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

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
-    single LLM call.
+    prompt as structured blocks, and produces a deterministic sentiment
+    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
-        # call produces the report directly.
-        chain = prompt | llm
-        result = chain.invoke(state["messages"])
+        # Format the template into a concrete message list so the structured
+        # and free-text paths receive the same input. No bind_tools — the
+        # data is already in the prompt.
+        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],
-            "sentiment_report": result.content,
+            "messages": [AIMessage(content=report_text)],
+            "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.
-2. **Source-by-source breakdown** — what each of news / StockTwits / Reddit is telling you, with specific evidence (cite message counts, ratios, notable posts).
-3. **Divergences, alignments, and key narratives** across sources.
-4. **Catalysts and risks** surfaced by the data.
-5. **Markdown table** at the end summarizing key sentiment signals, their direction, source, and supporting evidence.
+- **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.
+- **overall_score**: A number from 0 (maximally bearish) to 10 (maximally bullish); 5 is neutral. Keep it consistent with overall_band.
+- **confidence**: low / medium / high, based on data quality and sample size.
+- **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).
 
 {get_language_instruction()}"""