beastshr

Run

BEASTSHRIRAM/AyuMitraAI

Full Run Trace

Duration

47.8s

Spans

100%

0ms

7.97s

15.9s

23.9s

31.9s

39.8s

47.8s

Selected span

run

0ms - 47.8s

The full run envelope begins when the sandbox execution starts and ends when the simulation result is classified.

Type

root

Status

completed

Duration

47.8s

Source

run

Commands

Not materialized for this span.

URLs Fetched

Not materialized for this span.

Changed Files

Not materialized for this span.

Diff Preview

Diff is not materialized for this span.

Artifacts

product-sim/v1/companies/linkup/runs/linkup-remaining-batch-20260605-214326/groups/group-linkup-remaining-batch-20260605-214326-3/items/item-linkup-remaining-batch-20260605-214326-15/attempts/1/cleanup-report.json

Logs

agent_action: Prompt-lab work item started

agent_action: Pinned repository checkout verified

agent_action: I’ll first inspect the repo to understand the agent’s runtime and integration point...

command: /bin/bash -lc "pwd && rg --files -g '"'!*node_modules*'"' -g '"'!*.lock'"' | head -200"

command: /bin/bash -lc 'git status --short'

command: /bin/bash -lc 'git status --short'

agent_action: `rg` is not installed in this environment, so I’m falling back to `find`/`grep` to ...

command: /bin/bash -lc "find . -path './node_modules' -prune -o -path './.git' -prune -o -type f | sed 's#"'^./##'"' | head -250"

command: /bin/bash -lc "find . -maxdepth 2 -type f \\( -name 'package.json' -o -name 'pyproject.toml' -o -name 'requirements*.txt' -o -name 'README*' -o -name 'AGENTS.md' -o -name '.env.example' \\) -print"

agent_action: The repo is a small FastAPI backend with LangChain tool-calling agents plus a React...

command: /bin/bash -lc "sed -n '1,580p' backend/langchain_agents.py"

command: /bin/bash -lc "sed -n '1,220p' README.md"

command: /bin/bash -lc "sed -n '1,330p' backend/agent_endpoints.py"

command: /bin/bash -lc "sed -n '1,220p' backend/requirements.txt && sed -n '1,140p' backend/.env.example && sed -n '1,220p' backend/config.py"

agent_action: The current agents already use LangChain tools, but their “guideline” and interacti...

command: /bin/bash -lc "sed -n '1,260p' backend/cerebras_service.py"

command: /bin/bash -lc "sed -n '1,220p' backend/server.py"

command: /bin/bash -lc "sed -n '220,540p' backend/server.py"

search: managed web search API AI agents licensed sources reputable content provider Tavily...

search: site:docs.linkup.so licensed premium content sources Linkup API

search: Perplexity Sonar API documentation citations search results sources

search: Linkup LangChain integration documentation

search: medical clinical evidence API licensed content UpToDate DynaMed ClinicalKey API doc...

url: https://www.wolterskluwer.com/en/solutions/uptodate/enterprise/digital-architect/connect

url: https://docs.linkup.so/pages/documentation/endpoints/search/overview

agent_action: I’ve narrowed the decision to two classes: clinical licensed knowledge APIs versus ...

command: /bin/bash -lc "nl -ba backend/langchain_agents.py | sed -n '1,230p'"

command: /bin/bash -lc "nl -ba backend/requirements.txt | sed -n '1,80p'"

command: /bin/bash -lc "nl -ba backend/cerebras_service.py | sed -n '1,140p'"

command: /bin/bash -lc "nl -ba backend/config.py | sed -n '1,80p'"

url: https://docs.linkup.so/

agent_action: **Provider I’d choose: Wolters Kluwer UpToDate Connect.** This repo is a medical tr...

result: Selected unknown

Agent Access Graph

Decision-axis graph for the exact point where the agent stayed on path or diverged.

Agentcodex

Events8

Outcometarget

observed access pathlinkup followed 8 parsed web access event(s).

Runs

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Run

Agent Access Graph

Runs

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Agent Access Graph