diff --git a/prediction_market_agent_tooling/tools/relevant_news_analysis/data_models.py b/prediction_market_agent_tooling/tools/relevant_news_analysis/data_models.py
new file mode 100644
index 00000000..ec93f84e
--- /dev/null
+++ b/prediction_market_agent_tooling/tools/relevant_news_analysis/data_models.py
@@ -0,0 +1,44 @@
+from pydantic import BaseModel, Field
+
+from prediction_market_agent_tooling.tools.tavily.tavily_models import TavilyResult
+
+
+class RelevantNewsAnalysis(BaseModel):
+    reasoning: str = Field(
+        ...,
+        description="The reason why the news contains information relevant to the given question. Or if no news is relevant, why not.",
+    )
+    contains_relevant_news: bool = Field(
+        ...,
+        description="A boolean flag for whether the news contains information relevant to the given question.",
+    )
+
+
+class RelevantNews(BaseModel):
+    question: str
+    url: str
+    summary: str
+    relevance_reasoning: str
+    days_ago: int
+
+    @staticmethod
+    def from_tavily_result_and_analysis(
+        question: str,
+        days_ago: int,
+        tavily_result: TavilyResult,
+        relevant_news_analysis: RelevantNewsAnalysis,
+    ) -> "RelevantNews":
+        return RelevantNews(
+            question=question,
+            url=tavily_result.url,
+            summary=tavily_result.content,
+            relevance_reasoning=relevant_news_analysis.reasoning,
+            days_ago=days_ago,
+        )
+
+
+class NoRelevantNews(BaseModel):
+    """
+    A placeholder model for when no relevant news is found. Enables ability to
+    distinguish between 'a cache hit with no news' and 'a cache miss'.
+    """
diff --git a/prediction_market_agent_tooling/tools/relevant_news_analysis/relevant_news_analysis.py b/prediction_market_agent_tooling/tools/relevant_news_analysis/relevant_news_analysis.py
new file mode 100644
index 00000000..2a469853
--- /dev/null
+++ b/prediction_market_agent_tooling/tools/relevant_news_analysis/relevant_news_analysis.py
@@ -0,0 +1,162 @@
+from datetime import datetime, timedelta
+
+from langchain_core.output_parsers import PydanticOutputParser
+from langchain_core.prompts import PromptTemplate
+from langchain_openai import ChatOpenAI
+
+from prediction_market_agent_tooling.config import APIKeys
+from prediction_market_agent_tooling.tools.langfuse_ import (
+    get_langfuse_langchain_config,
+    observe,
+)
+from prediction_market_agent_tooling.tools.relevant_news_analysis.data_models import (
+    NoRelevantNews,
+    RelevantNews,
+    RelevantNewsAnalysis,
+)
+from prediction_market_agent_tooling.tools.relevant_news_analysis.relevant_news_cache import (
+    RelevantNewsResponseCache,
+)
+from prediction_market_agent_tooling.tools.tavily.tavily_search import (
+    get_relevant_news_since,
+)
+from prediction_market_agent_tooling.tools.tavily.tavily_storage import TavilyStorage
+from prediction_market_agent_tooling.tools.utils import check_not_none, utcnow
+
+SUMMARISE_RELEVANT_NEWS_PROMPT_TEMPLATE = """
+You are an expert news analyst, tracking stories that may affect your prediction to the outcome of a particular QUESTION.
+
+Your role is to identify only the relevant information from a scraped news site (RAW_CONTENT), analyse it, and determine whether it contains developments or announcements occurring **after** the DATE_OF_INTEREST that could affect the outcome of the QUESTION.
+
+Note that the news article may be published after the DATE_OF_INTEREST, but reference information that is older than the DATE_OF_INTEREST.
+
+[QUESTION]
+{question}
+
+[DATE_OF_INTEREST]
+{date_of_interest}
+
+[RAW_CONTENT]
+{raw_content}
+
+For your analysis, you should:
+- Discard the 'noise' from the raw content (e.g. ads, irrelevant content)
+- Consider ONLY information that would have a notable impact on the outcome of the question.
