Comparison
future-agi vs Awesome-LLMSecOps
Verdict
Pick future-agi if future-AGI is an open-source platform for evaluating and observing LLM and AI agent applications, offering features like tracing, evaluations, simulations, and guardrails. It is self-hostable and supports deployment via,; pick Awesome-LLMSecOps if awesome-LLMSecOps is a curated list that emphasizes practical security implementation for the operations of large language models.
Markdown twin · future-agi alternatives · Awesome-LLMSecOps alternatives
GraphCanon updated Sep 20, 2026
vs
Trust & integrity
| Signal | future-agi | Awesome-LLMSecOps |
|---|---|---|
| Maintenance | Very active (0d since push) As of Sep 18, 2026 · github_public_v1 | Active (19d since push) As of Sep 12, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 18, 2026 · github_public_v1 | Not a fork · Personal account As of Sep 12, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Sep 18, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 15, 2026 · 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
- future-agi
- Open-source, end-to-end platform for evaluating, observing, and improving LLM and AI agent applications
- Awesome-LLMSecOps
- Curated security resources for LLM operations
Stars
- future-agi
- 2.0k
- Awesome-LLMSecOps
- 155
Forks
- future-agi
- 627
- Awesome-LLMSecOps
- 76
Open issues
- future-agi
- 961
- Awesome-LLMSecOps
- 20
Language
- future-agi
- Python
- Awesome-LLMSecOps
- HTML
Adopt for
- future-agi
- Future-AGI is an open-source platform for evaluating and observing LLM and AI agent applications, offering features like tracing, evaluations, simulations, and guardrails. It is self-hostable and supports deployment via,
- Awesome-LLMSecOps
- Awesome-LLMSecOps is a curated list that emphasizes practical security implementation for the operations of large language models.
Persona
- future-agi
- -
- Awesome-LLMSecOps
- -
Runtime
- future-agi
- -
- Awesome-LLMSecOps
- -
License
- future-agi
- Apache License 2.0, allowing for free use, modification, and distribution of the software, with the condition that any derivative works also be licensed under the same terms.
- Awesome-LLMSecOps
- -
Last pushed
- future-agi
- Sep 18, 2026
- Awesome-LLMSecOps
- Aug 23, 2026
Categories
- future-agi
- AI Agents, Evaluation & Observability, LLM Frameworks
- Awesome-LLMSecOps
- AI Agents, Evaluation & Observability
Trust and health
Maintenance
- future-agi
- Very active (96%)
- Awesome-LLMSecOps
- Active (82%)
Days since push
- future-agi
- 0d
- Awesome-LLMSecOps
- 19d
Open issues (now)
- future-agi
- 961
- Awesome-LLMSecOps
- 20
Stars delta
- future-agi
- +473 (30d)
- Awesome-LLMSecOps
- +5 (30d)
Open issues delta
- future-agi
- +365 (30d)
- Awesome-LLMSecOps
- +9 (30d)
Owner type
- future-agi
- Organization
- Awesome-LLMSecOps
- User
Full report
- future-agi
- Trust report
- Awesome-LLMSecOps
- Trust report
Choose future-agi if…
- future-agi is primarily Python; Awesome-LLMSecOps is HTML.
- 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 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 NOT to use 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.
Choose Awesome-LLMSecOps if…
- Awesome-LLMSecOps is primarily HTML; future-agi is Python.
- Tags unique to Awesome-LLMSecOps: adversarial-ml-threat-modeling, ai-agents-security, llm-red-teaming, prompt-injection.
- Need a specialized focus on LLM-specific security threats like recursive pollution and prompt manipulation
When NOT to use Awesome-LLMSecOps
- Looking for extensive academic references or ArXiv papers in descriptions
- Require real-time interactive tools rather than curated static lists of resources
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (future-agi/future-agi) · observed Sep 20, 2026
- GitHub forks (future-agi/future-agi) · observed Sep 20, 2026
- Last push (future-agi/future-agi) · observed Sep 18, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
- GitHub stars (wearetyomsmnv/Awesome-LLMSecOps) · observed Sep 20, 2026
- GitHub forks (wearetyomsmnv/Awesome-LLMSecOps) · observed Sep 20, 2026
- Last push (wearetyomsmnv/Awesome-LLMSecOps) · observed Aug 23, 2026
- License file (unknown) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
GitHub stars on cards: future-agi 2.0k · Awesome-LLMSecOps 155 (synced Sep 20, 2026).
Common questions
- What is the difference between future-agi and Awesome-LLMSecOps?
- future-agi: Open-source, end-to-end platform for evaluating, observing, and improving LLM and AI agent applications. Awesome-LLMSecOps: Curated security resources for LLM operations. See the comparison table for live GitHub stats and shared categories.
- When should I choose future-agi over Awesome-LLMSecOps?
- Choose future-agi over Awesome-LLMSecOps when future-agi is primarily Python; Awesome-LLMSecOps is HTML; 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 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 choose Awesome-LLMSecOps over future-agi?
- Choose Awesome-LLMSecOps over future-agi when Awesome-LLMSecOps is primarily HTML; future-agi is Python; Tags unique to Awesome-LLMSecOps: adversarial-ml-threat-modeling, ai-agents-security, llm-red-teaming, prompt-injection; Need a specialized focus on LLM-specific security threats like recursive pollution and prompt manipulation.
- 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.
- When should I avoid Awesome-LLMSecOps?
- Looking for extensive academic references or ArXiv papers in descriptions Require real-time interactive tools rather than curated static lists of resources
- Is future-agi or Awesome-LLMSecOps more popular on GitHub?
- future-agi has more GitHub stars (2,032 vs 155). Stars measure visibility, not whether either tool fits your constraints.
- Are future-agi and Awesome-LLMSecOps open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to future-agi or Awesome-LLMSecOps?
- GraphCanon lists graph-backed alternatives at future-agi alternatives and Awesome-LLMSecOps alternatives (future-agi markdown twin, Awesome-LLMSecOps 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, future-agi or Awesome-LLMSecOps?
- future-agi: Very active. Awesome-LLMSecOps: 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 future-agi and Awesome-LLMSecOps?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: future-agi trust report; Awesome-LLMSecOps trust report.