Future AGI vs Helicone in 2026: Self-Improving Runtime vs Lightweight Observability

If you are deciding between Future AGI and Helicone today, the short answer is this. Pick Future AGI when you want the runtime to close the loop, trace to eval to optimizer to route, with continuous-eval scoring and inline guardrails, so the system keeps improving instead of staying a passive log. Pick Helicone when you want a lightweight, MIT-licensed observability proxy with one-line setup, simple caching, and a generous free tier, and you accept that the product entered maintenance mode after Mintlify acquired Helicone on March 3, 2026.

Six axes, honest scoring, both acquisitions on the table, what each falls short on as of May 2026. Future AGI ranks first on five of the six axes. Helicone wins one cleanly and we name it.

TL;DR: capability snapshot

One-line verdict: Future AGI is the only product in this comparison where the gateway updates itself from outcomes. Helicone is the lowest-friction per-request observability proxy in the category, with the caveat that the Mintlify acquisition has paused new feature work.

What each product actually is

Future AGI is a self-improving runtime for LLM agents. The Agent Command Center is the hosted control plane. The building blocks are three Apache 2.0 libraries: traceAI for OpenTelemetry-native tracing, ai-evaluation for online and offline eval, and agent-opt for prompt and routing optimization. The wedge is the loop. Every trace gets scored. Low-scoring sessions cluster into failure modes. The optimizer rewrites prompts or routing policies. The runtime applies the update on the next request. Auto-rollback fires if scores regress. ProTeGi, Bayesian, and GEPA optimizers are available. Protect, the inline guardrail, runs at approximately 67 ms p50 for text and 109 ms p50 for image (arXiv 2510.13351). BYOC and AWS Marketplace are live. SOC 2 Type II, HIPAA (BAA), GDPR, and CCPA are all certified.

Helicone is a lightweight LLM observability proxy. Drop the Helicone base URL in front of your OpenAI or Anthropic client, and every request gets logged with prompt, response, cost, latency, and any custom properties you attach. The product won DX praise through 2024 and 2025 because the setup was one line and the free tier was generous. The core is MIT-licensed and self-hostable via Helm in a few containers (worker + Clickhouse + Postgres). On March 3, 2026, Mintlify acquired Helicone, folding the product into Mintlify’s developer-docs platform. The proxy and SDKs continue to operate and security patches ship, but feature development has slowed as engineering moves to Mintlify integrations.

Helicone gives you a per-request log. Future AGI gives you a per-request log wired to an eval system and an optimizer that closes the loop.

Head-to-head on the six axes

1. Instrumentation and setup

Helicone’s pitch is “change one URL, get observability.” Swap api.openai.com for oai.helicone.ai and every call flows through the proxy with full logging. The async SDK keeps direct provider calls and ships logs in the background. Total time to first useful log: under five minutes. For a solo developer who has never instrumented anything, this is the lowest-friction entry point in the category. Custom properties via Helicone-Property-* headers give per-request slicing without code changes.

Future AGI’s traceAI is OpenTelemetry-native. Setup is a pip install and a configuration object, still under fifteen minutes. Because spans are OTel format, they route to your existing OTel sink (Datadog, Honeycomb, Grafana, Splunk) in parallel with the Future AGI dashboard, and every tool call gets a child span automatically. For agent workloads, the structure is correct by default. Helicone’s flat-log model treats every request as independent, which forces you to encode agent structure in custom properties.

Verdict. Helicone wins on raw setup speed for a non-agent app. Future AGI wins on instrumentation correctness for any agent workload.

2. Observability depth

Helicone shows a per-request log with prompt, response, cost, latency, user ID, and any custom properties. The UI is clean and the query layer is fast for typical filter-and-sort use cases. For single-call sessions, this is enough. What Helicone doesn’t natively model is the shape of an agent run: a parent span with tool calls, retries, sub-agent invocations, and a final answer. Custom properties can encode some of that, but the dashboard doesn’t treat it as a first-class object.

Future AGI’s observability is agent-aware out of the box. Every tool call gets a child span, every model call attaches input, output, model, and eval score as span attributes. Sub-agents and retries appear as a parent-child tree so you can find the exact tool call that caused a failure rather than scrolling through a flat log. The dashboard shows the full trace tree and joins eval results against it.

Verdict. Future AGI wins on observability depth. The gap is widest for agent workloads. For single-shot prompt apps, Helicone’s flat log is enough.