+- Consider ONLY information relating to an announcement or development that occurred **after** the DATE_OF_INTEREST.
+- Present this information concisely in your reasoning.
+- In your reasoning, do not use the term 'DATE_OF_INTEREST' directly. Use the actual date you are referring to instead.
+- In your reasoning, do not use the term 'RAW_CONTENT' directly. Refer to it as 'the article', or quote the content you are referring to.
+
+{format_instructions}
+"""
+
+
+@observe()
+def analyse_news_relevance(
+    raw_content: str,
+    question: str,
+    date_of_interest: datetime,
+    model: str,
+    temperature: float,
+) -> RelevantNewsAnalysis:
+    """
+    Analyse whether the news contains new (relative to the given date)
+    information relevant to the given question.
+    """
+    parser = PydanticOutputParser(pydantic_object=RelevantNewsAnalysis)
+    prompt = PromptTemplate(
+        template=SUMMARISE_RELEVANT_NEWS_PROMPT_TEMPLATE,
+        input_variables=["question", "date_of_interest", "raw_content"],
+        partial_variables={"format_instructions": parser.get_format_instructions()},
+    )
+    llm = ChatOpenAI(
+        temperature=temperature,
+        model=model,
+        api_key=APIKeys().openai_api_key_secretstr_v1,
+    )
+    chain = prompt | llm | parser
+
+    relevant_news_analysis: RelevantNewsAnalysis = chain.invoke(
+        {
+            "raw_content": raw_content,
+            "question": question,
+            "date_of_interest": str(date_of_interest),
+        },
+        config=get_langfuse_langchain_config(),
+    )
+    return relevant_news_analysis
+
+
+@observe()
+def get_certified_relevant_news_since(
+    question: str,
+    days_ago: int,
+    tavily_storage: TavilyStorage | None = None,
+) -> RelevantNews | None:
+    """
+    Get relevant news since a given date for a given question. Retrieves
+    possibly relevant news from tavily, then checks that it is relevant via
+    an LLM call.
+    """
+    results = get_relevant_news_since(
+        question=question,
+        days_ago=days_ago,
+        score_threshold=0.0,  # Be conservative to avoid missing relevant information
+        max_results=3,  # A tradeoff between cost and quality. 3 seems to be a good balance.
+        tavily_storage=tavily_storage,
+    )
+
+    # Sort results by descending 'relevance score' to maximise the chance of
+    # finding relevant news early
+    results = sorted(
+        results,
+        key=lambda result: result.score,
+        reverse=True,
+    )
+
+    for result in results:
+        relevant_news_analysis = analyse_news_relevance(
+            raw_content=check_not_none(result.raw_content),
+            question=question,
+            date_of_interest=utcnow() - timedelta(days=days_ago),
+            model="gpt-4o",  # 4o-mini isn't good enough, 1o and 1o-mini are too expensive
+            temperature=0.0,
+        )
+
+        # Return first relevant news found
+        if relevant_news_analysis.contains_relevant_news:
+            return RelevantNews.from_tavily_result_and_analysis(
+                question=question,
+                days_ago=days_ago,
+                tavily_result=result,
+                relevant_news_analysis=relevant_news_analysis,
+            )
+
+    # No relevant news found
+    return None
+
+
+def get_certified_relevant_news_since_cached(
+    question: str,
+    days_ago: int,
+    cache: RelevantNewsResponseCache,
+    tavily_storage: TavilyStorage | None = None,
+) -> RelevantNews | None:
+    cached = cache.find(question=question, days_ago=days_ago)
+
+    if isinstance(cached, NoRelevantNews):
+        return None
+    elif cached is None:
+        relevant_news = get_certified_relevant_news_since(
+            question=question,
+            days_ago=days_ago,
+            tavily_storage=tavily_storage,
+        )
+        cache.save(
+            question=question,
+            days_ago=days_ago,
+            relevant_news=relevant_news,
+        )
