--- title: "instruct-eval vs ai-berkshire" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/declare-lab-instruct-eval-vs-xbtlin-ai-berkshire" tools: ["declare-lab-instruct-eval", "xbtlin-ai-berkshire"] --- # instruct-eval vs ai-berkshire *GraphCanon updated Jul 12, 2026* ## Verdict Pick instruct-eval when license: instruct-eval is Apache-2.0, ai-berkshire is MIT; pick ai-berkshire when license: ai-berkshire is MIT, instruct-eval is Apache-2.0. [instruct-eval](https://declare-lab.github.io/instruct-eval/) reports 552 GitHub stars, 45 forks, and 24 open issues, last pushed Mar 10, 2024. [ai-berkshire](https://github.com/xbtlin/ai-berkshire#readme) has 13k stars, 1.8k forks, and 17 open issues, last pushed Jul 11, 2026. Figures are from public GitHub metadata via [instruct-eval's repository](https://github.com/declare-lab/instruct-eval) and [ai-berkshire's repository](https://github.com/xbtlin/ai-berkshire). | | [instruct-eval](/tools/declare-lab-instruct-eval.md) | [ai-berkshire](/tools/xbtlin-ai-berkshire.md) | | --- | --- | --- | | Tagline | Code for evaluating instruction-tuned language models like Alpaca and Flan-T5 | AI-era Berkshire: a value investing research framework utilizing Claude Code / Codex with methodologies from Warren Buffett, Charlie Munger among others and multi-Agent adversarial analysis. | | Stars | 552 | 12,711 | | Forks | 45 | 1,803 | | Open issues | 24 | 17 | | Language | Python | Python | | Adopt for | - | ai-berkshire implements a unique approach to value investing research through AI agents powered by Claude Code/Codex, inspired by the methodologies of Warren Buffett and Charlie Munger amongst other investors. The tool's | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Evaluation & Observability | AI Agents, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [instruct-eval](/tools/declare-lab-instruct-eval.md) | [ai-berkshire](/tools/xbtlin-ai-berkshire.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 853d | 0d | | Open issues (now) | 24 | 17 | | Owner type | Organization | User | | Security scan | 83 low (83 low) | No MCP manifest | | Full report | [trust report](/tools/declare-lab-instruct-eval/trust.md) | [trust report](/tools/xbtlin-ai-berkshire/trust.md) | ## Decision facts: ai-berkshire - **Adopt for:** ai-berkshire implements a unique approach to value investing research through AI agents powered by Claude Code/Codex, inspired by the methodologies of Warren Buffett and Charlie Munger amongst other investors. The tool's ## Choose when ### Choose instruct-eval if… - License: instruct-eval is Apache-2.0, ai-berkshire is MIT. - Tags unique to instruct-eval: benchmarking, evaluation, instruct-tuning, instruction-following. ### Choose ai-berkshire if… - License: ai-berkshire is MIT, instruct-eval is Apache-2.0. - Tags unique to ai-berkshire: ai, financial-analysis, investment-research, portfolio-management. - Also covers AI Agents. - You need to leverage multi-Agent adversarial analysis for deep fundamental stock market assessment aligned with renowned investor philosophies. ## When NOT to use instruct-eval - Last GitHub push was 854 days ago (dormant maintenance, Mar 10, 2024). Validate activity before betting a new project on instruct-eval. - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ## When NOT to use ai-berkshire - If your investment research requires real-time trading data or dynamic algorithmic trading strategies which are not the tool's expertise. - When you prefer a more manual or traditional approach to value investing that does not integrate AI-driven adversarial agent methodologies. ## Common questions ### What is the difference between instruct-eval and ai-berkshire? instruct-eval: Code for evaluating instruction-tuned language models like Alpaca and Flan-T5. ai-berkshire: AI-era Berkshire: a value investing research framework utilizing Claude Code / Codex with methodologies from Warren Buffett, Charlie Munger among others and multi-Agent adversarial analysis.. See the comparison table for live GitHub stats and shared categories. ### When should I choose instruct-eval over ai-berkshire? Choose instruct-eval over ai-berkshire when License: instruct-eval is Apache-2.0, ai-berkshire is MIT; Tags unique to instruct-eval: benchmarking, evaluation, instruct-tuning, instruction-following. ### When should I choose ai-berkshire over instruct-eval? Choose ai-berkshire over instruct-eval when License: ai-berkshire is MIT, instruct-eval is Apache-2.0; Tags unique to ai-berkshire: ai, financial-analysis, investment-research, portfolio-management; Also covers AI Agents; You need to leverage multi-Agent adversarial analysis for deep fundamental stock market assessment aligned with renowned investor philosophies. ### When should I avoid instruct-eval? Last GitHub push was 854 days ago (dormant maintenance, Mar 10, 2024). Validate activity before betting a new project on instruct-eval. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ### When should I avoid ai-berkshire? If your investment research requires real-time trading data or dynamic algorithmic trading strategies which are not the tool's expertise. When you prefer a more manual or traditional approach to value investing that does not integrate AI-driven adversarial agent methodologies. ### Is instruct-eval or ai-berkshire more popular on GitHub? ai-berkshire has more GitHub stars (12,711 vs 552). Stars measure visibility, not whether either tool fits your constraints. ### Are instruct-eval and ai-berkshire open source? Yes - both are open-source projects on GitHub (instruct-eval: Apache-2.0, ai-berkshire: MIT). ### Where can I find alternatives to instruct-eval or ai-berkshire? GraphCanon lists graph-backed alternatives at [instruct-eval alternatives](/tools/declare-lab-instruct-eval/alternatives) and [ai-berkshire alternatives](/tools/xbtlin-ai-berkshire/alternatives) ([instruct-eval markdown twin](/tools/declare-lab-instruct-eval/alternatives.md), [ai-berkshire markdown twin](/tools/xbtlin-ai-berkshire/alternatives.md)), 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](/compare/declare-lab-instruct-eval-vs-xbtlin-ai-berkshire.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, instruct-eval or ai-berkshire? instruct-eval: Dormant. ai-berkshire: 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 ai-berkshire? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [instruct-eval trust report](/tools/declare-lab-instruct-eval/trust); [ai-berkshire trust report](/tools/xbtlin-ai-berkshire/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=declare-lab-instruct-eval`](/api/graphcanon/graph?tool=declare-lab-instruct-eval) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_