LangSmith Fetch lets Claude Code, Cursor agents debug from terminal
Developers have been handed a growing toolbox of AI‑driven coding assistants—Claude Code, Cursor, and a handful of others—yet the gap between generating code and diagnosing why a script stalls remains wide. In practice, engineers still juggle separate logs, console output, and opaque model traces, often switching between IDEs and command‑line tools just to see what an agent actually did. That friction slows iteration and makes it harder to trust the output of these assistants, especially when they’re embedded in larger workflows.
Enter a new command‑line utility that promises to bridge that divide. By pulling the full execution record of an agent straight into the terminal, it lets you treat the model as a first‑class debugging subject rather than a black box you can only query indirectly. The approach is simple: invoke the tool, pipe the data, and let the agent’s own insights surface alongside your own diagnostics. This shift could turn everyday code‑generation bots into transparent, step‑by‑step collaborators.
Built for coding agents...
Built for coding agents Here's where it gets really powerful: LangSmith Fetch makes your coding agents expert agent debuggers. When you're using Claude Code, Cursor, or other AI coding assistants, they can now access your complete agent execution data directly. Just run langsmith-fetch and pipe the output to your coding agent. Suddenly, your coding agent can: - Analyze why your agent made a specific decision - Identify inefficient patterns across multiple traces - Suggest prompt improvements based on actual execution data - Build test cases from production failures Example workflow with Claude Code: claude-code "use langsmith-fetch to analyze the traces in
and tell me why the agent failed" Your coding agent now has complete context about what happened, without you manually explaining or copying data around.
LangSmith Fetch finally puts trace data at your fingertips, letting developers stay in the terminal instead of hopping to a web UI. By piping the output of a running agent, Claude Code, Cursor, or similar assistants can read full execution logs on the fly. The tool promises to cut the back‑and‑forth that typically slows debugging sessions.
Yet the announcement offers few details about latency, security, or how it handles large trace volumes. It's also unclear whether the CLI integrates smoothly with all IDEs or only a subset. For teams already invested in LangSmith's cloud console, the addition may feel like a convenient shortcut; for newcomers, the learning curve of a new command‑line interface could offset the gains.
In practice, the value will hinge on how reliably the fetched data mirrors what the UI displays. Until real‑world usage reports emerge, the practical impact of LangSmith Fetch remains uncertain, though the concept aligns with a broader push toward tighter developer workflows.
Further Reading
- How to turn Claude Code into a domain specific coding agent - LangChain Blog
- Testing AI coding agents (2025): Cursor vs. Claude, OpenAI, and Gemini - Render Engineering Blog
- Claude Code vs Cursor: Complete comparison guide in 2025 - Northflank Blog
- Cursor Agent vs Claude Code: A Comparative Guide in 2025 - F22 Labs Blog
- Claude Code vs Cursor: Full Comparison for Developers in 2025 - UI Bakery Blog
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Common Questions Answered
How does LangSmith Fetch let Claude Code and Cursor agents debug directly from the terminal?
LangSmith Fetch runs as a CLI tool that streams full agent execution data to the standard output. By piping this output into Claude Code, Cursor, or similar assistants, the agents can read complete logs and traces without leaving the terminal. This eliminates the need to switch to separate IDEs or web dashboards for debugging.
What specific debugging functions does LangSmith Fetch add to AI coding assistants?
The tool enables agents to analyze why a particular decision was made during a run, spot inefficient patterns across multiple execution traces, and even suggest prompt refinements. These capabilities turn a simple code generator into an expert‑level debugger that can reason about its own behavior in real time.
What concerns remain unanswered about LangSmith Fetch’s performance and security?
The announcement does not provide details on latency when streaming large trace volumes, nor does it explain how the CLI secures sensitive execution data. Additionally, it is unclear whether the tool integrates smoothly with existing development pipelines or requires additional configuration.
Why is having trace data available in the terminal considered advantageous over a web UI for developers?
Accessing trace data in the terminal keeps developers in their primary workflow, reducing the context‑switching overhead of opening a separate web interface. This streamlined approach speeds up iteration cycles and helps maintain trust in AI‑generated code by making debugging steps more transparent and immediate.