Future AGI: Open-Source LLM Evaluation and Observability Platform

Most teams building with LLMs bolt on observability as an afterthought, then scramble when outputs go wrong in production. Future AGI bundles the core pieces, tracing every call, running structured evaluations, simulating user scenarios, managing datasets, routing through a model gateway, and enforcing guardrails, into one self-hosted package under Apache 2.0. That means no vendor lock-in and no data leaving your infrastructure.

What makes this different from hosted alternatives like LangSmith or Arize is the self-hostable angle. Regulated industries, companies with strict data residency requirements, or teams that simply do not want to pay per-trace fees have a credible open-source option here. The tradeoff is that someone on your team needs to deploy and maintain it.

For an operator who has already shipped an AI feature and is now asking why it sometimes gives bad answers, this platform gives you the audit trail and the testing harness to find and fix the problem systematically. It is not a no-code tool, but the concepts it covers, logging, scoring, red-teaming, are things any product or ops lead can understand and direct.

How teams can use it

Product manager

What for: Track whether a customer-facing AI feature is giving accurate, on-brand answers over time

Outcome: A live dashboard showing pass and fail rates for key output criteria, so regressions are caught before users report them

Build it in 5 steps:

1. Work with a developer to deploy the platform on your company server or cloud account.
2. Define three to five criteria for a good answer, for example factually correct, no competitor mentions, under 100 words.
3. Connect your LLM app to the tracing module so every production call is logged automatically.
4. Set up an eval that scores each logged response against your criteria using the built-in scoring tools.
5. Review the eval dashboard weekly and flag any criteria where the pass rate drops below your threshold.

Where it gets complex: Initial deployment and connecting the tracing SDK to your existing app requires a developer.

Customer support lead

What for: Test a support chatbot against a library of real past tickets before pushing a prompt change to production

Outcome: Confidence that a new prompt version handles edge cases correctly, with a side-by-side comparison against the previous version

Build it in 5 steps:

1. Export a sample of 50 to 100 past support tickets with known correct resolutions into a CSV.
2. Upload that CSV as a dataset inside the platform.
3. Run the current prompt version against the dataset and record the baseline scores.
4. Update the prompt and run the same dataset again.
5. Compare the two score reports and only promote the new prompt if it matches or beats the baseline.

Where it gets complex: Connecting the dataset runner to your live chatbot environment may need a developer for the API wiring.

Compliance officer

What for: Enforce guardrails on an internal AI assistant to block outputs that contain sensitive data categories or policy-violating language

Outcome: Every response from the assistant is checked against defined rules before it reaches the user, with violations logged for audit

Build it in 5 steps:

1. List the categories of content that must never appear in outputs, for example personal account numbers, competitor names, or legal advice.
2. Work with a developer to configure those categories as guardrail rules in the platform.
3. Route all assistant traffic through the platform gateway so guardrails run on every call.
4. Review the violations log weekly to spot patterns and tighten rules as needed.
5. Export the log monthly as evidence for internal audit or regulatory review.

Where it gets complex: Writing precise guardrail rule logic for nuanced compliance categories may need a legal and technical review together.

Operations lead

What for: Simulate how an AI agent handles unusual or adversarial user inputs before it goes live in a new workflow

Outcome: A documented set of failure modes identified in simulation, with fixes applied before any real user is affected

Build it in 5 steps:

1. List the ten to twenty trickiest or most unusual inputs your users might send, based on past experience.
2. Enter those as a simulation scenario set in the platform.
3. Run the simulation against your current agent and review which inputs caused wrong or unsafe outputs.
4. Share the failure report with the team responsible for the agent prompt or logic.
5. Re-run the simulation after fixes are applied to confirm the failure rate dropped.

Where it gets complex: Complex multi-step agent simulations with tool calls or external API dependencies need developer setup.

One caution

Self-hosting means your team owns deployment, upgrades, and infrastructure. There is no managed cloud tier, so a developer or DevOps resource is required to get it running.