Comparison
instruct-eval vs futureagi-sdk
Verdict
Pick instruct-eval if key facts about instruct-eval; pick futureagi-sdk if future AGI SDK is an innovative toolkit designed for production-grade AI evaluation, prompt management, and observability. It supports Python and TypeScript languages and is licensed under Apache-2.0.
Markdown twin · instruct-eval alternatives · futureagi-sdk alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | instruct-eval | futureagi-sdk |
|---|---|---|
| Maintenance | Dormant (879d since push) As of 2w · github_public_v1 | Active (25d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Organization account As of 3w · github_public_v1 |
| OSV dependency advisories | Published findings As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- instruct-eval
- Quantitative evaluation for instruction-tuned language models
- futureagi-sdk
- Production-grade AI evaluation, prompt management & observability SDK
Stars
- instruct-eval
- 552
- futureagi-sdk
- 48
Forks
- instruct-eval
- 45
- futureagi-sdk
- 5
Open issues
- instruct-eval
- 24
- futureagi-sdk
- 3
Language
- instruct-eval
- Python
- futureagi-sdk
- Python
Adopt for
- instruct-eval
- Key facts about instruct-eval
- futureagi-sdk
- Future AGI SDK is an innovative toolkit designed for production-grade AI evaluation, prompt management, and observability. It supports Python and TypeScript languages and is licensed under Apache-2.0.
Persona
- instruct-eval
- -
- futureagi-sdk
- -
Runtime
- instruct-eval
- -
- futureagi-sdk
- -
License
- instruct-eval
- The tool is distributed under Apache-2.0 license
- futureagi-sdk
- The Future AGI SDK uses the Apache License, Version 2.0 (Apache-2.0). It allows users to freely use, modify, and distribute the software while maintaining copyright notices.
Last pushed
- instruct-eval
- Mar 10, 2024
- futureagi-sdk
- Jul 8, 2026
Categories
- instruct-eval
- Evaluation & Observability
- futureagi-sdk
- Evaluation & Observability
Trust and health
Maintenance
- instruct-eval
- Dormant (18%)
- futureagi-sdk
- Active (82%)
Days since push
- instruct-eval
- 879d
- futureagi-sdk
- 25d
Open issues (now)
- instruct-eval
- 24
- futureagi-sdk
- 3
OSV dependency advisories
- instruct-eval
- Published findings
- futureagi-sdk
- No lockfile (source not queried)
Full report
- instruct-eval
- Trust report
- futureagi-sdk
- Trust report
Choose instruct-eval if…
- 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, instruct-tuning, llm, safety.
- 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 NOT to use 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.
Choose futureagi-sdk if…
- Requirements: Supports Python and TypeScript languages; Automated evaluations with sub-100ms guardrails.
- Tags unique to futureagi-sdk: ai-agents, annotations, dataset, development.
- Future AGI SDK is an innovative toolkit designed for production-grade AI evaluation, prompt management, and observability. It supports Python and TypeScript languages and is licensed under Apache-2.0.
When NOT to use futureagi-sdk
- Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (declare-lab/instruct-eval) · observed Aug 7, 2026
- GitHub forks (declare-lab/instruct-eval) · observed Aug 7, 2026
- Last push (declare-lab/instruct-eval) · observed Mar 10, 2024
- License file (Apache-2.0) · observed Aug 7, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (future-agi/futureagi-sdk) · observed Aug 2, 2026
- GitHub forks (future-agi/futureagi-sdk) · observed Aug 2, 2026
- Last push (future-agi/futureagi-sdk) · observed Jul 8, 2026
- License file (Apache-2.0) · observed Aug 2, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: instruct-eval 552 · futureagi-sdk 48 (synced Aug 7, 2026).
Common questions
- What is the difference between instruct-eval and futureagi-sdk?
- instruct-eval: Quantitative evaluation for instruction-tuned language models. futureagi-sdk: Production-grade AI evaluation, prompt management & observability SDK. See the comparison table for live GitHub stats and shared categories.
- When should I choose instruct-eval over futureagi-sdk?
- Choose instruct-eval over futureagi-sdk 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, instruct-tuning, llm, safety; 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 futureagi-sdk over instruct-eval?
- Choose futureagi-sdk over instruct-eval when Requirements: Supports Python and TypeScript languages; Automated evaluations with sub-100ms guardrails; Tags unique to futureagi-sdk: ai-agents, annotations, dataset, development; Future AGI SDK is an innovative toolkit designed for production-grade AI evaluation, prompt management, and observability. It supports Python and TypeScript languages and is licensed under Apache-2.0.
- 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 futureagi-sdk?
- Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers.
- Is instruct-eval or futureagi-sdk more popular on GitHub?
- instruct-eval has more GitHub stars (552 vs 48). Stars measure visibility, not whether either tool fits your constraints.
- Are instruct-eval and futureagi-sdk open source?
- Yes - both are open-source projects on GitHub (instruct-eval: Apache-2.0, futureagi-sdk: Apache-2.0).
- Where can I find alternatives to instruct-eval or futureagi-sdk?
- GraphCanon lists graph-backed alternatives at instruct-eval alternatives and futureagi-sdk alternatives (instruct-eval markdown twin, futureagi-sdk 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 futureagi-sdk?
- instruct-eval: Dormant. futureagi-sdk: 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 futureagi-sdk?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: instruct-eval trust report; futureagi-sdk trust report.