Comparison
agentops vs thinkgpt
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.
Markdown twin · agentops alternatives · thinkgpt alternatives
GraphCanon updated 1w
vs
Trust & integrity
| Signal | agentops | thinkgpt |
|---|---|---|
| Maintenance | Steady (49d since push) As of 1w · github_public_v1 | Dormant (806d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 1w · github_public_v1 | Not a fork · Organization account As of 2w · 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
- agentops
- Python SDK for AI agent monitoring and LLM cost tracking
- thinkgpt
- Agent techniques to augment your LLM and push it beyond its limits
Stars
- agentops
- 5.8k
- thinkgpt
- 1.6k
Forks
- agentops
- 612
- thinkgpt
- 132
Open issues
- agentops
- 176
- thinkgpt
- 16
Language
- agentops
- Python
- thinkgpt
- Python
Adopt for
- agentops
- AgentOps is an open-source Python toolkit for monitoring AI agents and tracking costs associated with Large Language Model usage.
- thinkgpt
- 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
- agentops
- -
- thinkgpt
- -
Runtime
- agentops
- -
- thinkgpt
- -
License
- agentops
- MIT
- thinkgpt
- ThinkGPT is released under the permissive Apache-2.0 license.
Last pushed
- agentops
- Jun 25, 2026
- thinkgpt
- May 23, 2024
Categories
- agentops
- AI Agents, Evaluation & Observability
- thinkgpt
- AI Agents
Trust and health
Maintenance
- agentops
- Steady (60%)
- thinkgpt
- Dormant (18%)
Days since push
- agentops
- 49d
- thinkgpt
- 806d
Open issues (now)
- agentops
- 176
- thinkgpt
- 16
Full report
- agentops
- Trust report
- thinkgpt
- Trust report
Shared compatibility
- Python · agentops: Python runtime · thinkgpt: Python runtime
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
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
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (AgentOps-AI/agentops) · observed Aug 14, 2026
- GitHub forks (AgentOps-AI/agentops) · observed Aug 14, 2026
- Last push (AgentOps-AI/agentops) · observed Jun 25, 2026
- License file (MIT) · observed Aug 14, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
- GitHub stars (jina-ai/thinkgpt) · observed Aug 8, 2026
- GitHub forks (jina-ai/thinkgpt) · observed Aug 8, 2026
- Last push (jina-ai/thinkgpt) · observed May 23, 2024
- License file (Apache-2.0) · observed Aug 8, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: agentops 5.8k · thinkgpt 1.6k (synced Aug 14, 2026).
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 and thinkgpt alternatives (agentops markdown twin, thinkgpt 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, 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; thinkgpt trust report.