2.26.5.32. pycsamt.agents.report#

pycsamt.agents.report#

ReportAgent — Assemble all agent results into a survey report.

The report is built in three formats:

  • Markdown — always produced; human-readable plain text + embedded image paths.

  • HTML — produced when markdown package is installed.

  • PDF — produced when weasyprint or pdfkit is installed.

The agent queries the LLM once per section (optional) to write a narrative paragraph, then assembles everything into a structured document:

  1. Title & metadata

  2. Data loading summary

  3. QC summary + figure

  4. Static-shift correction summary + figure

  5. Phase tensor analysis summary + figures

  6. Forward modelling summary + figure

  7. Recommendations

Classes

ReportAgent(*[, api_key, model, ...])

Generate a structured MT survey report from agent results.
class pycsamt.agents.report.ReportAgent(*, api_key=None, model=None, llm_provider='claude', report_title='MT/AMT Survey Report', formats=None)[source]

Bases: BaseAgent

Generate a structured MT survey report from agent results.

Parameters:

  • api_key (str)

  • model (str)

  • llm_provider (str)

  • report_title (str) – Title for the report.

  • formats (list of {"md", "html", "pdf"}) – Output formats. Default ["md", "html"].

  • keys (Output data)

  • ----------

  • results (dict) – Keyed by agent step name → AgentResult. Expected keys: "load", "qc", "static_shift", "phase_analysis", "forward" (all optional).

  • output_dir (str)

  • title (str, optional — overrides constructor default)

  • keys

  • ----------------

  • text (report_md str — full markdown)

  • None (report_path_html str or)

  • file (report_path_md str — path to .md)

  • None

  • name (sections dict — section text keyed by)

Examples

>>> agent = ReportAgent(api_key="sk-ant-…")
>>> result = agent.execute(
...     {
...         "results": {"load": load_result, "qc": qc_result},
...         "output_dir": "/out/report",
...         "title": "WILLY_DATA AMT Survey — L22PLT",
...     }
... )
>>> print(result["report_path_md"])
/out/report/survey_report.md
SYSTEM_PROMPT: str = 'You are a geophysics technical writer specialising in MT surveys.\nWrite clear, concise report sections in formal scientific English.\nUse complete sentences. No markdown headings inside your response.\nKeep each section to 3–5 sentences.\n'

Override in subclasses to give the LLM its domain expertise.

execute(input_data)[source]

Run this agent on input_data and return an AgentResult.

Subclasses must implement this method. The contract:

  • Reset self._last_cost = 0.0 at the top.

  • Record wall-clock time with t0 = time.time().

  • Return AgentResult(elapsed_seconds=time.time()-t0, cost_estimate_usd=self._last_cost, ...).

Parameters:

input_data (dict[str, Any])

Return type:

AgentResult

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