--- title: "agentops vs databuff" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/agentops-ai-agentops-vs-databufflabs-databuff" tools: ["agentops-ai-agentops", "databufflabs-databuff"] --- # agentops vs databuff *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 databuff if dataBuff is an AI-native open-source APM software that integrates OpenTelemetry standards to offer full-chain monitoring, service topology analysis, and AI assistance in problem-solving for cloud-native scenarios. [agentops](https://agentops.ai) reports 5.8k GitHub stars, 625 forks, and 184 open issues, last pushed Jun 25, 2026. [databuff](https://databuff.ai) has 665 stars, 130 forks, and 11 open issues, last pushed Sep 10, 2026. Figures are from public GitHub metadata via [agentops's repository](https://github.com/AgentOps-AI/agentops) and [databuff's repository](https://github.com/databufflabs/databuff). | | [agentops](/tools/agentops-ai-agentops.md) | [databuff](/tools/databufflabs-databuff.md) | | --- | --- | --- | | Tagline | Python SDK for AI agent monitoring and LLM cost tracking | AI-native OpenTelemetry APM with multi-agent root-cause analysis | | Stars | 5,830 | 665 | | Forks | 625 | 130 | | Open issues | 184 | 11 | | Language | Python | Java | | Adopt for | AgentOps is an open-source Python toolkit for monitoring AI agents and tracking costs associated with Large Language Model usage. | DataBuff is an AI-native open-source APM software that integrates OpenTelemetry standards to offer full-chain monitoring, service topology analysis, and AI assistance in problem-solving for cloud-native scenarios. | | Persona | - | - | | Runtime | - | - | | License | MIT | AGPL-3.0 | | Categories | AI Agents, Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [agentops](/tools/agentops-ai-agentops.md) | [databuff](/tools/databufflabs-databuff.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 86d | 0d | | Open issues (now) | 184 | 11 | | Stars delta | +59 (30d) | +138 (30d) | | Open issues delta | +8 (30d) | 0 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/agentops-ai-agentops/trust.md) | [trust report](/tools/databufflabs-databuff/trust.md) | ## 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: databuff - **Hosting:** self hosted - **Pricing:** freemium - Open-source under the AGPL-3.0 license, no cost for use but with obligations. - **Adopt for:** DataBuff is an AI-native open-source APM software that integrates OpenTelemetry standards to offer full-chain monitoring, service topology analysis, and AI assistance in problem-solving for cloud-native scenarios. - **License detail:** AGPL-3.0 ## Choose when ### Choose agentops if… - agentops is primarily Python; databuff is Java. - License: agentops is MIT, databuff is AGPL-3.0. - Tags unique to agentops: ai-agents, benchmarking, cost-tracking. - Also covers AI Agents. - Integrations are needed specifically with Langchain, CrewAI, or OpenAI Agents SDK ### Choose databuff if… - databuff is primarily Java; agentops is Python. - License: databuff is AGPL-3.0, agentops is MIT. - Pricing: Open-source under the AGPL-3.0 license, no cost for use but with obligations.. - Tags unique to databuff: ai, aiops, apm, devops. - Use DataBuff when you need AI-driven root-cause analysis capabilities across traces, metrics, and service topologies. ## 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 databuff - DataBuff may not be suitable when you require real-time eBPF APM capabilities, as this feature is still under development. - Do not use DataBuff if your monitoring requirements do not involve the use of AI to handle multiple agents and their coordination for complex problems. - If your project prefers proprietary observability solutions over open-source alternatives that enforce AGPL-3.0 licensing terms, DataBuff might not align with your project's goals. ## Common questions ### What is the difference between agentops and databuff? agentops: Python SDK for AI agent monitoring and LLM cost tracking. databuff: AI-native OpenTelemetry APM with multi-agent root-cause analysis. See the comparison table for live GitHub stats and shared categories. ### When should I choose agentops over databuff? Choose agentops over databuff when agentops is primarily Python; databuff is Java; License: agentops is MIT, databuff is AGPL-3.0; Tags unique to agentops: ai-agents, benchmarking, cost-tracking; Also covers AI Agents; Integrations are needed specifically with Langchain, CrewAI, or OpenAI Agents SDK. ### When should I choose databuff over agentops? Choose databuff over agentops when databuff is primarily Java; agentops is Python; License: databuff is AGPL-3.0, agentops is MIT; Pricing: Open-source under the AGPL-3.0 license, no cost for use but with obligations.; Tags unique to databuff: ai, aiops, apm, devops; Use DataBuff when you need AI-driven root-cause analysis capabilities across traces, metrics, and service topologies. ### 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 databuff? DataBuff may not be suitable when you require real-time eBPF APM capabilities, as this feature is still under development. Do not use DataBuff if your monitoring requirements do not involve the use of AI to handle multiple agents and their coordination for complex problems. If your project prefers proprietary observability solutions over open-source alternatives that enforce AGPL-3.0 licensing terms, DataBuff might not align with your project's goals. ### Is agentops or databuff more popular on GitHub? agentops has more GitHub stars (5,830 vs 665). Stars measure visibility, not whether either tool fits your constraints. ### Are agentops and databuff open source? Yes - both are open-source projects on GitHub (agentops: MIT, databuff: AGPL-3.0). ### Where can I find alternatives to agentops or databuff? GraphCanon lists graph-backed alternatives at [agentops alternatives](/tools/agentops-ai-agentops/alternatives) and [databuff alternatives](/tools/databufflabs-databuff/alternatives) ([agentops markdown twin](/tools/agentops-ai-agentops/alternatives.md), [databuff markdown twin](/tools/databufflabs-databuff/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-databufflabs-databuff.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, agentops or databuff? agentops: Steady. databuff: 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 databuff? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [agentops trust report](/tools/agentops-ai-agentops/trust); [databuff trust report](/tools/databufflabs-databuff/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/_