diff --git a/tests/test_post_screen.py b/tests/test_post_screen.py
new file mode 100644
index 000000000..11c11fe42
--- /dev/null
+++ b/tests/test_post_screen.py
@@ -0,0 +1,288 @@
+"""Jev post screening, against TypeSafe's documented request and response shapes."""
+
+import pytest
+import requests
+
+from tradingagents.agents import post_screen as typesafe
+
+QUESTIONS = {"is_urgent": {"type": "noul", "instructions": "Does this convey urgency?"}}
+ANSWERS = {"is_urgent": {"type": "noul", "noul": 0.95}}
+
+
+class _Response:
+    def __init__(self, status, payload=None, headers=None):
+        self.status_code = status
+        self._payload = payload
+        self.headers = headers or {}
+
+    def json(self):
+        if self._payload is None:
+            raise ValueError("no JSON")
+        return self._payload
+
+
+class _Calls(list):
+    def __init__(self):
+        super().__init__()
+        self.queue = []
+        self.sleeps = []
+
+
+@pytest.fixture
+def post(monkeypatch):
+    """Queue responses on ``.queue``; the list records each call to requests.post."""
+    calls = _Calls()
+    queue = calls.queue
+
+    def fake_post(url, **kwargs):
+        calls.append((url, kwargs))
+        item = queue.pop(0)
+        if isinstance(item, Exception):
+            raise item
+        return item
+
+    monkeypatch.setattr(typesafe.requests, "post", fake_post)
+    monkeypatch.setattr(typesafe.time, "sleep", calls.sleeps.append)
+    monkeypatch.setenv("TYPESAFE_API_KEY", "ts-test")
+    monkeypatch.delenv("TYPESAFE_DEFAULT_MODEL", raising=False)
+    return calls
+
+
+def _ok():
+    return _Response(200, {"model": "jev-1.13.0", "answers": ANSWERS,
+                           "usage": {"input_tokens": 296, "output_tokens": 20}})
+
+
+@pytest.mark.unit
+def test_sends_the_documented_request_and_returns_the_answers(post):
+    post.queue.append(_ok())
+
+    assert typesafe.system_one("Help! My payouts have been failing.", QUESTIONS) == ANSWERS
+
+    url, kwargs = post[0]
+    assert url == "https://api.typesafe.ai/v1/systemone"
+    assert kwargs["headers"]["Authorization"] == "Bearer ts-test"
+    assert kwargs["json"] == {"state": "Help! My payouts have been failing.",
+                              "model": "jev-latest", "questions": QUESTIONS}
+
+
+@pytest.mark.unit
+def test_the_model_follows_the_sdk_environment(post, monkeypatch):
+    monkeypatch.setenv("TYPESAFE_DEFAULT_MODEL", "jev-1.13.0")
+    post.queue.append(_ok())
+
+    typesafe.system_one("s", QUESTIONS)
+
+    assert post[0][1]["json"]["model"] == "jev-1.13.0"
+
+
+@pytest.mark.unit
+@pytest.mark.parametrize("transient", [
+    _Response(429), _Response(529), requests.ConnectionError(), requests.Timeout(),
+    requests.exceptions.ChunkedEncodingError(),
+])
+def test_rate_limits_overload_and_dropped_connections_are_retried(post, transient):
+    post.queue.extend([transient, _ok()])
+
+    assert typesafe.system_one("s", QUESTIONS) == ANSWERS
+    assert len(post) == 2
+
+
+@pytest.mark.unit
+def test_a_retry_after_header_sets_the_wait(post):
+    post.queue.extend([_Response(429, headers={"retry-after": "7"}), _ok()])
+
+    typesafe.system_one("s", QUESTIONS)
+
+    assert post.sleeps == [7.0]
+
+
+@pytest.mark.unit
+def test_a_long_retry_after_is_capped(post):
+    post.queue.extend([_Response(529, headers={"retry-after": "600"}), _ok()])
+
+    typesafe.system_one("s", QUESTIONS)
+
+    assert post.sleeps == [typesafe._MAX_WAIT]
+
+
+@pytest.mark.unit
+def test_retries_are_bounded(post):
+    post.queue.extend([_Response(529)] * 3)
+
+    with pytest.raises(typesafe.TypeSafeError, match="HTTP 529"):
+        typesafe.system_one("s", QUESTIONS)
+    assert len(post) == 3
+
+
+@pytest.mark.unit
+@pytest.mark.parametrize("error", [requests.exceptions.InvalidHeader(), requests.exceptions.TooManyRedirects()])
+def test_other_request_errors_are_screening_failures_without_retry(post, error):
+    post.queue.append(error)
+
+    with pytest.raises(typesafe.TypeSafeError, match=type(error).__name__):
+        typesafe.system_one("s", QUESTIONS)
+    assert len(post) == 1
+
+
+@pytest.mark.unit
+@pytest.mark.parametrize("status", [401, 422, 500])
