— AI agent evaluation and simulation platform to catch hallucinations and optimize performance. Best for AI engineers building production agents needing evaluation before deployment, Teams developing customer-facing chatbots, support bots, or voice agents, Developers in regulated industries (healthcare, collections) requiring compliance testing. Free to use.
AI engineers building production agents needing evaluation before deploymentTeams developing customer-facing chatbots, support bots, or voice agentsDevelopers in regulated industries (healthcare, collections) requiring compliance testingStartups and enterprises wanting open-source, self-hostable agent evaluation platform
Not ideal for
Teams looking for a simple LLM playground without evaluation workflowsNon-technical users who prefer no-code agent building without IDEProjects that only need basic observability without simulation testingOrganizations requiring extensive third-party integrations out-of-the-box
If you're tired of black-box agent testing, Future AGI's simulation-first approach is a game-changer. It's still early-stage (986 GitHub stars), but the focus on catching hallucinations and iterative improvement is exactly what production agent teams need.
Future AGI stands out by putting simulations at the center of agent development. While other tools focus on logging and tracing, Future AGI lets you create scenarios with different personas and edge cases, then run your agent through them to catch failures before deployment. The example of a debt collection agent shows how you can test for suicide threats, hostility, and compliance adherence—critical for production safety. The Agent IDE allows direct editing of prompts and tools, with immediate evaluation runs to see score improvements (e.g., 67% to 91% after adding a retrieval step). However, the platform is relatively new (986 GitHub stars, Apache 2.0 license), so integration ecosystem and community support are still maturing. Pricing isn't disclosed on the page, but there's a free tier to get started. For teams building customer-facing agents, especially in regulated industries, Future AGI's simulation and evaluation focus is a strong alternative to general-purpose observability tools. If you already have a robust testing pipeline with platforms like LangSmith or Arize AI, you might find overlapping features, but Future AGI's unified simulation-to-monitoring flow is compelling. Real-world caveats: the debt collection example is very specific—expect to invest time in building your own scenarios and evaluation metrics tailored to your use case. The monitoring section mentions real-time tracing and dashboards, but details on alerting customizations are sparse.
Skip Future AGI if Skip Future AGI if you need a no-code chatbot builder or a simple prompt playground without the depth of observability and evaluation tooling.
Latest from Future AGI
Updated yesterday
Across the latest 8 updates: 1 feature update and 7 news mentions.
How likely is Future AGI to still be operational in 12 months? Based on 6 signals including funding, development activity, and platform risk.
funding runway
80
website health
90
github activity
50
category mortality
70
wrapper dependency
100
hyperscaler overlap
80
About Future AGI
Future AGI is a platform for building, testing, and monitoring AI agents at scale. It helps teams catch hallucinations, evaluate performance, and optimize agents using real-world simulations and synthetic data generation. Ideal for AI developers and product teams building production-ready agents, Future AGI provides a complete lifecycle from simulation to monitoring. Key features include scenario-based simulations, an Agent IDE for iteration, comprehensive evaluation with metrics like factuality and relevance, and real-time monitoring with dashboards and alerting. Compared to alternatives like LangSmith or Weights & Biases, Future AGI focuses on simulation-driven evaluation and self-improving agents, offering an open-source friendly approach.
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Key Features
Scenario-based simulations for agents
Synthetic data generation
Agent IDE for iterating and refining agents
Comprehensive evaluation with factuality, relevance, safety, completeness metrics
Error feed for catching issues
AI optimization to improve with data
Real-time monitoring with tracing, dashboards, alerting
Command center for overview
Self-hosted option (open source Apache 2.0)
LLM cost calculator
Evaluation TCO calculator
Support for phone call simulations
Real-world workflow fit
Concrete scenarios for the personas Future AGI actually fits — and what changes day-one when you adopt it.
AI engineer onboarding a customer support agent
Start the free tier, instrument the agent with traceAI in 10 minutes, run the built-in debt collection simulation with 20 scenarios, and view evaluation scores (factuality 62%, relevance 71%) in the dashboard.
Outcome: Identify that the agent relies on general knowledge instead of retrieval-augmented generation (RAG). Add a KB search step, re-run evaluation, and see the overall score jump to 91%.