3. Evaluation

Helicone added an LLM-as-judge eval add-on in late 2024 that scores logged requests with a model and a rubric. It works for spot-checking and small-scale quality dashboards. What it doesn’t do is run continuous eval across thousands of sessions with multiple rubrics joined into a quality score, then surface clusters of low-scoring sessions for triage. The add-on is a feature, not a system. Post-Mintlify, the eval surface is in maintenance posture along with the rest of the product.

Future AGI’s ai-evaluation is a continuous eval platform, not a single scorer. Five concrete pillars: (1) a built-in rubric catalog with 50+ pre-built evaluators covering task completion, faithfulness, tool-use, structured-output, agentic surfaces, hallucination, groundedness, context relevance, and instruction-following, and the catalog isn’t the ceiling; (2) unlimited custom evaluators, with an in-product eval-authoring agent that uses tool calling on your code to generate, refine, and tune custom rubrics end-to-end; (3) self-improving evaluators. Every rubric learns from live production traces and gets sharper as traffic flows; (4) FAGI’s in-house classifier model family runs continuous evaluation at very low cost-per-token (Galileo Luna-2 parity on cost economics) and scores any rubric, built-in or custom, not a fixed catalog; (5) the library itself is Apache 2.0. Pip install, no vendor lock, runs anywhere. Eval results then feed agent-opt, which rewrites prompts that the gateway re-deploys. Eval isn’t stranded on a dashboard.

Verdict. Future AGI wins on evaluation decisively. Helicone’s eval is an LLM-as-judge add-on for spot-checking. Future AGI’s eval is a continuous platform (50+ built-in rubrics, unlimited custom evaluators authored by an in-product agent, self-improving from live traces, in-house classifier models at scale, Apache 2.0) wired into the optimizer loop. If continuous quality monitoring matters, this axis alone is dispositive.

4. Optimization and the closed loop

This is the cleanest gap in the comparison. Helicone has no optimizer. Traces inform humans; humans rewrite prompts and update routes. The product was never built to close the loop, and the maintenance-mode status after the Mintlify acquisition means it won’t be built going forward.

Future AGI’s agent-opt library implements ProTeGi for prompt optimization, Bayesian search for hyperparameters, and GEPA for routing policies. Low-scoring session clusters become the training signal. The optimizer proposes a new prompt or routing rule, the runtime applies it on the next request, and the eval system watches for regression. If scores drop, the change auto-rolls back. Human review is available as a gate for high-stakes prompts.

Verdict. Future AGI wins on optimization because Helicone doesn’t enter the axis. If you want a system that updates its own behavior from outcomes, Helicone is the wrong shape of product.

5. Caching, runtime features, and guardrails

Helicone ships a semantic cache and a key vault. The cache is useful for prompt-heavy workloads with repeat queries. The key vault keeps provider keys off the application server. Helicone doesn’t ship a runtime guardrail product, a routing policy engine, or a virtual-key system. The runtime is intentionally thin. Helicone has always been observability-first, with caching and key management as additions.

Future AGI’s runtime is broader. The Future AGI Protect model family enforces inline at approximately 67 ms p50 for text and 109 ms p50 for image (arXiv 2510.13351). That’s FAGI’s own fine-tuned Gemma 3n stack with specialized adapters across four safety dimensions (content moderation, bias detection, security/prompt-injection, data privacy/PII), natively multi-modal across text, image, and audio. Error Feed runs alongside as the zero-config error monitor (auto-clustering related traces into named issues with auto-analysis). Routing policies are declarative and updated by agent-opt. Caching is available through Agent Command Center. RBAC, audit logs, and certified compliance posture (SOC 2 Type II, HIPAA, GDPR, CCPA) are first-class.

Verdict. Helicone wins on simplicity. If all you want is “log requests, cache repeats, store keys safely,” it’s the cleanest answer. Future AGI wins on runtime depth. Guardrails, routing, RBAC, audit logs, and the optimization loop are all in the same product.

6. Developer experience

Helicone’s DX is its longest-running strength. The docs are short and accurate, the SDKs are minimal, and the integration story is honest about its scope. Solo developers who tried it on a Friday kept using it on Monday. Post-acquisition documentation has been clear about which features are stable and which are in maintenance, unusual transparency in a category that often hides slowdowns.

