feat(sentiment): structured output for the Sentiment Analyst

The analyst emitted free-form prose, so its sentiment header varied by
provider and run and downstream consumers needed drifting regex. Extend
the structured-output pattern the trio already uses: a SentimentReport
schema (band + 0-10 score + confidence + narrative) rendered to a
deterministic header, with a free-text fallback for providers that lack
native structured output.

#796

tests/test_structured_agents.py

@@ -1,23 +1,28 @@
-"""Tests for structured-output agents (Trader and Research Manager).
+"""Tests for structured-output agents (Trader, Research Manager, Sentiment Analyst).
 
 The Portfolio Manager has its own coverage in tests/test_memory_log.py
 (which exercises the full memory-log → PM injection cycle).  This file
 covers the parallel schemas, render functions, and graceful-fallback
-behavior we added for the Trader and Research Manager so all three
+behavior we added for the Trader, Research Manager, and Sentiment Analyst
-decision-making agents share the same shape.
+so they share the same deterministic output shape.
 """
 
 from unittest.mock import MagicMock
 
 import pytest
+from pydantic import ValidationError
 
+from tradingagents.agents.analysts.sentiment_analyst import create_sentiment_analyst
 from tradingagents.agents.managers.research_manager import create_research_manager
 from tradingagents.agents.schemas import (
     PortfolioRating,
     ResearchPlan,
+    SentimentBand,
+    SentimentReport,
     TraderAction,
     TraderProposal,
     render_research_plan,
+    render_sentiment_report,
     render_trader_proposal,
 )
 from tradingagents.agents.trader.trader import create_trader
@@ -230,3 +235,126 @@ class TestResearchManagerAgent:
         rm = create_research_manager(llm)
         result = rm(_make_rm_state())
         assert result["investment_plan"] == plain_response
+
+
+# ---------------------------------------------------------------------------
+# Sentiment Analyst: schema, render, structured happy path + fallback
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.unit
+class TestRenderSentimentReport:
+    def test_header_contains_band_and_score(self):
+        report = SentimentReport(
+            overall_band=SentimentBand.BULLISH,
+            overall_score=7.2,
+            confidence="high",
+            narrative="Source breakdown here.",
+        )
+        md = render_sentiment_report(report)
+        assert "**Overall Sentiment:** **Bullish**" in md
+        assert "(Score: 7.2/10)" in md
+
+    def test_header_contains_confidence(self):
+        report = SentimentReport(
+            overall_band=SentimentBand.NEUTRAL,
+            overall_score=5.0,
+            confidence="low",
+            narrative="Limited data.",
+        )
+        assert "**Confidence:** Low" in render_sentiment_report(report)
+
+    def test_narrative_preserved_in_output(self):
+        narrative = "## Breakdown\n\nStockTwits: 70% bullish.\n\n| Signal | Direction |\n|---|---|\n| News | Neutral |"
+        report = SentimentReport(
+            overall_band=SentimentBand.MILDLY_BULLISH,
+            overall_score=6.0,
+            confidence="medium",
+            narrative=narrative,
+        )
+        assert narrative in render_sentiment_report(report)
+
+    def test_all_six_bands_render(self):
+        for band in SentimentBand:
+            report = SentimentReport(
+                overall_band=band, overall_score=5.0,
+                confidence="medium", narrative="n",
+            )
+            assert band.value in render_sentiment_report(report)
+
+    def test_score_out_of_range_rejected(self):
+        with pytest.raises(ValidationError):
+            SentimentReport(
+                overall_band=SentimentBand.BULLISH, overall_score=11.0,
+                confidence="high", narrative="n",
+            )
+
+
+def _make_sentiment_state():
+    return {
+        "company_of_interest": "NVDA",
+        "trade_date": "2026-01-15",
+        "asset_type": "stock",
+        "messages": [],
+    }
+
+
+def _structured_sentiment_llm(captured: dict, report: SentimentReport | None = None):
+    """MagicMock LLM whose structured binding captures the prompt and returns
+    a real SentimentReport so render_sentiment_report works."""
+    if report is None:
+        report = SentimentReport(
+            overall_band=SentimentBand.BULLISH, overall_score=7.5,
+            confidence="high",
+            narrative="StockTwits 75% bullish. News constructive. Reddit upbeat.",
+        )
+    structured = MagicMock()
+    structured.invoke.side_effect = lambda prompt: (
+        captured.__setitem__("prompt", prompt) or report
+    )
+    llm = MagicMock()
+    llm.with_structured_output.return_value = structured
+    return llm
+
+
+@pytest.mark.unit
+class TestSentimentAnalystAgent:
+    def test_structured_path_produces_rendered_markdown(self):
+        captured = {}
+        report = SentimentReport(
+            overall_band=SentimentBand.MILDLY_BEARISH, overall_score=4.0,
+            confidence="medium", narrative="Mixed signals across sources.",
+        )
+        analyst = create_sentiment_analyst(_structured_sentiment_llm(captured, report))
+        sr = analyst(_make_sentiment_state())["sentiment_report"]
+        assert "**Overall Sentiment:** **Mildly Bearish**" in sr
+        assert "(Score: 4.0/10)" in sr
+        assert "Mixed signals across sources." in sr
+
+    def test_sentiment_report_also_in_messages(self):
+        captured = {}
+        analyst = create_sentiment_analyst(_structured_sentiment_llm(captured))
+        result = analyst(_make_sentiment_state())
+        assert len(result["messages"]) == 1
+        assert result["sentiment_report"] == result["messages"][0].content
+
+    def test_prompt_contains_ticker(self):
+        captured = {}
+        create_sentiment_analyst(_structured_sentiment_llm(captured))(_make_sentiment_state())
+        assert any("NVDA" in str(m) for m in captured["prompt"])
+
+    def test_falls_back_to_freetext_when_structured_unavailable(self):
+        plain = "**Overall Sentiment:** **Bearish** (Score: 3.0/10)\n**Confidence:** Low\n\nLimited data."
+        llm = MagicMock()
+        llm.with_structured_output.side_effect = NotImplementedError("provider unsupported")
+        llm.invoke.return_value = MagicMock(content=plain)
+        assert create_sentiment_analyst(llm)(_make_sentiment_state())["sentiment_report"] == plain
+
+    def test_falls_back_to_freetext_when_structured_call_fails(self):
+        plain = "Fallback free-text sentiment."
+        structured = MagicMock()
+        structured.invoke.side_effect = ValueError("bad JSON from model")
+        llm = MagicMock()
+        llm.with_structured_output.return_value = structured
+        llm.invoke.return_value = MagicMock(content=plain)
+        assert create_sentiment_analyst(llm)(_make_sentiment_state())["sentiment_report"] == plain