+def test_other_failures_raise_without_retry(post, status):
+    post.queue.append(_Response(status))
+
+    with pytest.raises(typesafe.TypeSafeError, match=f"HTTP {status}"):
+        typesafe.system_one("s", QUESTIONS)
+    assert len(post) == 1
+
+
+@pytest.mark.unit
+@pytest.mark.parametrize("payload", [
+    None, {"model": "jev"}, {"answers": {"other": {}}},
+    {"answers": {"is_urgent": {"type": "choice", "choice": "yes"}}},
+])
+def test_a_response_without_every_answer_is_an_error(post, payload):
+    post.queue.append(_Response(200, payload))
+
+    with pytest.raises(typesafe.TypeSafeError, match="malformed"):
+        typesafe.system_one("s", QUESTIONS)
+
+
+
+def _post_answers(about: float, stance: str = "bullish", confidence: float = 0.9):
+    return _Response(200, {"model": "jev-1.13.0", "answers": {
+        "about": {"type": "noul", "noul": about},
+        "stance": {"type": "choice", "choice": stance, "confidence": confidence,
+                   "probabilities": {stance: 1.0}},
+    }})
+
+
+@pytest.fixture
+def jev(post, monkeypatch):
+    """Answer each post by its text: ``post`` maps text -> response."""
+    answers = {}
+
+    def fake_post(url, **kwargs):
+        post.append((url, kwargs))
+        return answers[kwargs["json"]["state"]["post"]]
+
+    monkeypatch.setattr(typesafe.requests, "post", fake_post)
+    monkeypatch.setattr(typesafe, "resolve_instrument_identity",
+                        lambda t: {"company_name": "NVIDIA Corporation"})
+    return answers
+
+
+@pytest.mark.unit
+def test_no_key_no_screen(monkeypatch):
+    monkeypatch.delenv("TYPESAFE_API_KEY", raising=False)
+    assert typesafe.jev_screen("NVDA") is None
+
+
+@pytest.mark.unit
+def test_each_post_is_its_own_state_under_the_fixed_questions(jev, post):
+    jev["NVDA to 200"] = _post_answers(0.9)
+
+    typesafe.jev_screen("NVDA")(["NVDA to 200"])
+
+    body = post[0][1]["json"]
+    assert body["state"] == {"instrument": "NVIDIA Corporation (NVDA)", "post": "NVDA to 200"}
+    assert body["questions"] == typesafe.QUESTIONS
+
+
+@pytest.mark.unit
+def test_screen_drops_clear_off_topic_posts_and_counts_confident_stances(jev):
+    jev.update({
+        "long NVDA": _post_answers(0.95, "bullish"),
+        "NVDA puts": _post_answers(0.9, "bearish"),
+        "maybe NVDA": _post_answers(0.4, "neutral"),       # uncertain relevance: kept
+        "NVDA?": _post_answers(0.8, "bullish", 0.3),       # uncertain stance: unclear
+        "NVDA!": _post_answers(0.8, "sideways"),           # not an option: unclear
+        "$AAPL $MSFT $NVDA pump": _post_answers(0.1, "bullish"),
+    })
+
+    keep, note = typesafe.jev_screen("NVDA")(list(jev))
+
+    assert keep == [True, True, True, True, True, False]
+    assert note == ("Screened by Jev: 5 of the 6 posts fetched are about NVIDIA Corporation (NVDA); "
+                    "their stance on its stock: 1 bullish, 1 bearish, 1 neutral, 2 unclear.")
+
+
+@pytest.mark.unit
+def test_one_failed_request_leaves_every_post_unscreened(jev):
+    jev.update({"a": _post_answers(0.1), "b": _Response(401)})
+
+    keep, note = typesafe.jev_screen("NVDA")(["a", "b"])
+
+    assert keep == [True, True]
+    assert note == "<Jev screening unavailable (HTTP 401); posts are unscreened>"
+
+
+@pytest.mark.unit
+def test_an_answer_missing_its_fields_reads_as_malformed(jev):
+    jev["a"] = _Response(200, {"answers": {"about": {"type": "noul"},
+                                           "stance": {"type": "choice"}}})
+
+    keep, note = typesafe.jev_screen("NVDA")(["a"])
+
+    assert keep == [True]
+    assert note == "<Jev screening unavailable (malformed response); posts are unscreened>"
+
+
+@pytest.mark.unit
+def test_the_first_failure_cancels_the_requests_not_yet_sent(jev, post, monkeypatch):
+    monkeypatch.setattr(typesafe, "_WORKERS", 1)
+    jev.update({"a": _Response(401), **{f"p{i}": _post_answers(0.9) for i in range(20)}})
+
+    typesafe.jev_screen("NVDA")(list(jev))
+
+    assert len(post) < 21
+
+
+@pytest.mark.unit
+def test_the_sentiment_analyst_hands_the_screen_to_both_social_fetchers(monkeypatch):
+    from langchain_core.messages import AIMessage
+
