $5/100K gateway requests. Includes email support and unlimited team members.
Where the pricing makes sense
The company stage and team size where Future AGI's pricing actually pencils out — and where peers do it cheaper.
Future AGI's free tier is generous for small teams (50GB tracing, 2K eval credits). Mid-sized teams will likely stay within free limits or pay modest usage fees. Compared to LangSmith's usage-based pricing, Future AGI offers a more transparent cost structure with volume discounts. Startups with limited budgets benefit from the free tier; enterprises pay for scale.
Setup time & first value
How long it actually takes to get something useful out of Future AGI — broken out by persona, not the marketing-page minute.
Sign up with no credit card; the agent IDE and evaluation pipeline are accessible within minutes. For voice simulations, integrate telephony (LiveKit, Retell, etc.) in a few hours. Self-hosted deployment requires Docker setup, typically a day for a devops engineer. Teams can see first evaluation results in under an hour.
Switching to or from Future AGI
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From LangSmith: Export traces via API or SDK; import into Future AGI's tracing pipeline. Re-run evaluations using compatible eval templates.
- ↗To LangSmith: Export traces via Future AGI's SDK; import via LangSmith's API. Requires mapping eval types manually.
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Resources & Guides
Tutorials & Learning
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