tradingagents/agents/analysts/sentiment_analyst.py

@@ -14,19 +14,31 @@ the LLM is invoked and injects them into the prompt as structured blocks:
   3. Reddit posts        — r/wallstreetbets, r/stocks, r/investing
 
 The agent does not use tool-calling; the data is in the prompt from
-turn 0. The LLM produces the sentiment report in a single invocation.
+turn 0. Output uses the structured-output pattern (json_schema for
+OpenAI/xAI, response_schema for Gemini, tool-use for Anthropic), falling
+back to free-text generation for providers that lack native support, so
+the sentiment header (band + score + confidence) is deterministic across
+runs and providers instead of free-form per-model prose.
 
 See: https://github.com/TauricResearch/TradingAgents/issues/557
+See: https://github.com/TauricResearch/TradingAgents/issues/796
 """
 
 from datetime import datetime, timedelta
 
+from langchain_core.messages import AIMessage
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+
+from tradingagents.agents.schemas import SentimentReport, render_sentiment_report
 from tradingagents.agents.utils.agent_utils import (
     get_instrument_context_from_state,
     get_language_instruction,
     get_news,
 )
+from tradingagents.agents.utils.structured import (
+    bind_structured,
+    invoke_structured_or_freetext,
+)
 from tradingagents.dataflows.reddit import fetch_reddit_posts
 from tradingagents.dataflows.stocktwits import fetch_stocktwits_messages
 
@@ -39,9 +51,11 @@ def create_sentiment_analyst(llm):
     """Create a sentiment analyst node for the trading graph.
 