+    from tradingagents.agents.analysts import sentiment_analyst
+
+    screen = object()
+    seen = []
+    monkeypatch.setattr(sentiment_analyst, "jev_screen", lambda ticker: screen)
+    monkeypatch.setattr(sentiment_analyst.get_news, "func", lambda *a: "news")
+    for name in ("fetch_stocktwits_messages", "fetch_reddit_posts"):
+        monkeypatch.setattr(sentiment_analyst, name, lambda *a, screen=None, **k: seen.append(screen) or "")
+
+    class _LLM:
+        def with_structured_output(self, *a, **k):
+            raise NotImplementedError
+
+        def invoke(self, messages):
+            return AIMessage(content="report")
+
+    node = sentiment_analyst.create_sentiment_analyst(_LLM())
+    node({"company_of_interest": "NVDA", "trade_date": "2026-01-09", "messages": []})
+
+    assert seen == [screen, screen]
+
+
+@pytest.mark.unit
+def test_a_failure_does_not_wait_for_requests_still_in_flight(jev, monkeypatch):
+    import threading
+    import time
+
+    release = threading.Event()
+
+    class _Slow:
+        status_code = 200
+        headers = {}
+
+        def json(self):
+            release.wait(5)
+            return _post_answers(0.9).json()
+
+    jev.update({"slow": _Slow(), "bad": _Response(401)})
+    started = time.monotonic()
+    keep, note = typesafe.jev_screen("NVDA")(["slow", "bad"])
+    elapsed = time.monotonic() - started
+    release.set()
+
+    assert keep == [True, True] and "unavailable" in note
+    assert elapsed < 2
diff --git a/tests/test_reddit_fallback.py b/tests/test_reddit_fallback.py
index 77d50c1b8..2bd630fd2 100644
--- a/tests/test_reddit_fallback.py
+++ b/tests/test_reddit_fallback.py
@@ -271,3 +271,43 @@ def test_empty_subreddit_on_a_full_page_is_not_called_empty():
         out = reddit.fetch_reddit_posts("NVDA", subreddits=("a", "b"))
     assert "r/b: <no posts found" not in out
     assert f"newest {reddit._FEED_PAGE}" in out
+
+
+def _screen_out(*dropped):
+    """A screen that drops posts whose text starts with one of ``dropped``."""
+    def screen(texts):
+        return [not t.startswith(dropped) for t in texts], "Screened: note"
+    return screen
+
+
+@pytest.mark.unit
+def test_screened_out_posts_free_their_subreddit_slots():
+    posts = [{"title": t, "created_utc": None, "selftext": "", "subreddit": "a"}
+             for t in ("SPAM1", "SPAM2", "A1", "A2")]
+    with patch.object(reddit, "_fetch_subreddit_rss", return_value=posts):
+        out = reddit.fetch_reddit_posts("NVDA", subreddits=("a",), limit_per_sub=2,
+                                        screen=_screen_out("SPAM"))
+    assert out.startswith("Screened: note")
+    assert "A1" in out and "A2" in out and "SPAM" not in out
+
+
+@pytest.mark.unit
+def test_a_subreddit_emptied_by_screening_is_not_called_empty():
+    posts = [{"title": "SPAM", "created_utc": None, "selftext": "", "subreddit": "b"},
+             {"title": "A1", "created_utc": None, "selftext": "", "subreddit": "a"}]
+    with patch.object(reddit, "_fetch_subreddit_rss", return_value=posts):
+        out = reddit.fetch_reddit_posts("NVDA", subreddits=("a", "b"), screen=_screen_out("SPAM"))
+    assert "r/b: <no posts about NVDA after screening>" in out
+
+
+@pytest.mark.unit
+def test_an_unavailable_screen_keeps_every_post_and_says_so():
+    posts = [{"title": "A1", "created_utc": None, "selftext": "", "subreddit": "a"}]
+
+    def unavailable(texts):
+        return [True] * len(texts), "<Jev screening unavailable (HTTP 529); posts are unscreened>"
+
+    with patch.object(reddit, "_fetch_subreddit_rss", return_value=posts):
+        screened = reddit.fetch_reddit_posts("NVDA", subreddits=("a", "b"), screen=unavailable)
+        plain = reddit.fetch_reddit_posts("NVDA", subreddits=("a", "b"))
+    assert screened == "<Jev screening unavailable (HTTP 529); posts are unscreened>\n\n" + plain
diff --git a/tests/test_stocktwits_resilience.py b/tests/test_stocktwits_resilience.py
index 429c0a138..6975b68b7 100644
--- a/tests/test_stocktwits_resilience.py
+++ b/tests/test_stocktwits_resilience.py
@@ -8,6 +8,7 @@ transport error must degrade to a placeholder rather than raise.
 from __future__ import annotations
 
