future agi vs langfuse

What Each Product Actually Is

Future AGI is an evaluation, tracing, simulation, gateway and guardrail platform for LLM apps and agents, fully open source under Apache-2.0 since Q2 2026. Evals come with 50+ metrics and Future AGI's own judge models (TURING_LARGE, TURING_SMALL, TURING_FLASH). Tracing is traceAI, on OpenTelemetry, and Error Feed groups failures and suggests root causes. Prompts are versioned and labelled in a Prompt Workbench, and metric-threshold alerts cover the observability side. It simulates multi-turn text and voice conversations, generates synthetic datasets, and optimizes prompts with methods such as ProTeGi, PromptWizard and GEPA. The Agent Command Center is an OpenAI-compatible gateway to 100+ providers, and Protect scans model inputs and outputs in real time across text, image and audio with 18 built-in scanners and 15 vendor adapters, including Lakera, Presidio and Llama Guard.

Langfuse covers tracing (its SDK is a thin layer over the OpenTelemetry client, and the server accepts OTLP at /api/public/otel), prompt management with versions and labels, evaluation (LLM-as-a-judge with prebuilt templates, code evaluators, user feedback, manual labels, custom pipelines over the API), datasets for experiments, a playground, and threshold alerts to Slack, webhooks or GitHub Actions. The core is MIT, with a few enterprise features in a separately licensed ee folder. Its README says it has been part of ClickHouse since January 2026.

That is the whole contrast in two paragraphs. Future AGI tries to be the entire reliability stack around a model call - test it, simulate it, optimize it, route it, guard it. Langfuse is the record of what happened and the place you iterate on prompts and scores, and it leaves routing and guarding to other tools.

Side by Side

Count the rows where one side says "no" and you see the shape: Future AGI has more features, Langfuse has fewer moving parts. Neither is a flaw. It is a choice about how much of the stack you want from one project.

Where Future AGI Is Stronger

• Breadth in one open-source project. Evals, simulation including voice, prompt optimization, a gateway and guardrails under one Apache-2.0 licence.
• Metrics and judges included. 50+ metrics and its own Turing judge models, so a team can score without first choosing and prompting a judge.
• Guardrails in the request path. Protect blocks, warns, masks or logs model inputs and outputs in real time, inline in the gateway or through an SDK, and vendor adapters let you plug in Lakera, Presidio or Llama Guard.
• Tool-call control at the gateway. A Tool Permissions scanner blocks tool calls and MCP Security inspects MCP calls, for traffic routed through the Agent Command Center.
• Deployment range. Managed SaaS, your VPC on AWS, GCP or Azure, or air-gapped on-prem.
• Cheaper OAuth SSO. OAuth SSO arrives with the $250-a-month Boost add-on; Langfuse Enterprise SSO needs Pro plus a $300 add-on or the $2,499 Enterprise plan. SAML with SCIM is on Future AGI's $750 Scale add-on.

The breadth has a practical upside beyond the feature count: one vendor and one place to look when an evaluation, a simulation and a guardrail disagree about the same conversation. A team that would otherwise assemble Langfuse, a separate gateway, a guardrail library and a simulation tool should count the integration work it would not be doing. Future AGI names its customers only in anonymized case studies, so ask for references in your industry before committing.

Where Langfuse Is Stronger

• A larger community. About 34,600 GitHub stars against roughly 2,000, and ClickHouse behind the project.
• A lighter self-host. Postgres, ClickHouse, Redis or Valkey and object storage, with Helm and Terraform templates available now. Future AGI adds RabbitMQ and Temporal, runs on Docker Compose, and lists Helm as coming soon.
• One simple meter. Units - traces, observations and scores - at $8 per 100,000 over the allowance, against separate meters for storage, AI credits, gateway requests and simulation.
• Alerts on every cloud plan, free included. Threshold alerts on observations and on numeric, categorical or boolean scores, delivered to Slack, a webhook or GitHub Actions; Hobby allows two per organisation, self-hosted v4 and later has no limit.
• Prompts wired into the SDK. Both products version and label prompts; Langfuse's SDK caches them client-side and links each trace to the prompt version that produced it, so scores can be compared by version without extra plumbing.
• No gateway to adopt. Langfuse instruments the app you have. Future AGI's guardrails and tool-call controls work best when traffic flows through its gateway, which is a routing change as well as a tool choice.

Where Each Sits in the Request Path

This is the substantive architectural difference. Future AGI's Protect and Tool Permissions sit in the path of a model call: inline in the Agent Command Center or called from an SDK, they see the prompt, the response and any tool call routed through the gateway, and can stop it. Future AGI documents tool-call blocking for traffic through the gateway; check how your own agent's tools are wired before relying on it for actions that do not pass through it.

Langfuse sits beside the request path. Its security docs are explicit: use "LLM Security libraries for run-time security measures", and Langfuse "for the ex-post evaluation of the effectiveness of these measures". You trace the guardrail, chart its scores and investigate what it missed. That is a clean separation of concerns, and it means blocking is a second tool you choose and run.

One Prompt Injection, Both Platforms

A research agent reads a web page that contains hidden instructions to email the conversation to an outside address.

1. Future AGI: scan, block, trace

   With model calls routed through the Agent Command Center, Protect scanners inspect the input carrying the injected text and can block, warn, mask or log; a vendor adapter such as Lakera can be one of the scanners. If the email tool call passes through the gateway, the Tool Permissions scanner can block it. traceAI records the run, and Error Feed groups it with similar failures.

2. Langfuse: trace, score, alert

   Langfuse records the trace, including the tool call. An LLM-as-a-judge evaluator scores the session for injection, and a score alert fires to Slack or a webhook. Stopping the email takes a separate guardrail library in the request path; Langfuse then traces that guardrail and scores how well it worked.

3. What you have afterwards

   With Future AGI, a blocked call and a trace, provided the traffic went through the gateway. With Langfuse, an excellent record of an email that was sent, unless you added a guardrail of your own.

Pricing and Self-Hosting

A small-team estimate, assumptions stated. Langfuse: 10,000 sessions a month at 1 trace, 20 observations and 2 scores each is 230,000 units, so Core costs $29 + 1.3 × $8 = $39.40 a month, assuming pro rata overage. Future AGI: if those sessions stay under 50 GB of storage and 100K gateway requests, storage and gateway are free; evaluations draw on AI credits at $10 per 1,000 beyond the free 2,000, but the page does not say how many credits one evaluation uses, so the eval bill cannot be computed from public numbers. Future AGI's pricing and enterprise pages also disagree on the SLA and on which features sit in which add-on; get the answer in writing.

Self-hosted, Future AGI is Docker Compose over Postgres, ClickHouse, Redis, RabbitMQ and Temporal, and its self-host telemetry is on by default (FUTURE_AGI_TELEMETRY_DISABLED=1 turns it off). Langfuse is Postgres, ClickHouse, Redis or Valkey and object storage, with Docker Compose for testing and Helm or Terraform for production. Five stateful services against four is not a dramatic gap on paper; Temporal and RabbitMQ are the two a small platform team is least likely to already run.