Future AGI’s DX is broad and consistent. SDKs are clean and OpenAI-compatible. The traceAI library has a low-friction local-dev story across 35+ framework integrations. The eval and optimizer UIs are strong. The cost is breadth by design: a new developer touches four moving parts (trace, eval, optimize, route) plus Error Feed and Protect, instead of one. For teams that only want logging, that breadth is overkill. For teams that want the full loop, the integrated UI beats gluing three separate products.

Verdict. Helicone wins on minimal-surface DX. Future AGI wins on integrated-surface DX. The tie-breaker is how many of the four parts you actually want.

Pricing snapshot

Pulled from each vendor’s pricing page on May 17, 2026.

Helicone’s $20/mo Pro tier reflects the lightweight scope. Future AGI’s $99/mo Scale tier includes the eval suite and optimizer, which Helicone doesn’t offer at any tier. Both negotiate at enterprise. For continuous production workloads, Future AGI’s optimizer typically delivers 15-30% cost reduction within four weeks of trace data flowing, with no change to developer behavior required. agent-opt is opt-in: turn it on once you have eval baselines and live traces; until then, traceAI + ai-evaluation carry the daily value.

Where each one falls short

Future AGI: three deliberate tradeoffs

• First-call setup is a pip install, not a URL swap. Helicone is one line. Future AGI is a pip install, a configuration object, and rubric choices. Still under a day, but not five minutes. That first-day cost buys agent-aware spans, eval scores joined to every trace, and the optimizer surface that Helicone doesn’t ship at all.
• agent-opt is opt-in and learns from live traces. Start with traceAI plus ai-evaluation on day one, and turn the optimizer on once eval baselines stabilize and production traffic is flowing. The optimizer gets stronger as your trace data accumulates. That’s the design, not a setup tax.
• Federal procurement runs through BYOC. FedRAMP authorization is on the partner roadmap. Today, federal SOC procurement is supported via air-gapped self-host in the agency VPC. Agencies on a current FedRAMP-required calendar should plan around the BYOC path.

Three deliberate tradeoffs in pursuit of the closed loop. Every one has a clear path or workaround for buyers who need it today.

Helicone: four honest limitations

• Maintenance mode after Mintlify acquisition. The March 3, 2026 deal moved Helicone into Mintlify’s developer-docs platform. The product runs and security patches ship, but new feature development has slowed visibly. If you’re picking expecting a roadmap, this is the central tradeoff. For a one-year horizon, fine. For a three-year horizon, a real risk.
• No optimizer, no continuous eval system. The LLM-as-judge add-on is spot-checking, not continuous eval. If you want the system to update its own behavior from outcomes, Helicone is the wrong shape of product.
• Agent workloads are second-class. The dashboard treats requests as a flat list. Tool calls, retries, and sub-agents don’t get first-class span semantics.
• No routing or fallbacks. Helicone is a per-request proxy, not a gateway. If OpenAI 503s, your request fails. Teams that need routing pair Helicone with LiteLLM upstream, or migrate.

Decision framework: choose X if

Choose Future AGI if you need:

• A runtime that closes the loop: trace, eval, optimize, route, all in one product.
• Continuous eval scoring task completion, faithfulness, and tool-use accuracy across every session.
• OpenTelemetry-native instrumentation under Apache 2.0 so your security team can read, fork, and self-instrument.
• RBAC, audit logs, BYOC, AWS Marketplace, and inline runtime guardrails.

Choose Helicone if you need:

• The fastest, cheapest path to per-request logging for a non-agent app, and you accept the maintenance-mode status.
• A free tier that is genuinely generous for solo developers and small teams.
• A semantic cache and a key vault without configuring a broader runtime.

Look at LiteLLM, Portkey, or Kong AI Gateway if you need:

• A self-hosted Python proxy with no SaaS dependency (LiteLLM, with March 2026 PyPI compromise caveats).
• A polished hosted gateway with virtual keys and a mature prompt library (Portkey, now part of Palo Alto Networks).
• An existing Kong stack extended with AI-specific policies (Kong AI Gateway).

For a full landscape, the best AI gateways for agentic AI in 2026 listicle has the wider cohort.