 import http.client
+import json
 from unittest.mock import patch
 from urllib.error import HTTPError
 
@@ -75,3 +76,41 @@ class TestStockTwitsCryptoSymbols:
         with patch.object(stocktwits, "urlopen", side_effect=fake_urlopen):
             stocktwits.fetch_stocktwits_messages("BTC-USD")
         assert "/symbol/BTC.X.json" in seen["url"]
+
+
+def _stream(*bodies):
+    payload = {"messages": [
+        {"body": b, "created_at": "2026-01-09T15:00:00Z", "user": {"username": "u"},
+         "entities": {"sentiment": {"basic": "Bullish"}}}
+        for b in bodies
+    ]}
+
+    class _Resp:
+        def __enter__(self):
+            return self
+
+        def __exit__(self, *a):
+            return False
+
+        def read(self):
+            return json.dumps(payload).encode()
+    return _Resp()
+
+
+def _drop_spam(texts):
+    return [not t.startswith("SPAM") for t in texts], "Screened: note"
+
+
+@pytest.mark.unit
+class TestStockTwitsScreening:
+    def test_screened_out_messages_leave_the_block_and_its_counts(self):
+        with patch.object(stocktwits, "urlopen", return_value=_stream("SPAM", "long NVDA")):
+            out = stocktwits.fetch_stocktwits_messages("NVDA", screen=_drop_spam)
+        assert out.startswith("Screened: note")
+        assert "long NVDA" in out and "SPAM" not in out
+        assert "Total: 1 most-recent" in out
+
+    def test_all_screened_out_is_not_called_empty(self):
+        with patch.object(stocktwits, "urlopen", return_value=_stream("SPAM")):
+            out = stocktwits.fetch_stocktwits_messages("NVDA", screen=_drop_spam)
+        assert "none of the 1 StockTwits messages is about $NVDA" in out
diff --git a/tradingagents/agents/analysts/sentiment_analyst.py b/tradingagents/agents/analysts/sentiment_analyst.py
index 82f31f65a..ff2cf3623 100644
--- a/tradingagents/agents/analysts/sentiment_analyst.py
+++ b/tradingagents/agents/analysts/sentiment_analyst.py
@@ -7,7 +7,8 @@ prompt, so the model reports on data it was given rather than inventing posts:
   2. StockTwits messages: the cashtag stream, with Bullish/Bearish tags
   3. Reddit posts: r/wallstreetbets, r/stocks, r/investing
 
