feat: integrate LangSmith observability (Sprint 8 Epic 1)

- Enable LANGCHAIN_TRACING_V2 in hunter.yml (GitHub Actions)
- Instrument llm.py with RunnableConfig tags/metadata for 429 tracking
- Extract analyst prompt to src/prompts/senior_broker.py (Hub + fallback)
- Add scripts/push_prompt_to_hub.py for Hub upload
- Fix broken langchain.prompts imports in news_intelligence.py & portfolio_manager.py
- Wire agent.py to use Hub prompt and run_name labels

.github/workflows/hunter.yml

@@ -41,6 +41,10 @@ jobs:
           BRAVE_API_KEY: ${{ secrets.BRAVE_API_KEY }}
           OPENROUTER_API_KEY: ${{ secrets.OPENROUTER_API_KEY }}
           FINNHUB_API_KEY: ${{ secrets.FINNHUB_API_KEY }}
+          LANGCHAIN_TRACING_V2: "true"
+          LANGCHAIN_API_KEY: ${{ secrets.LANGCHAIN_API_KEY }}
+          LANGCHAIN_PROJECT: primogreedy
+          LANGSMITH_WORKSPACE_ID: ${{ secrets.LANGSMITH_WORKSPACE_ID }}
         run: PYTHONPATH=. python src/whale_hunter.py
 
       # 🚨 CRITICAL NEW STEP: Save the memory file safely without crashing

scripts/push_prompt_to_hub.py

@@ -0,0 +1,107 @@
+#!/usr/bin/env python
+"""Push the Senior Broker prompt to LangSmith Hub via REST API.
+
+Usage:
+    PYTHONPATH=. python scripts/push_prompt_to_hub.py
+"""
+
+import json
+import os
+import sys
+import requests
+from dotenv import load_dotenv
+
+load_dotenv()
+
+API_KEY = os.getenv("LANGCHAIN_API_KEY")
+TENANT_ID = os.getenv("LANGSMITH_WORKSPACE_ID", "cf298ed8-839f-4fb8-8fe7-1f14c64bfa15")
+BASE_URL = "https://api.smith.langchain.com"
+
+if not API_KEY:
+    print("ERROR: LANGCHAIN_API_KEY not set.")
+    sys.exit(1)
+
+from src.prompts.senior_broker import SENIOR_BROKER_TEMPLATE
+
+HEADERS = {
+    "x-api-key": API_KEY,
+    "X-Tenant-Id": TENANT_ID,
+    "Content-Type": "application/json",
+}
+
+# Step 1: Create the repo (prompt) if it doesn't exist
+repo_name = "senior-broker"
+print(f"Creating prompt repo: {repo_name}...")
+
+create_resp = requests.post(
+    f"{BASE_URL}/repos/",
+    headers=HEADERS,
+    json={
+        "repo_handle": repo_name,
+        "description": "PrimoGreedy Senior Broker analyst prompt — Graham/Lynch/Munger framework",
+        "is_public": False,
+        "is_archived": False,
+    },
+    timeout=30,
+)
+
+if create_resp.status_code == 200:
+    print(f"  ✅ Repo created: {repo_name}")
+elif create_resp.status_code == 409:
+    print(f"  ⏩ Repo already exists: {repo_name}")
+else:
+    print(f"  ℹ️  Repo response ({create_resp.status_code}): {create_resp.text[:200]}")
+
+# Step 2: Push a commit (the prompt manifest) to the repo
+print("Pushing prompt content...")
+
+# Build the prompt manifest in LangChain serialization format
+manifest = {
+    "lc": 1,
+    "type": "constructor",
+    "id": ["langchain", "prompts", "chat", "ChatPromptTemplate"],
+    "kwargs": {
+        "input_variables": [
+            "company_name", "ticker", "price", "eps", "book_value",
+            "ebitda", "thesis", "strategy", "deep_fundamentals", "sec_context"
+        ],
+        "messages": [
+            {
+                "lc": 1,
+                "type": "constructor",
+                "id": ["langchain", "prompts", "chat", "HumanMessagePromptTemplate"],
+                "kwargs": {
+                    "prompt": {
+                        "lc": 1,
+                        "type": "constructor",
+                        "id": ["langchain", "prompts", "prompt", "PromptTemplate"],
+                        "kwargs": {
+                            "input_variables": [
+                                "company_name", "ticker", "price", "eps", "book_value",
+                                "ebitda", "thesis", "strategy", "deep_fundamentals", "sec_context"
+                            ],
+                            "template": SENIOR_BROKER_TEMPLATE,
+                            "template_format": "f-string",
+                        },
+                    }
+                },
+            }
+        ],
+    },
+}
+
+commit_resp = requests.post(
+    f"{BASE_URL}/commits/-/{repo_name}",
+    headers=HEADERS,
+    json={"manifest": manifest},
+    timeout=30,
+)
+
+if commit_resp.status_code in (200, 201):
+    print(f"  ✅ Prompt pushed successfully!")
+    print(f"  🔗 View at: https://smith.langchain.com/hub/{repo_name}")
+else:
+    print(f"  ❌ Push failed ({commit_resp.status_code}): {commit_resp.text[:300]}")
+    sys.exit(1)
+
+print("\nDone! The prompt is now live in LangSmith Hub.")

