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LangChain + E2B Tool Integration
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Linked challenge: Sandboxed Memory Profiling for Agent Code Execution with LangChain and E2B
Format
Code-aware
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Linked challenge
Sandboxed Memory Profiling for Agent Code Execution with LangChain and E2B
Prompt source
Original prompt text with formatting preserved for inspection.
1 lines
1 sections
No variables
0 checklist items
Import `from langchain.agents import initialize_agent` and `from e2b import Sandbox`. Create a LangChain tool named `e2b_runner` that runs Python code inside E2B and returns both the output and peak RAM used.
Adaptation plan
Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.
Keep stable
Hold the task contract and output shape stable so generated implementations remain comparable.
Tune next
Update libraries, interfaces, and environment assumptions to match the stack you actually run.
Verify after
Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.