--- title: "agentops vs agentic-vbench" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/agentops-ai-agentops-vs-philolabs-agentic-vbench" tools: ["agentops-ai-agentops", "philolabs-agentic-vbench"] --- # agentops vs agentic-vbench *GraphCanon updated Sep 20, 2026* ## Verdict Pick agentops if agentOps is an open-source Python toolkit for monitoring AI agents and tracking costs associated with Large Language Model usage; pick agentic-vbench if agenticVBench evaluates AI agents' real-world post-production capabilities with specific task prompts for activities like audio restoration. [agentops](https://agentops.ai) reports 5.8k GitHub stars, 625 forks, and 184 open issues, last pushed Jun 25, 2026. [agentic-vbench](https://agenticvbench.com/) has 96 stars, 27 forks, and 37 open issues, last pushed Sep 2, 2026. Figures are from public GitHub metadata via [agentops's repository](https://github.com/AgentOps-AI/agentops) and [agentic-vbench's repository](https://github.com/PhiloLabs/agentic-vbench). | | [agentops](/tools/agentops-ai-agentops.md) | [agentic-vbench](/tools/philolabs-agentic-vbench.md) | | --- | --- | --- | | Tagline | Python SDK for AI agent monitoring and LLM cost tracking | A benchmark for evaluating AI agents in performing real-world post-production tasks like audio and video editing. | | Stars | 5,830 | 96 | | Forks | 625 | 27 | | Open issues | 184 | 37 | | Language | Python | Python | | Adopt for | AgentOps is an open-source Python toolkit for monitoring AI agents and tracking costs associated with Large Language Model usage. | AgenticVBench evaluates AI agents' real-world post-production capabilities with specific task prompts for activities like audio restoration. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | AI Agents, Evaluation & Observability | AI Agents, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [agentops](/tools/agentops-ai-agentops.md) | [agentic-vbench](/tools/philolabs-agentic-vbench.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 86d | 6d | | Open issues (now) | 184 | 37 | | Stars delta | +59 (30d) | +14 (30d) | | Open issues delta | +8 (30d) | -20 (30d) | | Full report | [trust report](/tools/agentops-ai-agentops/trust.md) | [trust report](/tools/philolabs-agentic-vbench/trust.md) | ## Shared compatibility - **Python**: [agentops](/tools/agentops-ai-agentops.md) - Python runtime; [agentic-vbench](/tools/philolabs-agentic-vbench.md) - Python runtime ## Decision facts: agentops - **Adopt for:** AgentOps is an open-source Python toolkit for monitoring AI agents and tracking costs associated with Large Language Model usage. ## Decision facts: agentic-vbench - **Requirements:** Requires Docker; Install via scripts provided in the repository.; Python virtual environment setup for reproducibility. - **Adopt for:** AgenticVBench evaluates AI agents' real-world post-production capabilities with specific task prompts for activities like audio restoration. ## Choose when ### Choose agentops if… - License: agentops is MIT, agentic-vbench is Apache-2.0. - Tags unique to agentops: benchmarking, cost-tracking. - Integrations are needed specifically with Langchain, CrewAI, or OpenAI Agents SDK ### Choose agentic-vbench if… - License: agentic-vbench is Apache-2.0, agentops is MIT. - Requirements: Requires Docker; Install via scripts provided in the repository.; Python virtual environment setup for reproducibility.. - Tags unique to agentic-vbench: benchmark, harbor, llm-evaluation, video-editing. - When you need to benchmark the performance of AI agents in handling specialized tasks such as audio and video editing that require precise restorative actions. ## When NOT to use agentops - If specific integration support is needed for frameworks not listed including Autogen AG2 CamelAI - In case self-hosting of components is impractical due to resource constraints ## When NOT to use agentic-vbench - When the focus is on generic performance evaluations rather than on real-world, task-specific benchmarks that assess handling complex post-production scenarios. - If your budget or timeline cannot accommodate a per-task wall clock time of ~10 minutes and cost ranging from $0.10 to $2 based on agent token usage. ## Common questions ### What is the difference between agentops and agentic-vbench? agentops: Python SDK for AI agent monitoring and LLM cost tracking. agentic-vbench: A benchmark for evaluating AI agents in performing real-world post-production tasks like audio and video editing.. See the comparison table for live GitHub stats and shared categories. ### When should I choose agentops over agentic-vbench? Choose agentops over agentic-vbench when License: agentops is MIT, agentic-vbench is Apache-2.0; Tags unique to agentops: benchmarking, cost-tracking; Integrations are needed specifically with Langchain, CrewAI, or OpenAI Agents SDK. ### When should I choose agentic-vbench over agentops? Choose agentic-vbench over agentops when License: agentic-vbench is Apache-2.0, agentops is MIT; Requirements: Requires Docker; Install via scripts provided in the repository.; Python virtual environment setup for reproducibility.; Tags unique to agentic-vbench: benchmark, harbor, llm-evaluation, video-editing; When you need to benchmark the performance of AI agents in handling specialized tasks such as audio and video editing that require precise restorative actions. ### When should I avoid agentops? If specific integration support is needed for frameworks not listed including Autogen AG2 CamelAI In case self-hosting of components is impractical due to resource constraints ### When should I avoid agentic-vbench? When the focus is on generic performance evaluations rather than on real-world, task-specific benchmarks that assess handling complex post-production scenarios. If your budget or timeline cannot accommodate a per-task wall clock time of ~10 minutes and cost ranging from $0.10 to $2 based on agent token usage. ### Is agentops or agentic-vbench more popular on GitHub? agentops has more GitHub stars (5,830 vs 96). Stars measure visibility, not whether either tool fits your constraints. ### Are agentops and agentic-vbench open source? Yes - both are open-source projects on GitHub (agentops: MIT, agentic-vbench: Apache-2.0). ### Where can I find alternatives to agentops or agentic-vbench? GraphCanon lists graph-backed alternatives at [agentops alternatives](/tools/agentops-ai-agentops/alternatives) and [agentic-vbench alternatives](/tools/philolabs-agentic-vbench/alternatives) ([agentops markdown twin](/tools/agentops-ai-agentops/alternatives.md), [agentic-vbench markdown twin](/tools/philolabs-agentic-vbench/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/agentops-ai-agentops-vs-philolabs-agentic-vbench.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, agentops or agentic-vbench? agentops: Steady. agentic-vbench: 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 agentops and agentic-vbench? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [agentops trust report](/tools/agentops-ai-agentops/trust); [agentic-vbench trust report](/tools/philolabs-agentic-vbench/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=agentops-ai-agentops`](/api/graphcanon/graph?tool=agentops-ai-agentops) - 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/_