     Pre-fetches news + StockTwits + Reddit data, injects them into the
-    prompt as structured blocks, and produces a sentiment report in a
+    prompt as structured blocks, and produces a deterministic sentiment
-    single LLM call.
+    report via structured output (with a free-text fallback for providers
+    that do not support it).
     """
+    structured_llm = bind_structured(llm, SentimentReport, "Sentiment Analyst")
 
     def sentiment_analyst_node(state):
         ticker = state["company_of_interest"]
@@ -83,14 +97,22 @@ def create_sentiment_analyst(llm):
         prompt = prompt.partial(current_date=end_date)
         prompt = prompt.partial(instrument_context=instrument_context)
 
-        # No bind_tools — the data is already in the prompt; a single LLM
+        # Format the template into a concrete message list so the structured
-        # call produces the report directly.
+        # and free-text paths receive the same input. No bind_tools — the
-        chain = prompt | llm
+        # data is already in the prompt.
-        result = chain.invoke(state["messages"])
+        formatted_messages = prompt.format_messages(messages=state["messages"])
+
+        report_text = invoke_structured_or_freetext(
+            structured_llm,
+            llm,
+            formatted_messages,
+            render_sentiment_report,
+            "Sentiment Analyst",
+        )
 
         return {
-            "messages": [result],
+            "messages": [AIMessage(content=report_text)],
-            "sentiment_report": result.content,
+            "sentiment_report": report_text,
         }
 
     return sentiment_analyst_node
@@ -143,21 +165,20 @@ Community discussion. Engagement signal via upvote score and comment count. Subr
 
 5. **Identify recurring narrative themes.** What topic keeps coming up across sources? That's the dominant narrative driving current sentiment.
 
-6. **Be honest about data limits.** If StockTwits returned only a handful of messages, or one or more sources returned an "<unavailable>" placeholder, the sentiment read is less robust — flag this caveat explicitly. If the sources are silent on a given subreddit, say so.
+6. **Be honest about data limits.** If StockTwits returned only a handful of messages, or one or more sources returned an "<unavailable>" placeholder, the sentiment read is less robust — flag this explicitly in the `confidence` field and the narrative. If the sources are silent on a given subreddit, say so.
 
 7. **Identify catalysts and risks** that emerge across sources — news of upcoming earnings, product launches, competitive threats, macro headlines, etc.
 
 8. **Past sentiment is not predictive.** Frame your conclusions as signal for the trader to weigh alongside fundamentals and technicals, not as a price call.
 
-## Output
+## Output fields
 
-Produce a sentiment report covering, in order:
+Fill the following fields:
 
-1. **Overall sentiment direction** — Bullish / Bearish / Neutral / Mixed — with a brief confidence note based on data quality and sample size.
+- **overall_band**: Exactly one of Bullish / Mildly Bullish / Neutral / Mixed / Mildly Bearish / Bearish. Use Mixed when sources point in clearly different directions; Neutral only when all sources are genuinely silent.
-2. **Source-by-source breakdown** — what each of news / StockTwits / Reddit is telling you, with specific evidence (cite message counts, ratios, notable posts).
+- **overall_score**: A number from 0 (maximally bearish) to 10 (maximally bullish); 5 is neutral. Keep it consistent with overall_band.
-3. **Divergences, alignments, and key narratives** across sources.
+- **confidence**: low / medium / high, based on data quality and sample size.
-4. **Catalysts and risks** surfaced by the data.
+- **narrative**: Full source-by-source breakdown, divergences, dominant narrative themes, catalysts and risks, and a markdown summary table of key sentiment signals (direction, source, supporting evidence).
-5. **Markdown table** at the end summarizing key sentiment signals, their direction, source, and supporting evidence.
 