-Each source is trimmed to the analysis window. These feeds serve recent items
+Each source is trimmed to the analysis window. With a TypeSafe key, the social
+posts are screened by Jev first (see post_screen). These feeds serve recent items
 and are not archived, so a historical run's sentiment inputs are not
 point-in-time.
 
@@ -22,6 +23,7 @@ from langchain_core.messages import AIMessage
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
 
 from tradingagents.agents.context import get_instrument_context_from_state, get_language_instruction
+from tradingagents.agents.post_screen import jev_screen
 from tradingagents.agents.schemas import SentimentReport, render_sentiment_report
 from tradingagents.agents.structured import (
     NO_EXTERNAL_TOOLS,
@@ -59,10 +61,11 @@ def create_sentiment_analyst(llm):
         news_block = get_news.func(ticker, start_date, end_date)
         # Pass the analysis window so a historical run trims social posts to it
         # instead of leaking today's chatter into a backtest (#1220).
+        screen = jev_screen(ticker)
         stocktwits_block = fetch_stocktwits_messages(
-            ticker, limit=30, start_date=start_date, end_date=end_date
+            ticker, limit=30, start_date=start_date, end_date=end_date, screen=screen
         )
-        reddit_block = fetch_reddit_posts(ticker, start_date=start_date, end_date=end_date)
+        reddit_block = fetch_reddit_posts(ticker, start_date=start_date, end_date=end_date, screen=screen)
 
         system_message = _build_system_message(
             ticker=ticker,
@@ -152,7 +155,7 @@ Community discussion, without vote or comment counts. Subreddit character matter
 
 ## How to analyze this data (best practices)
 
-1. **Read the StockTwits Bullish/Bearish ratio as a leading retail-sentiment signal.** A 70/30 bullish/bearish split is moderately bullish; ≥90/10 may indicate over-extension and contrarian risk; 50/50 is uncertainty. Sample size matters — base rates on the actual message count, not percentages alone.
+1. **Read the StockTwits Bullish/Bearish ratio as a leading retail-sentiment signal.** A 70/30 bullish/bearish split is moderately bullish; ≥90/10 may indicate over-extension and contrarian risk; 50/50 is uncertainty. Sample size matters — base rates on the actual message count, not percentages alone. A block headed "Screened by Jev" has had off-topic posts removed; its stance count is a classifier's read of every on-topic post fetched, labelled or not, of which the posts listed are a sample. Read it alongside the user tags.
 
 2. **Look for cross-source divergences.** If news framing is bearish but StockTwits is overwhelmingly bullish, that mismatch is itself a signal — it can mean retail is leaning into a thesis the news flow hasn't caught up to (or vice versa, that retail is chasing while institutions are cautious).
 