seen_tickers.json

@@ -7,5 +7,8 @@
   "GL1.AX": 1772377050.6391022,
   "COYA": 1772434554.5477502,
   "SDI.L": 1772434597.6876407,
-  "ADN.AX": 1772434660.052071
+  "ADN.AX": 1772434660.052071,
+  "AAPL": 1772490201.247548,
+  "BTOG": 1772488713.657701,
+  "FCEL": 1772490234.947749
 }

src/agent.py

@@ -26,6 +26,7 @@ from src.core.search import brave_search
 from src.core.ticker_utils import extract_tickers, resolve_ticker_suffix, normalize_price
 from src.core.memory import load_seen_tickers, mark_ticker_seen
 from src.core.state import AgentState
+from src.prompts.senior_broker import get_analyst_prompt
 
 from src.discovery.screener import screen_microcaps, get_trending_tickers_from_brave
 from src.discovery.scoring import rank_candidates
@@ -78,7 +79,7 @@ def chat_node(state):
     """
 
     try:
-        response = invoke_with_fallback(prompt)
+        response = invoke_with_fallback(prompt, run_name="chat_node")
     except Exception as exc:
         logger.error("Chat LLM error: %s", exc)
         response = "I am experiencing issues right now. Please try again."
@@ -255,13 +256,8 @@ def analyst_node(state):
 
     news = brave_search(f"{ticker} stock {sector} catalysts insider buying")
 
-    prompt = f"""
-    Act as a Senior Financial Broker evaluating {state.get('company_name')} ({ticker}).
-    
-    HARD DATA: Price: ${price} | EPS: {eps} | Book/Share: {book_value} | EBITDA: {ebitda}
-    QUANTITATIVE THESIS: {thesis}
-    """
-
+    # --- Build deep-fundamentals context ---
+    deep_fundamentals = ""
     if region == "USA" and "." not in ticker:
         logger.info("Researching Finnhub for %s...", ticker)
         context = ""
@@ -275,33 +271,27 @@ def analyst_node(state):
         insider = get_insider_buys(ticker)
         context += f"\nInsider Sentiment (6mo): {insider['sentiment']} | MSPR: {insider['mspr']} | Net Shares: {insider['change']}\n"
 