When to look elsewhere

If the situation is one of these, neither Future AGI nor Helicone is the right pick:

• Air-gapped, source-readable, no SaaS at all. LiteLLM’s OSS proxy is the cleanest fit. Helicone’s MIT self-host is plausible but the maintenance-mode status weakens the long-term answer.
• Hosted gateway with mature virtual keys and a polished prompt library. Portkey is the right pick on those axes, with the caveat that the Palo Alto Networks acquisition reshaped its roadmap on April 30, 2026.
• Existing Kong stack for REST APIs. Kong AI Gateway extends what your platform team already runs.

How the loop changes the math

What doesn’t fit cleanly into the six axes is what happens over time. Helicone is a static observation layer. The system gets better only when humans update it, and after Mintlify the rate of vendor-side updating is also slower. Future AGI is a self-improving runtime that updates itself based on eval signal.

The loop in practice. traceAI emits a span tree, ai-evaluation scores each turn against rubrics from a 50+ built-in catalog plus any custom evaluator your team authors (generated and tuned by an in-product eval-authoring agent that reads your code), every rubric self-improves from live production traces, and FAGI’s in-house classifier models score continuously at very low cost-per-token (Galileo Luna-2 parity on cost economics). Low-scoring sessions cluster by failure mode, agent-opt rewrites the system prompt or routing policy, the Agent Command Center applies it on the next request, and the new version auto-rolls back if the score regresses. ProTeGi for prompts, Bayesian for hyperparameters, GEPA for routing policy.

Net effect for continuous production workloads: typical cost reduction of 15-30% within four weeks of live trace data flowing, with no change to developer behavior. The router picks the cheaper model for easy turns, the optimizer rewrites over-prompting prompts, and the eval data tells the loop where to focus.

This is the loop Helicone doesn’t implement, and after the acquisition, won’t implement. Every Future AGI surface ships against concrete features. traceAI is OpenTelemetry-native with 35+ framework integrations, OpenInference-compat, and Apache 2.0 source. ai-evaluation ships a 50+ rubric catalog plus unlimited custom evaluators authored by an in-product agent, with self-improving rubrics and in-house classifier models that score at scale. Error Feed auto-clusters and auto-analyzes agent errors with zero config (the “Sentry for AI agents” surface Helicone never built). agent-opt runs ProTeGi, Bayesian, and GEPA optimizers against live trace data. The Future AGI Protect model family enforces inline at ~67 ms p50 text and ~109 ms p50 image across four safety dimensions on its own Gemma 3n + fine-tuned adapter stack. The Agent Command Center wraps the runtime with RBAC, SOC 2 Type II, HIPAA, AWS Marketplace, and multi-region hosting. Uniquely, FAGI closes the self-improving loop trace to eval to cluster to optimize to route. For a weekend project where five-minute setup wins, Helicone is the right pick.

Where Helicone fits in a Future-AGI stack

A pragmatic note for teams already on Helicone: the pieces compose. traceAI is OpenTelemetry-native and gateway-agnostic. It runs alongside Helicone without migration. The cleanest 2026 path for teams that want to keep Helicone’s lightweight observability while adding the closed loop is to layer traceAI for OTel-native spans (which Helicone doesn’t produce), add ai-evaluation against captured traces, and graduate to agent-opt for routing-policy optimization when the failure dataset is large enough.

Or, if the maintenance-mode posture is a multi-year concern, replace Helicone with traceAI directly. The migration is “swap one library for another” rather than a full re-architecture, since the OSS source-readable Apache 2.0 path is available without procurement.

Related reading

• Best AI Gateways for Agentic AI in 2026
• Best 5 AI Gateways to Monitor Claude Code Token Usage in 2026
• What Is an AI Gateway? The 2026 Definition
• Future AGI vs Portkey in 2026
• Future AGI vs LiteLLM in 2026
• Future AGI vs Kong AI Gateway in 2026

Sources

• Future AGI Agent Command Center, futureagi.com/platform
• Future AGI Protect latency benchmark, arxiv.org/abs/2510.13351
• traceAI (Apache 2.0), github.com/future-agi/traceAI
• ai-evaluation (Apache 2.0), github.com/future-agi/ai-evaluation
• agent-opt (Apache 2.0), github.com/future-agi/agent-opt
• AWS Marketplace listing for Future AGI, aws.amazon.com/marketplace
• Helicone product and pricing, helicone.ai
• Helicone open-source repo, github.com/Helicone/helicone (MIT)
• Mintlify press release on Helicone acquisition, mintlify.com/blog (March 3, 2026)