diff --git a/tradingagents/agents/post_screen.py b/tradingagents/agents/post_screen.py
new file mode 100644
index 000000000..271c0009a
--- /dev/null
+++ b/tradingagents/agents/post_screen.py
@@ -0,0 +1,174 @@
+"""Social-post screening with TypeSafe's Jev, when ``TYPESAFE_API_KEY`` is set.
+
+Jev answers typed questions with calibrated probabilities. Each StockTwits or
+Reddit post is asked two: is it about the instrument, and which way does it
+lean on the instrument's stock. Code turns the answers into what the Sentiment
+Analyst reads: posts that are clearly about something else are dropped, and a
+stance count over the rest heads the source's block.
+
+Configured by TypeSafe's own SDK variables, ``TYPESAFE_API_KEY`` and
+``TYPESAFE_DEFAULT_MODEL``. Without a key nothing here runs; if any request fails, the source's posts are kept unscreened and the
+block says screening was unavailable.
+"""
+
+import logging
+import os
+import random
+import time
+from concurrent.futures import ThreadPoolExecutor, as_completed
+
+import requests
+
+from tradingagents.agents.context import resolve_instrument_identity
+
+logger = logging.getLogger(__name__)
+
+_URL = "https://api.typesafe.ai/v1/systemone"
+_DEFAULT_MODEL = "jev-latest"
+_RETRY_STATUSES = (429, 529)    # rate limited, overloaded: back off and retry
+_TRANSIENT = (requests.ConnectionError, requests.Timeout, requests.exceptions.ChunkedEncodingError)
+_ATTEMPTS = 3
+_MAX_WAIT = 30.0
+_TIMEOUT = 15.0
+_WORKERS = 16                   # well inside the documented 1,200 requests per minute
+
+# A post is dropped only on a clear "not about it"; the uncertain middle stays.
+_OFF_TOPIC_BELOW = 0.3
+# A stance counts only when Jev is not genuinely uncertain about it.
+_STANCE_CONFIDENCE = 0.5
+
+QUESTIONS = {
+    "about": {
+        "type": "noul",
+        "instructions": "Is `post` about `instrument`: the company, its stock, its products or its outlook?",
+        "criteria": {
+            "true": "`post` discusses `instrument` itself.",
+            "false": "`post` names `instrument` only in passing or in a list of tickers, is spam or "
+                     "promotion, or is about a different company.",
+        },
+    },
+    "stance": {
+        "type": "choice",
+        "instructions": "What does the author of `post` expect for the stock price of `instrument`?",
+        "criteria": {
+            "bullish": "The author expects `instrument`'s stock to rise, or is buying or holding it long.",
+            "bearish": "The author expects `instrument`'s stock to fall, or is selling or shorting it.",
+            "neutral": "The author gives no view of their own on `instrument`'s stock: a question, "
+                       "news without opinion, or someone else's view quoted.",
+        },
+    },
+}
+
+
+class TypeSafeError(Exception):
+    """A System One request that did not produce answers."""
+
+
+def system_one(state, questions: dict) -> dict[str, dict]:
+    """Ask ``questions`` about ``state``; return the answers keyed by question name.
+
+    Rate-limit and overload responses and dropped connections are retried with
+    backoff, honouring ``Retry-After``; any other failure raises
+    ``TypeSafeError`` at once.
+    """
+    body = {
+        "state": state,
+        "model": os.environ.get("TYPESAFE_DEFAULT_MODEL") or _DEFAULT_MODEL,
+        "questions": questions,
+    }
+    headers = {"Authorization": f"Bearer {os.environ.get('TYPESAFE_API_KEY', '')}"}
+    backoff, retry_after = 1.0, None
+    for attempt in range(_ATTEMPTS):
+        if attempt:
+            time.sleep(retry_after if retry_after is not None else backoff * random.uniform(0.8, 1.2))
+            backoff *= 2
+        try:
+            response = requests.post(_URL, json=body, headers=headers, timeout=_TIMEOUT)
+        except requests.RequestException as exc:
+            failure, retry_after = type(exc).__name__, None
+            if isinstance(exc, _TRANSIENT):