ML platform team enforcing CI/CD gates for LLM apps
Integrate Future AGI's eval SDK into GitHub Actions. Run a suite of 50 evaluation checks (heuristic, LLM-as-judge, code evals) on every pull request.
Outcome: Automatically block PRs that introduce regressions (e.g., factuality drop below 80%) and pass only those that score above threshold. Ship with confidence.
Voice AI developer optimizing latency for a LiveKit agent
Instrument the LiveKit pipeline with traceAI, run a simulation with 100 synthetic calls, and view span-level timing for STT, LLM, and TTS stages.
Outcome: Identify the LLM call as the primary bottleneck (2.3s avg). Swap from gpt-4o-mini to a faster model or enable caching via the Command Center, reducing latency by 40%.
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.
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.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Annual total
Free
Over 12 months
Effective monthly
Free
Billed monthly
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/month
Ideal for
Solo developers and small teams exploring LLM observability with low volume: 50GB tracing, 2K eval credits, 100K gateway requests, 1M simulation tokens, 60 min voice simulation per month.
What this tier adds
Starting tier with generous free quotas; no credit card required. Community support, 30-day data retention, unlimited team members and projects.
Pay-as-you-go
Usage-based after free tier
Ideal for
Teams scaling beyond the free tier – pay only for what you use, with volume discounts at scale. Suitable for production workloads with variable usage.
What this tier adds
Usage-based after free tier: storage from $2/GB, AI Credits from $10/1K, gateway from $5/100K requests. Includes email support and all features of the free tier.
Hidden costs & gotchas
What the public pricing page doesn't put in bold. Captured from pricing-page footnotes, contract terms, and recurring complaints.
•Tracing over 50GB/mo: $2/GB up to 500GB, then $1.50/GB.
•Eval AI credits over 2K/mo: $10/1K credits.
•Gateway requests over 100K/mo: $5/100K requests.
•Text simulation tokens over 1M/mo: $2/1M tokens.
•Voice simulation minutes over 60 min/mo: $0.08/min.
•Cache hits over 100K/mo: $1/100K hits.
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 freemium model with generous free tier (50GB tracing, 2K eval credits) is ideal for startups and small teams. At scale, pay-as-you-go rates (e.g., $2/GB for tracing) are competitive with LangSmith's per-seat pricing, especially for high-volume teams. Enterprise teams that self-host pay only for storage and compute, avoiding per-seat costs.
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.
For cloud (free tier): sign up without a credit card, instrument your agent with the traceAI SDK (one .instrument() call), and see your first traces within 10 minutes. For self-hosted (Docker): run `bin/install` from the cloned repo, takes about 30 seconds plus initial pull of pre-built images. Windows users can use the native PowerShell installer. No email server required for the first account.
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.
Migrating in
→From LangSmith: export dataset traces as JSON via LangSmith's API, import into Future AGI via the dataset API or CSV upload.
→From Weights & Biases Prompts: export prompt versions and evaluations as CSV, re-create experiments in Future AGI's Agent IDE.
→From custom tracing (e.g., manual OpenTelemetry): migrate by adding the traceAI library; it auto-captures spans from existing instrumented code.
→From no platform: start with the free tier and the traceAI SDK, no prior setup needed.
Migrating out
↗To LangSmith: export traces and datasets via Future AGI's API, import into LangSmith's dataset format.
↗To a custom observability stack: use the OpenTelemetry-native spans (compatible with any OTLP receiver).
↗To Datadog: forward Future AGI traces via OpenTelemetry exporter to Datadog's OTLP endpoint.
Recent material changes
Pricing, brand, ownership, or deprecation changes worth knowing before you commit. Most-recent first.
•2026-05-11: Self-host in one command (bin/install) with pre-built Docker images, Windows PowerShell installer, and no email server requirement for first account.
•2026-05-11: Prompts now support Jinja2 templates alongside Mustache; conditional logic and filters available.
•2026-05-11: Voice recordings stored locally by default, decoupling evals from provider URL availability.
•2026-05-11: New Request Explorer in AI gateway logs for filtering by model, status, or metadata and exporting results.
Open Source LLM Red Team Frameworks Compared (2026)
OSS red-team for LLMs splits three ways: orchestrators (PyRIT), probe libraries (garak), and benchmark suites (HarmBench, JailbreakBench, AdvBench). Pick one from each family or you