 {get_language_instruction()}"""
 

tradingagents/agents/schemas.py

@@ -19,7 +19,7 @@ so that:
 from __future__ import annotations
 
 from enum import Enum
-from typing import Optional
+from typing import Literal, Optional
 
 from pydantic import BaseModel, Field
 
@@ -226,3 +226,92 @@ def render_pm_decision(decision: PortfolioDecision) -> str:
     if decision.time_horizon:
         parts.extend(["", f"**Time Horizon**: {decision.time_horizon}"])
     return "\n".join(parts)
+
+
+# ---------------------------------------------------------------------------
+# Sentiment Analyst
+# ---------------------------------------------------------------------------
+
+
+class SentimentBand(str, Enum):
+    """Discrete sentiment direction produced by the Sentiment Analyst.
+
+    Six tiers keep the signal granular enough to be actionable while remaining
+    small enough for every provider to map reliably from its JSON output.
+    """
+
+    BULLISH = "Bullish"
+    MILDLY_BULLISH = "Mildly Bullish"
+    NEUTRAL = "Neutral"
+    MIXED = "Mixed"
+    MILDLY_BEARISH = "Mildly Bearish"
+    BEARISH = "Bearish"
+
+
+class SentimentReport(BaseModel):
+    """Structured sentiment report produced by the Sentiment Analyst.
+
+    Replaces the previous free-form prose output so downstream consumers
+    (dashboards, audit logs, PDF renderers, other agents) can read
+    ``overall_band`` and ``overall_score`` without maintaining fragile regex
+    fallbacks that drift with every model release. ``narrative`` preserves the
+    rich source-by-source analysis; ``render_sentiment_report`` prepends a
+    deterministic header so the saved report stays human-readable.
+    """
+
+    overall_band: SentimentBand = Field(
+        description=(
+            "Overall sentiment direction. Exactly one of: "
+            "Bullish / Mildly Bullish / Neutral / Mixed / Mildly Bearish / Bearish. "
+            "Use Mixed when sources point in clearly different directions. "
+            "Use Neutral only when all sources are genuinely silent or non-committal."
+        ),
+    )
+    overall_score: float = Field(
+        ge=0.0,
+        le=10.0,
+        description=(
+            "Numeric sentiment intensity on a 0–10 scale. "
+            "0 = maximally bearish, 5 = neutral, 10 = maximally bullish. "
+            "Guideline for consistency with overall_band: "
+            "Bullish ~6.5–10, Mildly Bullish ~5.5–6.4, Neutral/Mixed ~4.5–5.5, "
+            "Mildly Bearish ~3.5–4.4, Bearish ~0–3.4. "
+            "Only the 0–10 bounds are enforced."
+        ),
+    )
+    confidence: Literal["low", "medium", "high"] = Field(
+        description=(
+            "Confidence in the assessment based on data quality and sample size. "
+            "Use 'low' when one or more sources returned a placeholder or fewer "
+            "than 5 data points; 'medium' when data is present but sparse; "
+            "'high' when all three sources returned substantive data."
+        ),
+    )
+    narrative: str = Field(
+        description=(
+            "Full sentiment report covering, in order: "
+            "(1) source-by-source breakdown with specific evidence (cite message "
+            "counts, ratios, notable posts); "
+            "(2) cross-source divergences and alignments; "
+            "(3) dominant narrative themes; "
+            "(4) catalysts and risks surfaced by the data; "
+            "(5) a markdown table summarising key sentiment signals, their "
+            "direction, source, and supporting evidence."
+        ),
+    )
+
+
+def render_sentiment_report(report: SentimentReport) -> str:
+    """Render a SentimentReport to the markdown shape the rest of the system expects.
+
+    The structured header (band + score + confidence) is prepended to the
+    narrative so the saved report is both human-readable and machine-parseable
+    without regex.
+    """
+    return "\n".join([
+        f"**Overall Sentiment:** **{report.overall_band.value}** "
+        f"(Score: {report.overall_score:.1f}/10)",
+        f"**Confidence:** {report.confidence.capitalize()}",
+        "",
+        report.narrative,
+    ])