Re agent

[kuivi]

Restructure data_analysis_agent for parallel downloads

Problem

The data analysis agent downloaded ERA5 and DestinE variables sequentially — each variable was a separate LLM tool call processed one at a time by AgentExecutor. Downloading 3 ERA5 + 2 DestinE variables meant 5 sequential network calls, wasting minutes of wall time.

Solution

Replace the single AgentExecutor with a 4-step pipeline where downloads happen in parallel:

filter_node → planner_node → download_node → analysis_node

1. filter_node — LLM condenses context into an analysis brief (same as before)
2. planner_node — LLM decides which variables to download, returns structured JSON
3. download_node — ThreadPoolExecutor fetches all variables in parallel (up to 6 threads)
4. analysis_node — AgentExecutor with Python REPL, no download tools (all data pre-fetched)

Pipeline steps are plain Python functions sharing a gstate dict, connected with @traceable for LangSmith visibility.

What this changes

• Downloads don't count as tool calls — full tool budget goes to analysis/plotting
• ERA5 default range — 1975–2024 (was 2015–2024), planner uses full range unless user asks otherwise
• DestinE default range — 2020–2039
• Download progress — per-variable timing shown in Streamlit status area
• API key security — Arraylake key read from ARRAYLAKE_API_KEY env var only, never stored in config or state
• Arraylake client cleanup — explicit client.close() to fix async finalizer errors in threads

Files changed

File	Change
data_analysis_agent.py	Restructured as 4-step parallel pipeline
streamlit_interface.py	Set ARRAYLAKE_API_KEY env var instead of storing in config
tools/era5_retrieval_tool.py	Init + close arraylake Client properly for threaded use