Comparison
langchain-visualizer vs eval-view
Verdict
Pick langchain-visualizer if a Python-based tool for visualizing LangChain workflows, offering detailed insights into prompt interactions and execution flow; pick eval-view if regression testing for AI agents to detect behavioral changes and output quality regressions over time.
Markdown twin · langchain-visualizer alternatives · eval-view alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | langchain-visualizer | eval-view |
|---|---|---|
| Maintenance | Dormant (885d since push) As of 2w · github_public_v1 | Very active (6d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 2w · github_public_v1 | Not a fork · Personal account As of 3w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) 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
- langchain-visualizer
- Visualization and debugging tool for LangChain workflows
- eval-view
- Regression testing for AI agents
Stars
- langchain-visualizer
- 737
- eval-view
- 126
Forks
- langchain-visualizer
- 49
- eval-view
- 21
Open issues
- langchain-visualizer
- 11
- eval-view
- 3
Language
- langchain-visualizer
- Python
- eval-view
- Python
Adopt for
- langchain-visualizer
- A Python-based tool for visualizing LangChain workflows, offering detailed insights into prompt interactions and execution flow.
- eval-view
- Regression testing for AI agents to detect behavioral changes and output quality regressions over time.
Persona
- langchain-visualizer
- -
- eval-view
- -
Runtime
- langchain-visualizer
- -
- eval-view
- -
License
- langchain-visualizer
- MIT
- eval-view
- The software uses the Apache-2.0 license, offering permissive terms for use and distribution.
Last pushed
- langchain-visualizer
- Mar 6, 2024
- eval-view
- Jul 26, 2026
Categories
- langchain-visualizer
- Evaluation & Observability
- eval-view
- AI Agents, Evaluation & Observability
Trust and health
Maintenance
- langchain-visualizer
- Dormant (18%)
- eval-view
- Very active (96%)
Days since push
- langchain-visualizer
- 885d
- eval-view
- 6d
Open issues (now)
- langchain-visualizer
- 11
- eval-view
- 3
Full report
- langchain-visualizer
- Trust report
- eval-view
- Trust report
Shared compatibility
- Python · langchain-visualizer: Python runtime · eval-view: Python runtime
Choose langchain-visualizer if…
- License: langchain-visualizer is MIT, eval-view is Apache-2.0.
- Tags unique to langchain-visualizer: cost-tracking, debugging, execution-flow, langchain.
- You prioritize UI aesthetics and colored highlighting of prompt parts.
When NOT to use langchain-visualizer
- Prefer the native tracing functionality provided by LangChain itself.
- Do not need detailed LLM call costs or execution flow insights.
Choose eval-view if…
- License: eval-view is Apache-2.0, langchain-visualizer is MIT.
- Pricing: Free to use under the terms of the Apache License, Version 2.0..
- Requirements: Python environment is required for installation and usage.; Installation with pip: `pip install evalview`; Offline support means no live API keys necessary for the basic diff functionality..
- Tags unique to eval-view: agent-benchmark, agent-evaluation, ai-agents, regression-testing.
- Also covers AI Agents.
- eval-view ships Docker support for self-hosted deployment.
- When you need to track and assess the behavior consistency of your AI agent across versions without involving live API calls.
When NOT to use eval-view
- If you do not need to monitor specific behavioral characteristics such as tool call sequences and parameter consistency over time.
- When real-time output quality evaluation is critical, as eval-view's offline diffing does not provide immediate feedback on output changes without an LLM judge.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (amosjyng/langchain-visualizer) · observed Aug 8, 2026
- GitHub forks (amosjyng/langchain-visualizer) · observed Aug 8, 2026
- Last push (amosjyng/langchain-visualizer) · observed Mar 6, 2024
- License file (MIT) · observed Aug 8, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (hidai25/eval-view) · observed Aug 2, 2026
- GitHub forks (hidai25/eval-view) · observed Aug 2, 2026
- Last push (hidai25/eval-view) · observed Jul 26, 2026
- License file (Apache-2.0) · observed Aug 2, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: langchain-visualizer 737 · eval-view 126 (synced Aug 8, 2026).
Common questions
- What is the difference between langchain-visualizer and eval-view?
- langchain-visualizer: Visualization and debugging tool for LangChain workflows. eval-view: Regression testing for AI agents. See the comparison table for live GitHub stats and shared categories.
- When should I choose langchain-visualizer over eval-view?
- Choose langchain-visualizer over eval-view when License: langchain-visualizer is MIT, eval-view is Apache-2.0; Tags unique to langchain-visualizer: cost-tracking, debugging, execution-flow, langchain; You prioritize UI aesthetics and colored highlighting of prompt parts.
- When should I choose eval-view over langchain-visualizer?
- Choose eval-view over langchain-visualizer when License: eval-view is Apache-2.0, langchain-visualizer is MIT; Pricing: Free to use under the terms of the Apache License, Version 2.0.; Requirements: Python environment is required for installation and usage.; Installation with pip:
pip install evalview; Offline support means no live API keys necessary for the basic diff functionality.; Tags unique to eval-view: agent-benchmark, agent-evaluation, ai-agents, regression-testing; Also covers AI Agents; eval-view ships Docker support for self-hosted deployment; When you need to track and assess the behavior consistency of your AI agent across versions without involving live API calls. - When should I avoid langchain-visualizer?
- Prefer the native tracing functionality provided by LangChain itself. Do not need detailed LLM call costs or execution flow insights.
- When should I avoid eval-view?
- If you do not need to monitor specific behavioral characteristics such as tool call sequences and parameter consistency over time. When real-time output quality evaluation is critical, as eval-view's offline diffing does not provide immediate feedback on output changes without an LLM judge.
- Is langchain-visualizer or eval-view more popular on GitHub?
- langchain-visualizer has more GitHub stars (737 vs 126). Stars measure visibility, not whether either tool fits your constraints.
- Are langchain-visualizer and eval-view open source?
- Yes - both are open-source projects on GitHub (langchain-visualizer: MIT, eval-view: Apache-2.0).
- Where can I find alternatives to langchain-visualizer or eval-view?
- GraphCanon lists graph-backed alternatives at langchain-visualizer alternatives and eval-view alternatives (langchain-visualizer markdown twin, eval-view 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, langchain-visualizer or eval-view?
- langchain-visualizer: Dormant. eval-view: 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 langchain-visualizer and eval-view?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: langchain-visualizer trust report; eval-view trust report.