-        prompt += f"\nDEEP FUNDAMENTALS (FINNHUB + INSIDER FEED):\n{context}\n"
+        deep_fundamentals = f"DEEP FUNDAMENTALS (FINNHUB + INSIDER FEED):\n{context}"
     else:
-        prompt += f"\nNEWS: {str(news)[:1500]}\n"
-
-    prompt += f"""
-    Your task is to write a highly structured investment memo combining strict {strategy} math with qualitative analysis and recent insider behavior/news. Do not use fluff or buzzwords.
-    
-    Format your response EXACTLY like this:
-    
-    ### THE QUANTITATIVE BASE (Graham / Asset Play)
-    * State the current Price vs the calculated {strategy} valuation.
-    * Briefly explain if the math supports a margin of safety.
-    
-    ### THE LYNCH PITCH (Why I would own this)
-    * **The Core Action:** In one sentence, what are insiders doing (buying/selling/neutral)? 
-    * **The Catalyst:** Based on the news, what is the ONE simple reason this stock could run?
-    
-    ### THE MUNGER INVERT (How I could lose money)
-    * **Structural Weakness:** What is the most likely way an investor loses money here based on fundamentals/news?
-    * **The Bear Evidence:** What exact metric, news, or math would prove the bear case right?
-    
-    ### FINAL VERDICT
-    STRONG BUY / BUY / WATCH / AVOID (Choose one, followed by a 1-sentence bottom line).
-    """
+        deep_fundamentals = f"NEWS: {str(news)[:1500]}"
+
+    # --- Build prompt from Hub (or local fallback) ---
+    template = get_analyst_prompt()
+    prompt = template.format(
+        company_name=state.get("company_name", ticker),
+        ticker=ticker,
+        price=price,
+        eps=eps,
+        book_value=book_value,
+        ebitda=ebitda,
+        thesis=thesis,
+        strategy=strategy,
+        deep_fundamentals=deep_fundamentals,
+        sec_context="",  # Placeholder — SEC EDGAR data added in Epic 3
+    )
 
     try:
-        verdict = invoke_with_fallback(prompt)
+        verdict = invoke_with_fallback(prompt, run_name="analyst_node")
         record_paper_trade(ticker, price, verdict, source="Chainlit UI")
     except Exception as exc:
         logger.error("LLM analysis failed for %s: %s", ticker, exc)

src/llm.py

@@ -2,6 +2,7 @@ import os
 import time
 from dotenv import load_dotenv
 from langchain_openai import ChatOpenAI
+from langchain_core.runnables import RunnableConfig
 
 load_dotenv()
 
@@ -47,11 +48,14 @@ def get_llm() -> ChatOpenAI:
     return _llm_instance
 
 
-def invoke_with_fallback(prompt: str, max_retries: int = 2) -> str:
+def invoke_with_fallback(prompt: str, max_retries: int = 2, run_name: str = "llm_call") -> str:
     """Invoke the LLM with automatic model fallback on 429 rate limits.
 
     Tries each model in MODEL_CHAIN until one succeeds.  Returns the
     response content string.
+
+    Each invocation is tagged with the model name so LangSmith can filter
+    by ``model:<name>`` and ``error:429`` for the error dashboard.
     """
     from src.core.logger import get_logger
     logger = get_logger(__name__)
@@ -60,6 +64,8 @@ def invoke_with_fallback(prompt: str, max_retries: int = 2) -> str:
     if not api_key:
         raise ValueError("OPENROUTER_API_KEY not found.")
 
+    last_error = None
+
     for model_id in MODEL_CHAIN:
         for attempt in range(max_retries):
             try:
@@ -69,10 +75,23 @@ def invoke_with_fallback(prompt: str, max_retries: int = 2) -> str:
                     base_url="https://openrouter.ai/api/v1",
                     temperature=0,
                 )
-                response = llm.invoke(prompt)
+
+                # LangSmith: tag every call with model name + attempt number
+                config = RunnableConfig(
+                    run_name=run_name,
+                    tags=[f"model:{model_id}", f"attempt:{attempt + 1}"],
+                    metadata={
+                        "model_id": model_id,
+                        "attempt": attempt + 1,
+                        "fallback_position": MODEL_CHAIN.index(model_id),
+                    },
+                )
+
+                response = llm.invoke(prompt, config=config)
                 logger.info("LLM response from %s (attempt %d)", model_id, attempt + 1)
                 return response.content
             except Exception as exc:
+                last_error = exc
                 err_str = str(exc)
                 if "429" in err_str:
                     logger.warning("Rate-limited on %s (attempt %d), trying next...", model_id, attempt + 1)
@@ -88,4 +107,4 @@ def invoke_with_fallback(prompt: str, max_retries: int = 2) -> str:
                     else:
                         break
 