+                continue
+            break
+        if response.status_code == 200:
+            return _answers(response, questions)
+        failure, retry_after = f"HTTP {response.status_code}", _retry_after(response)
+        if response.status_code not in _RETRY_STATUSES:
+            break
+    raise TypeSafeError(failure)
+
+
+def _retry_after(response) -> float | None:
+    try:
+        return min(max(0.0, float(response.headers.get("retry-after"))), _MAX_WAIT)
+    except (TypeError, ValueError):
+        return None
+
+
+def _answers(response, questions: dict) -> dict[str, dict]:
+    try:
+        payload = response.json()
+        answers = payload["answers"]
+        if all(answers[q]["type"] == spec["type"] for q, spec in questions.items()):
+            logger.debug("TypeSafe answered with %s", payload.get("model"))
+            return answers
+    except (ValueError, KeyError, TypeError):
+        pass
+    raise TypeSafeError("malformed response")
+
+
+def _stance(answer: dict) -> str:
+    choice = answer["choice"]
+    if choice not in QUESTIONS["stance"]["criteria"] or answer["confidence"] < _STANCE_CONFIDENCE:
+        return "unclear"
+    return choice
+
+
+def jev_screen(ticker: str):
+    """A post screen for the social fetchers, or None without a TypeSafe key.
+
+    The screen takes the post texts and returns one keep flag per post and a
+    note line for the top of the source's block.
+    """
+    if not os.environ.get("TYPESAFE_API_KEY"):
+        return None
+    name = resolve_instrument_identity(ticker).get("company_name")
+    instrument = f"{name} ({ticker})" if name else ticker
+
+    def screen(posts: list[str]) -> tuple[list[bool], str]:
+        try:
+            answers = _ask_each(instrument, posts)
+            keep = [a["about"]["noul"] >= _OFF_TOPIC_BELOW for a in answers]
+            stances = [_stance(a["stance"]) for a, kept in zip(answers, keep, strict=True) if kept]
+        except (KeyError, TypeError):
+            return _unscreened(instrument, posts, "malformed response")
+        except TypeSafeError as exc:
+            return _unscreened(instrument, posts, str(exc))
+        counts = ", ".join(f"{stances.count(s)} {s}" for s in ("bullish", "bearish", "neutral", "unclear"))
+        return keep, (
+            f"Screened by Jev: {len(stances)} of the {len(posts)} posts fetched are about "
+            f"{instrument}; their stance on its stock: {counts}."
+        )
+
+    return screen
+
+
+def _unscreened(instrument: str, posts: list[str], failure: str) -> tuple[list[bool], str]:
+    logger.warning("Jev screening failed for %s: %s", instrument, failure)
+    return [True] * len(posts), f"<Jev screening unavailable ({failure}); posts are unscreened>"
+
+
+def _ask_each(instrument: str, posts: list[str]) -> list[dict]:
+    """One request per post. The first failure raises at once: requests not yet
+    sent are cancelled, and those in flight finish in the background unread."""
+    answers: list = [None] * len(posts)
+    pool = ThreadPoolExecutor(max_workers=_WORKERS)
+    try:
+        futures = {
+            pool.submit(system_one, {"instrument": instrument, "post": post}, QUESTIONS): i
+            for i, post in enumerate(posts)
+        }
+        for future in as_completed(futures):
+            answers[futures[future]] = future.result()
+    finally:
+        pool.shutdown(wait=False, cancel_futures=True)
+    return answers
diff --git a/tradingagents/dataflows/vendors/reddit.py b/tradingagents/dataflows/vendors/reddit.py
index db8397bfe..c490ebabe 100644
--- a/tradingagents/dataflows/vendors/reddit.py
+++ b/tradingagents/dataflows/vendors/reddit.py
@@ -82,6 +82,7 @@ DEFAULT_SUBREDDITS = ("wallstreetbets", "stocks", "investing")
 # subreddits fits well inside one page, which keeps a high-volume subreddit from
 # crowding the others out of a combined search.
 _FEED_PAGE = 100
+_SCREEN_CHARS = 1000   # of a post's title and body sent for screening
 
