--- title: "agentops vs tma1" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/agentops-ai-agentops-vs-tma1-ai-tma1" tools: ["agentops-ai-agentops", "tma1-ai-tma1"] --- # agentops vs tma1 *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 tma1 if tMA1 is specialized in local-first observability by tracking every LLM call and routing this information to the next agent turn via hooks and MCP. [agentops](https://agentops.ai) reports 5.8k GitHub stars, 625 forks, and 184 open issues, last pushed Jun 25, 2026. [tma1](https://tma1.ai/) has 117 stars, 14 forks, and 5 open issues, last pushed Sep 10, 2026. Figures are from public GitHub metadata via [agentops's repository](https://github.com/AgentOps-AI/agentops) and [tma1's repository](https://github.com/tma1-ai/tma1). | | [agentops](/tools/agentops-ai-agentops.md) | [tma1](/tools/tma1-ai-tma1.md) | | --- | --- | --- | | Tagline | Python SDK for AI agent monitoring and LLM cost tracking | Local-first observability for AI agents with LLM call tracking | | Stars | 5,830 | 117 | | Forks | 625 | 14 | | Open issues | 184 | 5 | | Language | Python | Go | | Adopt for | AgentOps is an open-source Python toolkit for monitoring AI agents and tracking costs associated with Large Language Model usage. | TMA1 is specialized in local-first observability by tracking every LLM call and routing this information to the next agent turn via hooks and MCP. | | Persona | - | - | | Runtime | - | - | | License | MIT | TMA1 is available under the Apache-2.0 license, allowing for broad usage with attribution. | | 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) | [tma1](/tools/tma1-ai-tma1.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 86d | 0d | | Open issues (now) | 184 | 5 | | Stars delta | +59 (30d) | +2 (30d) | | Open issues delta | +8 (30d) | -10 (30d) | | Full report | [trust report](/tools/agentops-ai-agentops/trust.md) | [trust report](/tools/tma1-ai-tma1/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: tma1 - **Pricing:** freemium - Free open-source tool with no initial cost to use or modify according to its Apache-2.0 license. - **Requirements:** TMA1 requires an installation script that auto-configures and sets up GreptimeDB.; Operational requirements are platform-independent, as it supports macOS/Linux install scripts and a PowerShell one for Windows. - **Adopt for:** TMA1 is specialized in local-first observability by tracking every LLM call and routing this information to the next agent turn via hooks and MCP. - **License detail:** TMA1 is available under the Apache-2.0 license, allowing for broad usage with attribution. ## Choose when ### Choose agentops if… - agentops is primarily Python; tma1 is Go. - License: agentops is MIT, tma1 is Apache-2.0. - Tags unique to agentops: ai-agents, benchmarking, cost-tracking. - Integrations are needed specifically with Langchain, CrewAI, or OpenAI Agents SDK ### Choose tma1 if… - tma1 is primarily Go; agentops is Python. - License: tma1 is Apache-2.0, agentops is MIT. - Pricing: Free open-source tool with no initial cost to use or modify according to its Apache-2.0 license.. - Requirements: TMA1 requires an installation script that auto-configures and sets up GreptimeDB.; Operational requirements are platform-independent, as it supports macOS/Linux install scripts and a PowerShell one for Windows.. - Tags unique to tma1: agent-observability, claude-code, codex, greptimedb. - Use TMA1 if you are operating self-hosted AI agents and require detailed observability over LLM calls that can be routed into subsequent turns for further action. ## 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 tma1 - Avoid TMA1 if you prefer cloud-based solutions or require real-time collaboration features that it does not inherently support due to its self-hosted nature. - Do not use TMA1 in environments sensitive to open ports, since it runs a local server and dashboard accessible at default localhost settings which may not suit all security policies. ## Common questions ### What is the difference between agentops and tma1? agentops: Python SDK for AI agent monitoring and LLM cost tracking. tma1: Local-first observability for AI agents with LLM call tracking. See the comparison table for live GitHub stats and shared categories. ### When should I choose agentops over tma1? Choose agentops over tma1 when agentops is primarily Python; tma1 is Go; License: agentops is MIT, tma1 is Apache-2.0; Tags unique to agentops: ai-agents, benchmarking, cost-tracking; Integrations are needed specifically with Langchain, CrewAI, or OpenAI Agents SDK. ### When should I choose tma1 over agentops? Choose tma1 over agentops when tma1 is primarily Go; agentops is Python; License: tma1 is Apache-2.0, agentops is MIT; Pricing: Free open-source tool with no initial cost to use or modify according to its Apache-2.0 license.; Requirements: TMA1 requires an installation script that auto-configures and sets up GreptimeDB.; Operational requirements are platform-independent, as it supports macOS/Linux install scripts and a PowerShell one for Windows.; Tags unique to tma1: agent-observability, claude-code, codex, greptimedb; Use TMA1 if you are operating self-hosted AI agents and require detailed observability over LLM calls that can be routed into subsequent turns for further action. ### 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 tma1? Avoid TMA1 if you prefer cloud-based solutions or require real-time collaboration features that it does not inherently support due to its self-hosted nature. Do not use TMA1 in environments sensitive to open ports, since it runs a local server and dashboard accessible at default localhost settings which may not suit all security policies. ### Is agentops or tma1 more popular on GitHub? agentops has more GitHub stars (5,830 vs 117). Stars measure visibility, not whether either tool fits your constraints. ### Are agentops and tma1 open source? Yes - both are open-source projects on GitHub (agentops: MIT, tma1: Apache-2.0). ### Where can I find alternatives to agentops or tma1? GraphCanon lists graph-backed alternatives at [agentops alternatives](/tools/agentops-ai-agentops/alternatives) and [tma1 alternatives](/tools/tma1-ai-tma1/alternatives) ([agentops markdown twin](/tools/agentops-ai-agentops/alternatives.md), [tma1 markdown twin](/tools/tma1-ai-tma1/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-tma1-ai-tma1.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, agentops or tma1? agentops: Steady. tma1: 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 tma1? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [agentops trust report](/tools/agentops-ai-agentops/trust); [tma1 trust report](/tools/tma1-ai-tma1/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/_