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How to Build a Multi-Agent Portfolio Review Workflow in Python

A good portfolio memo routes each holding to the right specialist and doesn't ship until it's actually rigorous. This recipe uses a Supervisor to send each position to an equities, fixed-income, or risk analyst, then an Evaluator-Optimizer loop that scores the memo against explicit criteria and revises it until it clears the bar.

Patterns used: Supervisor · Evaluator-Optimizer

Architecture

flowchart TD
    H[Holding] --> S[Supervisor\nclassify asset class]
    S -->|equity| EQ[Equities Analyst]
    S -->|fixed_income| FI[Rates Analyst]
    S -->|risk| RK[Risk Analyst]
    EQ --> M{Evaluator-Optimizer}
    FI --> M
    RK --> M
    M -->|score < threshold, revise| M
    M -->|passes| O[Investment Memo]

Implementation

pip install pyagent-patterns pyagent-providers

import asyncio
from pyagent_patterns.base import Agent
from pyagent_patterns.orchestration import Supervisor
from pyagent_patterns.resolution import EvaluatorOptimizer
from pyagent_providers import AnthropicLLM

fast_llm = AnthropicLLM("claude-haiku-3-5-20241022")
smart_llm = AnthropicLLM("claude-sonnet-4-20250514")

# ── Supervisor routes each holding to the right specialist analyst ──────────────
desk = Supervisor(
    classifier=Agent(
        "router", fast_llm,
        system_prompt="Classify the holding as exactly one of: equity, fixed_income, risk. Reply with only the label.",
    ),
    routes={
        "equity": Agent("equities", smart_llm,
                        system_prompt="Analyze the equity position: thesis, valuation multiples, key catalysts and risks."),
        "fixed_income": Agent("rates", smart_llm,
                              system_prompt="Analyze the bond position: duration, credit quality, and rate sensitivity."),
        "risk": Agent("risk", smart_llm,
                      system_prompt="Assess portfolio-level risk: concentration, correlation, and tail scenarios."),
    },
    default_route="risk",
)

# ── Evaluator-Optimizer tightens the memo against explicit criteria ─────────────
memo = EvaluatorOptimizer(
    generator=Agent("writer", smart_llm,
                    system_prompt="Write a concise investment memo from the analysis: recommendation, rationale, risks."),
    evaluator=Agent("reviewer", smart_llm,
                    system_prompt="Score the memo 1-10 against the criteria. Demand specific fixes for any criterion below bar."),
    criteria=["clear recommendation", "evidence-backed rationale", "explicit downside", "position sizing"],
    pass_threshold=8,
    max_rounds=3,
)

async def main():
    analysis = await desk.run("AAPL — 12% of the book, up 30% YTD, trading at 30x forward earnings")
    result = await memo.run(analysis.output)
    print(result.output)
    print(f"Converged in {result.metadata['rounds']} rounds, final score {result.metadata['final_score']}")

asyncio.run(main())

Expected Output

INVESTMENT MEMO — AAPL

Recommendation: TRIM to 8% of book.
Rationale: strong franchise and cash generation, but 30x forward earnings prices in a lot; the 12%
weight is now a concentration risk after the 30% run.
Downside: multiple compression to 24x → ~20% drawdown on the position.
Sizing: trim 4 points; redeploy into the underweight fixed-income sleeve.

Converged in 2 rounds, final score 9

The Evaluator-Optimizer is what forces "explicit downside" and "position sizing" to actually appear — the criteria the first draft skipped and the loop demanded.

Customization

Tune the bar and criteria

memo.pass_threshold = 9                      # stricter for IC-ready memos
memo.criteria.append("comparison vs benchmark")

Review the whole book

async def review_book(holdings: list[str]) -> list[str]:
    out = []
    for h in holdings:
        analysis = await desk.run(h)
        out.append((await memo.run(analysis.output)).output)
    return out

Add a data-gathering analyst

Give the analysts live numbers by routing through a ReAct agent that queries market data — see the SQL Analytics Assistant.

When to Use

Situation Fit
Route each item to one specialist ✅ Supervisor
Output must iterate to an explicit quality bar ✅ Evaluator-Optimizer
Two analysts should argue bull vs bear ❌ Use Debate (Loan Underwriting)
One agent critiques its own draft ❌ Use Self-Reflection

Cost Profile

Stage Typical model Avg cost Volume (1k holdings/mo)
Classifier claude-haiku $0.0005 $0.50
Analyst claude-sonnet $0.005 $5
Memo (≤3 optimize rounds) claude-sonnet $0.012 $12
Per holding mix ~$0.0175 ~$17.5/mo

pass_threshold and max_rounds trade memo quality against cost — most memos clear the bar in two rounds.

See Also