How to use Cursor to complete an agent migration
Cursor completed recruiter-agent integrations after a broad CrewAI-to-LangGraph rewrite failed; Karpić tested it and supplied a working foundation by hand.
linkedin.com
Original prompt
Shared by the source author. See source notes for context.
Here is an agent implemented in crewai @recruiter_agent.py. Migrate it to Langgraph and keep all of the functionality
Reach people exploring AI agents
See placements →Evidence at a glance
Sourced account- Source author
- Arsenije Karpić
- Original account
- Read the source ↗
- Published
- Source link checked
- Reported result
- The owner reports that Cursor completed the remaining tool integrations after a minimal LangGraph foundation was built by hand; no repository-level or recruiting benchmark is supplied.
- Human involvement
- Karpić tested the generated migration, rejected the failed approach and manually established the working architecture before delegating integration work.
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How the agent did it
- Karpić supplied the existing recruiter-agent file and asked Cursor to preserve functionality during migration.
- Testing failed and follow-up prompts stalled, so the owner discarded the generated attempt and built a minimal working foundation.
- Cursor then implemented the remaining front-end and PDF-related tools against that foundation.
Arsenije Karpić asked Cursor to migrate a recruiter agent from CrewAI to LangGraph while preserving its functionality. The existing Python implementation used task-specific prompts, front-end integration tools and PDF extraction tools. A broad file-level request produced code, but testing failed.
Follow-up requests did not resolve the error messages. Karpić discarded that attempt and built the smallest working LangGraph foundation himself. Once the foundation behaved correctly, Cursor implemented the remaining tools and integrations without the earlier problems. The final reported result is a completed migration supported by a human-created base, rather than a successful automatic rewrite from the initial request.
The human checkpoint was architectural and executable: establish a minimal implementation that works before delegating the rest. The post shares the original unsuccessful prompt, making the change in task boundary concrete.
It does not publish a benchmark, repository diff or a detailed explanation of the initial root cause. It also gives no measured recruiting outcome. This is a firsthand implementation account about recovering a stalled framework migration; the evidence supports restored tool integration after human intervention, with the specific failure mechanism left undocumented.
Prompt
Original prompt
Source: LinkedIn · Arsenije Karpić
Frequently asked questions
Did the initial broad migration request work?
No. The generated code failed testing and further prompts stalled.
What changed before Cursor succeeded?
The owner supplied a minimal working LangGraph foundation.
Was a specific root cause published?
The post reports errors, without documenting their underlying cause.
More details
About this account
Karpić supplied the existing recruiter-agent file and asked Cursor to preserve functionality during migration. Testing failed and follow-up prompts stalled, so the owner discarded the generated attempt and built a minimal working foundation.
Original public firsthand post and exact publication JSON-LD opened. The shared prompt is the unsuccessful first request. Outcome is owner-reported; source does not expose a diff, root-cause analysis or measured hiring result. Original prompt excerpt.
Credit: Arsenije Karpić — LinkedIn
Source published
Added to Agents In Use
Source checked
Tools and topics
Cursor · CrewAI · LangGraph · Python
framework migration · architecture checkpoint · tool integration
Editorial completeness: 100/100. Measures documented fields, not agent performance or verified outcomes.