How to Build a Multi-Agent ESG Report Analyzer in Python¶

ESG analysis pulls from many sources — third-party ratings, a company's own disclosures, and a regulatory framework — and which sources matter depends on the mandate. This recipe uses the Orchestrator-Workers pattern: a lead decides which workers to dispatch (ratings, disclosure extraction, SFDR/CSRD scoring), then synthesizes their output into one sustainability summary.

Patterns used: Orchestrator-Workers

Architecture¶

flowchart TD
    R[Company + Mandate] --> O[ESG Lead\nplan workers]
    O -->|assign| RT[Ratings Worker]
    O -->|assign| DX[Disclosure Extractor]
    O -->|assign| SC[SFDR/CSRD Scorer]
    RT --> S[ESG Lead\nsynthesize]
    DX --> S
    SC --> S
    S --> A[ESG Summary + Score]

Implementation¶

pip install pyagent-patterns pyagent-providers

import asyncio
from pyagent_patterns.base import Agent
from pyagent_patterns.orchestration import OrchestratorWorkers
from pyagent_providers import AnthropicLLM, OpenAILLM

fast_llm = OpenAILLM("gpt-4o-mini")
smart_llm = AnthropicLLM("claude-sonnet-4-20250514")

esg = OrchestratorWorkers(
    orchestrator=Agent(
        "esg_lead", smart_llm,
        system_prompt=(
            "You analyze a company's ESG profile for an investor mandate. Decide which workers are "
            "needed and assign each a subtask — skip any the mandate doesn't require. Respond as JSON: "
            '{"assignments": [{"worker": "name", "subtask": "..."}]}. Then synthesize a summary with '
            "an overall ESG rating (A-E), the top strength, the top controversy, and an SFDR article fit."
        ),
    ),
    workers=[
        Agent(
            "ratings", fast_llm,
            system_prompt="Summarize the company's third-party ESG ratings and any rating disagreements.",
        ),
        Agent(
            "disclosure_extractor", smart_llm,
            system_prompt="Extract the company's reported Scope 1/2/3 emissions, targets, and board-diversity data.",
        ),
        Agent(
            "sfdr_scorer", smart_llm,
            system_prompt="Score alignment with SFDR/CSRD: PAI coverage, taxonomy eligibility, and gaps.",
        ),
        Agent(
            "controversy", fast_llm,
            system_prompt="List recent ESG controversies (labor, governance, environmental) with severity.",
        ),
    ],
)

async def main():
    result = await esg.run(
        "Analyze ACME Energy for an SFDR Article 8 fund. Mandate excludes thermal coal; "
        "emphasize emissions trajectory and governance."
    )
    print(result.output)
    print(f"Workers used: {result.metadata['workers_used']}")

asyncio.run(main())

Expected Output¶

ESG SUMMARY — ACME Energy  (SFDR Article 8 screen)

Overall: C (improving) — credible 2030 target but high Scope 3.
Strength: board independence 80%, science-based target validated.
Controversy: 2024 spill settlement (medium); no thermal-coal exposure (passes the exclusion).
SFDR: Article 8 fit OK; PAI coverage partial — water & biodiversity metrics missing.

Workers used: ['ratings', 'disclosure_extractor', 'sfdr_scorer', 'controversy']

The lead skips workers the mandate doesn't need (e.g. no controversy scan for a passive index screen), so cost tracks the depth each analysis actually requires.

Customization¶

Plug in live ratings/filings tools¶

Replace the static workers with ReAct agents that pull MSCI/Sustainalytics scores and the latest 10-K / CSRD filing — see the SQL Analytics Assistant.

Batch a fund's holdings¶

async def screen_fund(companies: list[str]) -> list[str]:
    out = await asyncio.gather(*(esg.run(c) for c in companies))
    return [r.output for r in out]

Stricter exclusion enforcement¶

esg.orchestrator.system_prompt += " If any hard-excluded activity is found, output EXCLUDE and stop."

When to Use¶

Situation	Use Orchestrator-Workers?
Which ESG sources to use depends on the mandate	✅ Yes
You want one synthesized rating from many specialists	✅ Yes
Every analysis runs the same fixed stages	❌ Use Pipeline
Teams of analysts each have their own sub-work	❌ Use Hierarchical

Cost Profile¶

Stage	Typical model	Avg cost	Volume (2k companies/yr)
Lead (plan + synthesize)	claude-sonnet	$0.008	$16
Workers (up to 4)	mini + sonnet	$0.010	$20
Per company	mix	~$0.018	~$36/yr