instruct-eval vs future-agi

Common questions

What is the difference between instruct-eval and future-agi?

instruct-eval: Quantitative evaluation for instruction-tuned language models. future-agi: Open-source, end-to-end platform for evaluating, observing, and improving LLM and AI agent applications. See the comparison table for live GitHub stats and shared categories.

When should I choose instruct-eval over future-agi?

Choose instruct-eval over future-agi when Requirements: Min 8 GB RAM; Requires Python environment setup and specific dependencies as outlined in the repository's documentation.; Tags unique to instruct-eval: benchmarking, evaluation, instruct-tuning, llm; When you need to quantitatively evaluate the performance of instruction-tuned large language models such as Alpaca and Flan-T5 on held-out tasks.

When should I choose future-agi over instruct-eval?

Choose future-agi over instruct-eval when Pricing: The core platform is free and open-source under the Apache License 2.0. However, for managed services or additional support, contact sales for pricing.; Requirements: Min 4 GB RAM; Requires Docker; Future-AGI requires Docker for deployment, with support for Docker Compose and upcoming Kubernetes and Helm support.; For production environments, a setup script is provided to generate secrets and pin image tags, ensuring a secure and reproducible deployment.; Tags unique to future-agi: ai-agents, ai-evals, ai-gateway, ai-optimization; Also covers AI Agents, LLM Frameworks; future-agi ships Docker support for self-hosted deployment; You need a self-hostable solution that does not phone home, ensuring full control over your data and evaluation logic.

When should I avoid instruct-eval?

When primarily interested in general model evaluation without a focus on instruction-tuned LMs. If your primary interest lies in qualitative assessment rather than quantitative metrics. If you need support for non-HuggingFace Transformer models, as instruct-eval mainly supports models from the HuggingFace ecosystem.

When should I avoid future-agi?

You require immediate Kubernetes or Helm support, as these are not yet available, though they are in development. You are seeking a managed service or a solution available through AWS Marketplace, as these options are not yet available, though they are planned for the future. Your project is in a highly dynamic environment where frequent updates and vendor support are critical, as Future-AGI is a community-driven project with a focus on self-hosting and open-source.

Is instruct-eval or future-agi more popular on GitHub?

future-agi has more GitHub stars (2,032 vs 553). Stars measure visibility, not whether either tool fits your constraints.

Are instruct-eval and future-agi open source?

Yes - both are open-source projects on GitHub (instruct-eval: Apache-2.0, future-agi: Apache-2.0).

Where can I find alternatives to instruct-eval or future-agi?

GraphCanon lists graph-backed alternatives at instruct-eval alternatives and future-agi alternatives (instruct-eval markdown twin, future-agi markdown twin), ranked by typed relationship edges rather than popularity votes.

Is there a machine-readable version of this comparison?

Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

Which is better maintained, instruct-eval or future-agi?

instruct-eval: Dormant. future-agi: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.

Where are the full trust reports for instruct-eval and future-agi?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: instruct-eval trust report; future-agi trust report.