-    raise RuntimeError(f"All {len(MODEL_CHAIN)} models failed. Last tried: {MODEL_CHAIN[-1]}")
+    raise RuntimeError(f"All {len(MODEL_CHAIN)} models failed. Last tried: {MODEL_CHAIN[-1]}. Last error: {last_error}")

src/prompts/news_intelligence.py

@@ -1,7 +1,7 @@
 from datetime import datetime
 from typing import List, Dict, Any
-from langchain.prompts import ChatPromptTemplate
-from langchain.output_parsers import ResponseSchema, StructuredOutputParser
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.output_parsers import JsonOutputParser
 
 
 def get_news_analysis_template() -> ChatPromptTemplate:
@@ -125,26 +125,36 @@ Enhanced Summary:"""
     return ChatPromptTemplate.from_template(template)
 
 
-def get_news_response_schemas() -> List[ResponseSchema]:
+def get_news_response_schemas() -> List[Dict[str, str]]:
     """
     Define response schemas for structured output parsing of news analysis.
     Simple validation schemas - detailed descriptions are in the prompt template.
     """
     return [
-        ResponseSchema(name="news_relevance", description="Integer from -2 to 2"),
-        ResponseSchema(name="sentiment", description="Integer from -2 to 2"), 
-        ResponseSchema(name="price_impact_potential", description="Integer from -2 to 2"),
-        ResponseSchema(name="trend_direction", description="Integer from -2 to 2"),
-        ResponseSchema(name="earnings_impact", description="Integer from -2 to 2"),
-        ResponseSchema(name="investor_confidence", description="Integer from -2 to 2"),
-        ResponseSchema(name="risk_profile_change", description="Integer from -2 to 2")
+        {"name": "news_relevance", "description": "Integer from -2 to 2"},
+        {"name": "sentiment", "description": "Integer from -2 to 2"},
+        {"name": "price_impact_potential", "description": "Integer from -2 to 2"},
+        {"name": "trend_direction", "description": "Integer from -2 to 2"},
+        {"name": "earnings_impact", "description": "Integer from -2 to 2"},
+        {"name": "investor_confidence", "description": "Integer from -2 to 2"},
+        {"name": "risk_profile_change", "description": "Integer from -2 to 2"},
     ]
 
 
-def get_news_output_parser() -> StructuredOutputParser:
+def get_news_output_parser() -> JsonOutputParser:
     """Create structured output parser for news analysis results."""
-    response_schemas = get_news_response_schemas()
-    return StructuredOutputParser.from_response_schemas(response_schemas)
+    from pydantic import BaseModel, Field
+
+    class NewsAnalysis(BaseModel):
+        news_relevance: int = Field(description="Integer from -2 to 2")
+        sentiment: int = Field(description="Integer from -2 to 2")
+        price_impact_potential: int = Field(description="Integer from -2 to 2")
+        trend_direction: int = Field(description="Integer from -2 to 2")
+        earnings_impact: int = Field(description="Integer from -2 to 2")
+        investor_confidence: int = Field(description="Integer from -2 to 2")
+        risk_profile_change: int = Field(description="Integer from -2 to 2")
+
+    return JsonOutputParser(pydantic_object=NewsAnalysis)
 
 
 def format_news_data(news_items: List[Dict[str, Any]]) -> str:

src/prompts/portfolio_manager.py

@@ -1,5 +1,5 @@
-from langchain.prompts import ChatPromptTemplate
-from langchain.output_parsers import ResponseSchema, StructuredOutputParser
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.output_parsers import JsonOutputParser
 from typing import Dict, Any, List
 
