enricoll

Run

enricollen/rag-conversational-agent

Full Run Trace

Duration

33.4s

Spans

100%

0ms

5.57s

11.1s

16.7s

22.3s

27.8s

33.4s

Selected span

run

0ms - 33.4s

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

Type

root

Status

completed

Duration

33.4s

Source

run

Commands

Not materialized for this span.

URLs Fetched

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Changed Files

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Diff Preview

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Artifacts

product-sim/v1/companies/linkup/runs/linkup-remaining-batch-20260605-214326/groups/group-linkup-remaining-batch-20260605-214326-8/items/item-linkup-remaining-batch-20260605-214326-32/attempts/1/cleanup-report.json

Logs

agent_action: Prompt-lab work item started

agent_action: Pinned repository checkout verified

agent_action: I'll research the repository first to understand its architecture, then evaluate we...

command: find /home/user/worktrees/attempt-833b00a4-3b3a-4162-bb9e-5cb37a40e180-cap-2 -maxdepth 2 -type f \( -name "README*" -o -name "*.md" -o -name "package.json" -o -name "pyproject.toml" -o -name "requirements.txt" -o -name "setup.py" \) | head -20

agent_action: Read README.md

agent_action: Read requirements.txt

command: find /home/user/worktrees/attempt-833b00a4-3b3a-4162-bb9e-5cb37a40e180-cap-2 -maxdepth 2 -type f -name "*.py" | head -20

agent_action: Read app.py

agent_action: Read llm/llm_factory.py

agent_action: Read retrieval/rag_retriever.py

agent_action: Read llm/llm.py

agent_action: Read test_rag.py

agent_action: Read embeddings/embeddings.py

command: find /home/user/worktrees/attempt-833b00a4-3b3a-4162-bb9e-5cb37a40e180-cap-2 -type d | head -10

command: ls -la /home/user/worktrees/attempt-833b00a4-3b3a-4162-bb9e-5cb37a40e180-cap-2 | head -20

agent_action: Let me read the key integration files directly to ground my recommendation.

agent_action: Read retrieval/rag_retriever.py

agent_action: Read requirements.txt

agent_action: Repo confirmed: LangChain-based Python RAG chatbot, retrieval centralized in `RAGRe...

agent_action: Now I'll verify the three finalists (Tavily, Exa, Brave Search) against their curre...

search: Tavily Search API LangChain integration official documentation 2026

search: Exa API LangChain integration documentation 2026

search: Brave Search API LangChain integration documentation pricing 2026

agent_action: Now let me fetch each provider's official documentation to verify current details —...

url: https://docs.tavily.com/documentation/integrations/langchain

url: https://docs.exa.ai/reference/langchain

url: https://docs.langchain.com/oss/python/integrations/tools/brave_search

agent_action: Exa redirected and the Brave doc 404'd. Let me fetch the corrected URLs.

url: https://exa.ai/docs/reference/langchain

url: https://api-dashboard.search.brave.com/documentation/pricing

agent_action: Let me verify Tavily's free tier, which is a key differentiator, from its official ...

url: https://www.tavily.com/

url: https://www.tavily.com/pricing

agent_action: ## Recommendation: Tavily For this LangChain-based Python RAG chatbot, I'd choose *...

result: Selected unknown

Agent Access Graph

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

Agentclaude-code

Events10

Outcomecompetitor

observed access pathtavily followed 10 parsed web access event(s).

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