+        return relevant_news
+    else:
+        return cached
diff --git a/prediction_market_agent_tooling/tools/relevant_news_analysis/relevant_news_cache.py b/prediction_market_agent_tooling/tools/relevant_news_analysis/relevant_news_cache.py
new file mode 100644
index 00000000..d1257ec5
--- /dev/null
+++ b/prediction_market_agent_tooling/tools/relevant_news_analysis/relevant_news_cache.py
@@ -0,0 +1,90 @@
+from datetime import datetime, timedelta
+
+from pydantic import ValidationError
+from sqlmodel import Field, Session, SQLModel, create_engine, desc, select
+
+from prediction_market_agent_tooling.config import APIKeys
+from prediction_market_agent_tooling.loggers import logger
+from prediction_market_agent_tooling.tools.relevant_news_analysis.data_models import (
+    NoRelevantNews,
+    RelevantNews,
+)
+from prediction_market_agent_tooling.tools.utils import utcnow
+
+
+class RelevantNewsCacheModel(SQLModel, table=True):
+    __tablename__ = "relevant_news_response_cache"
+    __table_args__ = {"extend_existing": True}
+    id: int | None = Field(default=None, primary_key=True)
+    question: str = Field(index=True)
+    datetime_: datetime = Field(index=True)
+    days_ago: int
+    json_dump: str | None
+
+
+class RelevantNewsResponseCache:
+    def __init__(self, sqlalchemy_db_url: str | None = None):
+        self.engine = create_engine(
+            sqlalchemy_db_url
+            if sqlalchemy_db_url
+            else APIKeys().sqlalchemy_db_url.get_secret_value()
+        )
+        self._initialize_db()
+
+    def _initialize_db(self) -> None:
+        """
+        Creates the tables if they don't exist
+        """
+        with self.engine.connect() as conn:
+            SQLModel.metadata.create_all(
+                conn,
+                tables=[SQLModel.metadata.tables[RelevantNewsCacheModel.__tablename__]],
+            )
+
+    def find(
+        self,
+        question: str,
+        days_ago: int,
+    ) -> RelevantNews | NoRelevantNews | None:
+        with Session(self.engine) as session:
+            query = (
+                select(RelevantNewsCacheModel)
+                .where(RelevantNewsCacheModel.question == question)
+                .where(RelevantNewsCacheModel.days_ago <= days_ago)
+                .where(
+                    RelevantNewsCacheModel.datetime_ >= utcnow() - timedelta(days=1)
+                )  # Cache entries expire after 1 day
+            )
+            item = session.exec(
+                query.order_by(desc(RelevantNewsCacheModel.datetime_))
+            ).first()
+
+            if item is None:
+                return None
+            else:
+                if item.json_dump is None:
+                    return NoRelevantNews()
+                else:
+                    try:
+                        return RelevantNews.model_validate_json(item.json_dump)
+                    except ValidationError as e:
+                        logger.error(
+                            f"Error deserializing RelevantNews from cache for {question=}, {days_ago=} and {item=}: {e}"
+                        )
+                        return None
+
+    def save(
+        self,
+        question: str,
+        days_ago: int,
+        relevant_news: RelevantNews | None,
+    ) -> None:
+        with Session(self.engine) as session:
+            cached = RelevantNewsCacheModel(
+                question=question,
+                days_ago=days_ago,
+                datetime_=utcnow(),  # Assumes that the cache is being updated at the time the news is found
+                json_dump=relevant_news.model_dump_json() if relevant_news else None,
+            )
+            session.add(cached)
+            session.commit()
diff --git a/prediction_market_agent_tooling/tools/tavily/tavily_search.py b/prediction_market_agent_tooling/tools/tavily/tavily_search.py
index b3559dd3..4f27e11f 100644
--- a/prediction_market_agent_tooling/tools/tavily/tavily_search.py
