--- title: "edsl vs eval-view" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/expectedparrot-edsl-vs-hidai25-eval-view" tools: ["expectedparrot-edsl", "hidai25-eval-view"] --- # edsl vs eval-view *GraphCanon updated Sep 20, 2026* ## Verdict Pick edsl if edsl stands out for designing AI-powered surveys and experiments in social science and market research by letting users simulate large-scale studies involving multiple AI agents and LLMs; pick eval-view if eval-view is a Python-based tool for regression testing of AI agents, supporting multiple platforms like LangGraph, CrewAI, OpenAI, and Anthropic. It snapshots AI behavior and detects regressions. [edsl](https://docs.expectedparrot.com) reports 497 GitHub stars, 84 forks, and 47 open issues, last pushed Sep 18, 2026. [eval-view](https://evalview.com) has 134 stars, 24 forks, and 2 open issues, last pushed Sep 5, 2026. Figures are from public GitHub metadata via [edsl's repository](https://github.com/expectedparrot/edsl) and [eval-view's repository](https://github.com/hidai25/eval-view). | | [edsl](/tools/expectedparrot-edsl.md) | [eval-view](/tools/hidai25-eval-view.md) | | --- | --- | --- | | Tagline | Framework for designing and analyzing AI-powered surveys and experiments | Regression testing for AI agents, snapshots behavior, diffs tool calls, catches regressions in CI | | Stars | 497 | 134 | | Forks | 84 | 24 | | Open issues | 47 | 2 | | Language | Python | Python | | Adopt for | edsl stands out for designing AI-powered surveys and experiments in social science and market research by letting users simulate large-scale studies involving multiple AI agents and LLMs. | Eval-view is a Python-based tool for regression testing of AI agents, supporting multiple platforms like LangGraph, CrewAI, OpenAI, and Anthropic. It snapshots AI behavior and detects regressions through diffing tool and | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | AI Agents, Evaluation & Observability, Model Training | AI Agents, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [edsl](/tools/expectedparrot-edsl.md) | [eval-view](/tools/hidai25-eval-view.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 1d | 13d | | Open issues (now) | 47 | 2 | | Stars delta | +14 (30d) | +8 (30d) | | Open issues delta | +5 (30d) | -1 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/expectedparrot-edsl/trust.md) | [trust report](/tools/hidai25-eval-view/trust.md) | ## Shared compatibility - **Python**: [edsl](/tools/expectedparrot-edsl.md) - Python runtime; [eval-view](/tools/hidai25-eval-view.md) - Python runtime ## Decision facts: edsl - **Adopt for:** edsl stands out for designing AI-powered surveys and experiments in social science and market research by letting users simulate large-scale studies involving multiple AI agents and LLMs. ## Decision facts: eval-view - **Adopt for:** Eval-view is a Python-based tool for regression testing of AI agents, supporting multiple platforms like LangGraph, CrewAI, OpenAI, and Anthropic. It snapshots AI behavior and detects regressions through diffing tool and ## Choose when ### Choose edsl if… - License: edsl is MIT, eval-view is Apache-2.0. - Tags unique to edsl: data-labeling, domain-specific-language, llm-agent, market-research. - Also covers Model Training. - When conducting complex simulations of social science studies that require the use of multiple AI agents or interactions with LLMs. ### Choose eval-view if… - License: eval-view is Apache-2.0, edsl is MIT. - Tags unique to eval-view: agent-benchmark, agent-evaluation, agentic-ai, ai-agents. - eval-view ships Docker support for self-hosted deployment. - When you need to snapshot and diff the behavior of AI agents across multiple platforms, including LangGraph, CrewAI, OpenAI, and Anthropic. ## When NOT to use edsl - If your project requires real human responses and feedback in experiments, edsl is a system designed around AI agents rather than living participants. - In cases where the scope of the experiment does not involve social science or require significant simulation capabilities with large numbers of AI entities. ## When NOT to use eval-view - If you are working exclusively with AI platforms not supported by eval-view, such as those not listed among LangGraph, CrewAI, OpenAI, and Anthropic. - When you do not require regression testing or behavior snapshotting for your AI agents, as eval-view is specifically designed for these purposes. - If you are looking for a tool that does not involve backend API charges for executing your agent, as eval-view does not skip these charges even with the --no-judge flag. - If you need a tool that automatically handles the migration from the OpenAI Assistants API to the Responses API without manual intervention, as eval-view requires following a migration guide for this. ## Common questions ### What is the difference between edsl and eval-view? edsl: Framework for designing and analyzing AI-powered surveys and experiments. eval-view: Regression testing for AI agents, snapshots behavior, diffs tool calls, catches regressions in CI. See the comparison table for live GitHub stats and shared categories. ### When should I choose edsl over eval-view? Choose edsl over eval-view when License: edsl is MIT, eval-view is Apache-2.0; Tags unique to edsl: data-labeling, domain-specific-language, llm-agent, market-research; Also covers Model Training; When conducting complex simulations of social science studies that require the use of multiple AI agents or interactions with LLMs. ### When should I choose eval-view over edsl? Choose eval-view over edsl when License: eval-view is Apache-2.0, edsl is MIT; Tags unique to eval-view: agent-benchmark, agent-evaluation, agentic-ai, ai-agents; eval-view ships Docker support for self-hosted deployment; When you need to snapshot and diff the behavior of AI agents across multiple platforms, including LangGraph, CrewAI, OpenAI, and Anthropic. ### When should I avoid edsl? If your project requires real human responses and feedback in experiments, edsl is a system designed around AI agents rather than living participants. In cases where the scope of the experiment does not involve social science or require significant simulation capabilities with large numbers of AI entities. ### When should I avoid eval-view? If you are working exclusively with AI platforms not supported by eval-view, such as those not listed among LangGraph, CrewAI, OpenAI, and Anthropic. When you do not require regression testing or behavior snapshotting for your AI agents, as eval-view is specifically designed for these purposes. If you are looking for a tool that does not involve backend API charges for executing your agent, as eval-view does not skip these charges even with the --no-judge flag. If you need a tool that automatically handles the migration from the OpenAI Assistants API to the Responses API without manual intervention, as eval-view requires following a migration guide for this. ### Is edsl or eval-view more popular on GitHub? edsl has more GitHub stars (497 vs 134). Stars measure visibility, not whether either tool fits your constraints. ### Are edsl and eval-view open source? Yes - both are open-source projects on GitHub (edsl: MIT, eval-view: Apache-2.0). ### Where can I find alternatives to edsl or eval-view? GraphCanon lists graph-backed alternatives at [edsl alternatives](/tools/expectedparrot-edsl/alternatives) and [eval-view alternatives](/tools/hidai25-eval-view/alternatives) ([edsl markdown twin](/tools/expectedparrot-edsl/alternatives.md), [eval-view markdown twin](/tools/hidai25-eval-view/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/expectedparrot-edsl-vs-hidai25-eval-view.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, edsl or eval-view? edsl: Very active. eval-view: 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 edsl and eval-view? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [edsl trust report](/tools/expectedparrot-edsl/trust); [eval-view trust report](/tools/hidai25-eval-view/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=expectedparrot-edsl`](/api/graphcanon/graph?tool=expectedparrot-edsl) - 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/_