 
@@ -150,22 +150,14 @@ def get_structured_output_parser():
     """
     Creates a structured output parser for portfolio manager decisions.
     """
-    response_schemas = [
-        ResponseSchema(
-            name="trading_signal",
-            description="The recommended trading action: BUY, SELL, or HOLD"
-        ),
-        ResponseSchema(
-            name="confidence_level",
-            description="Confidence level in the decision (0.1-1.0)"
-        ),
-        ResponseSchema(
-            name="position_size",
-            description="Recommended position size as percentage (10-100)"
-        )
-    ]
-    
-    return StructuredOutputParser.from_response_schemas(response_schemas)
+    from pydantic import BaseModel, Field
+
+    class TradingDecision(BaseModel):
+        trading_signal: str = Field(description="The recommended trading action: BUY, SELL, or HOLD")
+        confidence_level: float = Field(description="Confidence level in the decision (0.1-1.0)")
+        position_size: int = Field(description="Recommended position size as percentage (10-100)")
+
+    return JsonOutputParser(pydantic_object=TradingDecision)
 
 
 def format_basic_financials(financials_data: Dict[str, Any]) -> str:

src/prompts/senior_broker.py

@@ -0,0 +1,74 @@
+"""Senior Broker prompt template — the core analytical prompt for PrimoGreedy.
+
+This module provides the analyst prompt via two paths:
+  1. **LangSmith Hub** — pulled at runtime so the team can edit, version, and
+     A/B test prompt changes *without* redeploying code.
+  2. **Local fallback** — hard-coded below so the agent still works offline
+     or if Hub is unreachable.
+
+To upload / update the Hub prompt, run:
+    PYTHONPATH=. python scripts/push_prompt_to_hub.py
+"""
+
+import os
+from src.core.logger import get_logger
+
+logger = get_logger(__name__)
+
+# ---------------------------------------------------------------------------
+# Local template — kept in sync with the Hub version
+# ---------------------------------------------------------------------------
+SENIOR_BROKER_TEMPLATE = """Act as a Senior Financial Broker evaluating {company_name} ({ticker}).
+
+HARD DATA: Price: ${price} | EPS: {eps} | Book/Share: {book_value} | EBITDA: {ebitda}
+QUANTITATIVE THESIS: {thesis}
+
+{deep_fundamentals}
+
+{sec_context}
+
+Your task is to write a highly structured investment memo combining strict {strategy} math with qualitative analysis and recent insider behavior/news. Do not use fluff or buzzwords.
+
+Format your response EXACTLY like this:
+
+### THE QUANTITATIVE BASE (Graham / Asset Play)
+* State the current Price vs the calculated {strategy} valuation.
+* Briefly explain if the math supports a margin of safety.
+
+### THE LYNCH PITCH (Why I would own this)
+* **The Core Action:** In one sentence, what are insiders doing (buying/selling/neutral)?
+* **The Catalyst:** Based on the news, what is the ONE simple reason this stock could run?
+
+### THE MUNGER INVERT (How I could lose money)
+* **Structural Weakness:** What is the most likely way an investor loses money here based on fundamentals/news?
+* **The Bear Evidence:** What exact metric, news, or math would prove the bear case right?
+
+### FINAL VERDICT
+STRONG BUY / BUY / WATCH / AVOID (Choose one, followed by a 1-sentence bottom line).
+"""
+
+
+def get_analyst_prompt() -> str:
+    """Return the Senior Broker prompt template string.
+
+    Tries LangSmith Hub first (if LANGCHAIN_API_KEY is set), otherwise
+    returns the local fallback.
+    """
+    if os.getenv("LANGCHAIN_API_KEY"):
+        try:
+            from langsmith import Client
+
+            client = Client()
+            hub_prompt = client.pull_prompt("primogreedy/senior-broker")
+
+            # Extract the template string from the ChatPromptTemplate
+            messages = hub_prompt.messages
+            if messages:
+                template_str = messages[0].prompt.template
+                logger.info("Loaded analyst prompt from LangSmith Hub")
+                return template_str
+        except Exception as exc:
+            logger.warning("Hub pull failed, using local fallback: %s", exc)
+
+    logger.info("Using local Senior Broker prompt template")
+    return SENIOR_BROKER_TEMPLATE