 
 _SEARCH_LOOKBACK = timedelta(days=7)  # matches t=week below
@@ -238,6 +239,7 @@ def fetch_reddit_posts(
     timeout: float = 10.0,
     start_date: str | None = None,
     end_date: str | None = None,
+    screen=None,
 ) -> str:
     """Fetch recent Reddit posts mentioning ``ticker`` across finance
     subreddits and return them as a formatted plaintext block.
@@ -250,6 +252,10 @@ def fetch_reddit_posts(
     When ``start_date``/``end_date`` (yyyy-mm-dd) are given, posts are trimmed to
     that window so a historical run does not leak current discussion into a
     backtest (#1220).
+
+    ``screen``, when given, takes each post's title and body and returns a keep
+    flag per post and a note line that heads the block. It runs before the
+    per-subreddit cut, so the posts it keeps fill the slots.
     """
     # Crypto reaches us as a Yahoo pair (BTC-USD); search Reddit for the base
     # ("BTC") so the query actually matches discussion instead of near-nothing.
@@ -270,22 +276,34 @@ def fetch_reddit_posts(
         period = f"within {start_date}..{end_date}" if window else "in the past 7 days"
         return gap or f"<no Reddit posts found mentioning {ticker.upper()} across {label} {period}>"
 
+    def sub_of(p):
+        return p.get("subreddit") or (subreddits[0] if len(subreddits) == 1 else "unknown")
+
+    note, screened_out = "", set()
+    if screen:
+        keep, note = screen([f"{p.get('title') or ''}\n{p.get('selftext') or ''}"[:_SCREEN_CHARS]
+                             for p in posts])
+        screened_out = {sub_of(p).lower() for p, kept in zip(posts, keep, strict=True) if not kept}
+        posts = [p for p, kept in zip(posts, keep, strict=True) if kept]
+
     # Group by the subreddit each entry names, in the requested order. Nothing
     # is dropped: an unlabelled post from a one-subreddit request belongs to it,
     # and any other name gets its own block.
     by_sub = {s.lower(): (s, []) for s in subreddits}
     for p in posts:
-        name = p.get("subreddit") or (subreddits[0] if len(subreddits) == 1 else "unknown")
-        by_sub.setdefault(name.lower(), (name, []))[1].append(p)
+        by_sub.setdefault(sub_of(p).lower(), (sub_of(p), []))[1].append(p)
 
     page_full = len(fetched) >= _FEED_PAGE
     blocks = []
     for sub, sub_posts in by_sub.values():
         if not sub_posts:
-            blocks.append(
-                f"r/{sub}: <not among the newest {_FEED_PAGE} matches across {label}>"
-                if page_full else f"r/{sub}: <no posts found mentioning {ticker.upper()}>"
-            )
+            if sub.lower() in screened_out:
+                blocks.append(f"r/{sub}: <no posts about {ticker.upper()} after screening>")
+            else:
+                blocks.append(
+                    f"r/{sub}: <not among the newest {_FEED_PAGE} matches across {label}>"
+                    if page_full else f"r/{sub}: <no posts found mentioning {ticker.upper()}>"
+                )
             continue
         sub_posts = sub_posts[:limit_per_sub]  # the feed is newest-first
         lines = [f"r/{sub} — {len(sub_posts)} recent posts mentioning {ticker.upper()}:"]
@@ -301,4 +319,4 @@ def fetch_reddit_posts(
                 + (f"\n    body excerpt: {selftext}" if selftext else "")
             )
         blocks.append("\n".join(lines))
-    return "\n\n".join(blocks)
+    return "\n\n".join(([note] if note else []) + blocks)
diff --git a/tradingagents/dataflows/vendors/stocktwits.py b/tradingagents/dataflows/vendors/stocktwits.py
index 987a279d6..b6985fc89 100644
--- a/tradingagents/dataflows/vendors/stocktwits.py
+++ b/tradingagents/dataflows/vendors/stocktwits.py
@@ -71,6 +71,7 @@ def fetch_stocktwits_messages(
     timeout: float = 10.0,
     start_date: str | None = None,
     end_date: str | None = None,
+    screen=None,
 ) -> str:
     """Fetch recent StockTwits messages for ``ticker`` and return them as a
     formatted plaintext block ready for prompt injection.
@@ -80,6 +81,9 @@ def fetch_stocktwits_messages(
     public stream only serves recent messages, so a window it cannot reach is
     reported as unavailable rather than as silence.
 
+    ``screen``, when given, takes the message bodies and returns a keep flag per
+    message and a note line that heads the block.
+
     Returns a placeholder string when the endpoint is unreachable, the
     symbol has no messages, or the response shape is unexpected — the
     caller never has to special-case None or exceptions.
@@ -109,6 +113,14 @@ def fetch_stocktwits_messages(
             )
         return f"<no StockTwits messages found for ${ticker.upper()}>"
 
+    note = ""
+    if screen:
+        keep, note = screen([m.get("body") or "" for m in messages])
+        screened = len(messages)
+        messages = [m for m, kept in zip(messages, keep, strict=True) if kept]
+        if not messages:
+            return f"{note}\n\n<none of the {screened} StockTwits messages is about ${ticker.upper()}>"
+
     lines = []
     bullish = bearish = unlabeled = 0
     for m in messages[:limit]:
@@ -141,4 +153,4 @@ def fetch_stocktwits_messages(
         f"Unlabeled: {unlabeled} · "
         f"Total: {total} most-recent messages"
     )
-    return summary + "\n\n" + "\n".join(lines)
+    return (f"{note}\n\n" if note else "") + summary + "\n\n" + "\n".join(lines)