--- title: "agentops vs thinkgpt" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/agentops-ai-agentops-vs-jina-ai-thinkgpt" tools: ["agentops-ai-agentops", "jina-ai-thinkgpt"] --- # agentops vs thinkgpt *GraphCanon updated Aug 14, 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 thinkgpt if thinkGPT stands out for its specialization in agent techniques to expand the abilities of large language models, offering unique value through Python integration under an Apache-2.0 license. [agentops](https://agentops.ai) reports 5.8k GitHub stars, 612 forks, and 176 open issues, last pushed Jun 25, 2026. [thinkgpt](https://github.com/jina-ai/thinkgpt) has 1.6k stars, 132 forks, and 16 open issues, last pushed May 23, 2024. Figures are from public GitHub metadata via [agentops's repository](https://github.com/AgentOps-AI/agentops) and [thinkgpt's repository](https://github.com/jina-ai/thinkgpt). | | [agentops](/tools/agentops-ai-agentops.md) | [thinkgpt](/tools/jina-ai-thinkgpt.md) | | --- | --- | --- | | Tagline | Python SDK for AI agent monitoring and LLM cost tracking | Agent techniques to augment your LLM and push it beyond its limits | | Stars | 5,771 | 1,581 | | Forks | 612 | 132 | | Open issues | 176 | 16 | | 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. | ThinkGPT stands out for its specialization in agent techniques to expand the abilities of large language models, offering unique value through Python integration under an Apache-2.0 license. | | Persona | - | - | | Runtime | - | - | | License | MIT | ThinkGPT is released under the permissive Apache-2.0 license. | | Categories | AI Agents, Evaluation & Observability | AI Agents | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [agentops](/tools/agentops-ai-agentops.md) | [thinkgpt](/tools/jina-ai-thinkgpt.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Dormant (18%) | | Days since push | 49d | 806d | | Open issues (now) | 176 | 16 | | Full report | [trust report](/tools/agentops-ai-agentops/trust.md) | [trust report](/tools/jina-ai-thinkgpt/trust.md) | ## Shared compatibility - **Python**: [agentops](/tools/agentops-ai-agentops.md) - Python runtime; [thinkgpt](/tools/jina-ai-thinkgpt.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: thinkgpt - **Pricing:** freemium - Open source with no direct costs, but may require resource investment for setup and maintenance. - **Requirements:** Min 4 GB RAM; Python environment is necessary. No Docker container required. - **Adopt for:** ThinkGPT stands out for its specialization in agent techniques to expand the abilities of large language models, offering unique value through Python integration under an Apache-2.0 license. - **License detail:** ThinkGPT is released under the permissive Apache-2.0 license. ## Choose when ### Choose agentops if… - License: agentops is MIT, thinkgpt is Apache-2.0. - Tags unique to agentops: ai-agents, benchmarking, cost-tracking. - Also covers Evaluation & Observability. - Integrations are needed specifically with Langchain, CrewAI, or OpenAI Agents SDK ### Choose thinkgpt if… - License: thinkgpt is Apache-2.0, agentops is MIT. - Pricing: Open source with no direct costs, but may require resource investment for setup and maintenance.. - Requirements: Min 4 GB RAM; Python environment is necessary. No Docker container required.. - Tags unique to thinkgpt: agent techniques, llm augmentation, machine learning enhancement, python library. - When you need advanced augmentation for your existing language model capabilities with an emphasis on agent-based techniques. ## 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 thinkgpt - If your project requires direct access to pre-built agent components from other libraries (e.g., LangChain), as ThinkGPT focuses on its own augmentation approach. - In scenarios where integration with proprietary or closed-source systems is required, given ThinkGPT's open-source nature under the Apache-2.0 license. ## Common questions ### What is the difference between agentops and thinkgpt? agentops: Python SDK for AI agent monitoring and LLM cost tracking. thinkgpt: Agent techniques to augment your LLM and push it beyond its limits. See the comparison table for live GitHub stats and shared categories. ### When should I choose agentops over thinkgpt? Choose agentops over thinkgpt when License: agentops is MIT, thinkgpt is Apache-2.0; Tags unique to agentops: ai-agents, benchmarking, cost-tracking; Also covers Evaluation & Observability; Integrations are needed specifically with Langchain, CrewAI, or OpenAI Agents SDK. ### When should I choose thinkgpt over agentops? Choose thinkgpt over agentops when License: thinkgpt is Apache-2.0, agentops is MIT; Pricing: Open source with no direct costs, but may require resource investment for setup and maintenance.; Requirements: Min 4 GB RAM; Python environment is necessary. No Docker container required.; Tags unique to thinkgpt: agent techniques, llm augmentation, machine learning enhancement, python library; When you need advanced augmentation for your existing language model capabilities with an emphasis on agent-based techniques. ### 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 thinkgpt? If your project requires direct access to pre-built agent components from other libraries (e.g., LangChain), as ThinkGPT focuses on its own augmentation approach. In scenarios where integration with proprietary or closed-source systems is required, given ThinkGPT's open-source nature under the Apache-2.0 license. ### Is agentops or thinkgpt more popular on GitHub? agentops has more GitHub stars (5,771 vs 1,581). Stars measure visibility, not whether either tool fits your constraints. ### Are agentops and thinkgpt open source? Yes - both are open-source projects on GitHub (agentops: MIT, thinkgpt: Apache-2.0). ### Where can I find alternatives to agentops or thinkgpt? GraphCanon lists graph-backed alternatives at [agentops alternatives](/tools/agentops-ai-agentops/alternatives) and [thinkgpt alternatives](/tools/jina-ai-thinkgpt/alternatives) ([agentops markdown twin](/tools/agentops-ai-agentops/alternatives.md), [thinkgpt markdown twin](/tools/jina-ai-thinkgpt/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-jina-ai-thinkgpt.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, agentops or thinkgpt? agentops: Steady. thinkgpt: Dormant. 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 thinkgpt? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [agentops trust report](/tools/agentops-ai-agentops/trust); [thinkgpt trust report](/tools/jina-ai-thinkgpt/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/_