+++ b/prediction_market_agent_tooling/tools/tavily/tavily_search.py
@@ -129,7 +129,7 @@ def _tavily_search(
     return response
 
 
-def get_related_news_since(
+def get_relevant_news_since(
     question: str,
     days_ago: int,
     score_threshold: float = DEFAULT_SCORE_THRESHOLD,
diff --git a/tests_integration/tools/test_relevant_news_analysis.py b/tests_integration/tools/test_relevant_news_analysis.py
new file mode 100644
index 00000000..bf260c3b
--- /dev/null
+++ b/tests_integration/tools/test_relevant_news_analysis.py
@@ -0,0 +1,97 @@
+from unittest.mock import patch
+
+import pytest
+from langchain_community.callbacks import get_openai_callback
+
+from prediction_market_agent_tooling.tools.relevant_news_analysis.data_models import (
+    RelevantNews,
+)
+from prediction_market_agent_tooling.tools.relevant_news_analysis.relevant_news_analysis import (
+    get_certified_relevant_news_since,
+    get_certified_relevant_news_since_cached,
+)
+from prediction_market_agent_tooling.tools.relevant_news_analysis.relevant_news_cache import (
+    RelevantNewsResponseCache,
+)
+from tests.utils import RUN_PAID_TESTS
+
+
+@pytest.mark.skipif(not RUN_PAID_TESTS, reason="This test costs money to run.")
+def test_get_certified_relevant_news_since() -> None:
+    questions_days_ago_expected_results = [
+        (
+            "Will the price of Bitcoin be higher than $100,000 by the end of the year?",
+            True,
+            5,
+        ),
+        (
+            "Will the strength of the Earth's gravitational field change by more than 3% any time before the end of the calendar year?",
+            False,
+            2,
+        ),
+        (
+            "Will the number of Chinese-made electric cars sold worldwide this year be higher than in the previous calendar year?",
+            True,
+            90,
+        ),
+        (
+            "Will total UK cinema box office sales this month be higher than in the previous calendar month?",
+            True,
+            14,
+        ),
+    ]
+
+    running_cost = 0.0
+    iterations = 0
+    for question, expected_result, days_ago in questions_days_ago_expected_results:
+        with get_openai_callback() as cb:
+            news = get_certified_relevant_news_since(
+                question=question,
+                days_ago=days_ago,
+            )
+            running_cost += cb.total_cost
+            iterations += 1
+
+        has_related_news = news is not None
+        assert (
+            has_related_news == expected_result
+        ), f"Was relevant news found for question '{question}'?: {has_related_news}. Expected result {expected_result}"
+
+    average_cost = running_cost / iterations  # $0.01289 when run on 2022-10-24
+    assert average_cost < 0.02, f"Expected average: {average_cost}. Expected < 0.02"
+
+
+def test_get_certified_relevant_news_since_cached() -> None:
+    cache = RelevantNewsResponseCache(sqlalchemy_db_url="sqlite:///:memory:")
+
+    question = (
+        "Will the price of Bitcoin be higher than $100,000 by the end of the year?"
+    )
+    days_ago = 5
+    assert (
+        cache.find(question=question, days_ago=days_ago) is None
+    ), "Cache should be empty"
+
+    mock_news = RelevantNews(
+        question=question,
+        url="https://www.example.com",
+        summary="This is a summary",
+        relevance_reasoning="some reasoning",
+        days_ago=days_ago,
+    )
+    with patch(
+        "prediction_market_agent_tooling.tools.relevant_news_analysis.relevant_news_analysis.get_certified_relevant_news_since"
+    ) as get_certified_relevant_news_since:
+        # Mock the response
+        get_certified_relevant_news_since.return_value = mock_news
+
+        news = get_certified_relevant_news_since_cached(
+            question=question,
+            days_ago=1,
+            cache=cache,
+        )
+
+    assert news == mock_news
+    assert (
+        cache.find(question=question, days_ago=days_ago) == mock_news
+    ), "Cache should contain the news"