diff --git a/crews/chart_library_research_crew/README.md b/crews/chart_library_research_crew/README.md
new file mode 100644
index 00000000..bb2a12a4
--- /dev/null
+++ b/crews/chart_library_research_crew/README.md
@@ -0,0 +1,45 @@
+# CrewAI + Chart Library — Two-Agent Research Crew
+
+Two agents in a sequential crew using Chart Library's cohort primitive:
+
+- **Researcher** calls the data tools (`get_cohort_distribution`,
+  `explain_cohort_filters`, `refine_cohort_with_filters`). Produces a
+  numeric-only report.
+- **Analyst** turns those numbers into a 150-word PM briefing,
+  citing sample sizes and disclosing survivorship.
+
+The separation is useful: the researcher has tool access but no
+narrative license; the analyst has narrative license but no tool
+access. This enforces that every written claim maps back to a real
+retrieved number.
+
+## Run
+
+```bash
+pip install crewai crewai-tools requests
+export OPENAI_API_KEY=sk-...        # CrewAI default; change to taste
+export CHART_LIBRARY_KEY=cl_...     # chartlibrary.io/developers
+python cohort_research_crew.py NVDA 2024-06-18
+```
+
+## Propagate upstream
+
+CrewAI maintains a `crewai-examples` repo and a "marketplace" at
+crewai.com. For broader reach:
+
+- Submit to `github.com/crewAIInc/crewAI-examples` under
+  `examples/chart_library_research_crew/`
+- Consider adding Chart Library as a listed tool in CrewAI's official
+  `crewai-tools` package (separate PR)
+- Post a tutorial on [crewai.com community](https://community.crewai.com/)
+  linking back to chartlibrary.io/developers
+
+## Extend
+
+- Swap researcher's model for a faster one (tool-calling doesn't need a
+  frontier model — Claude Haiku or GPT-4o mini works).
+- Add a `risk_officer` agent that reads the analyst's briefing and
+  calls back to Chart Library for a same-regime drawdown distribution
+  before signing off.
+- Hook the final output into a trading-advice service that requires
+  survivorship disclosure as a pre-flight check.
diff --git a/crews/chart_library_research_crew/cohort_research_crew.py b/crews/chart_library_research_crew/cohort_research_crew.py
new file mode 100644
index 00000000..243769df
--- /dev/null
+++ b/crews/chart_library_research_crew/cohort_research_crew.py
@@ -0,0 +1,194 @@
+# -*- coding: utf-8 -*-
+"""
+CrewAI example — two-agent crew using Chart Library's cohort primitive
+for grounded financial research.
+
+Agents
+------
+Researcher — calls get_cohort_distribution + explain_cohort_filters to
+             surface conditional structure. Never writes prose.
+Analyst    — takes the researcher's numeric findings and writes a
+             sized-up briefing for a portfolio manager. Must cite
+             every number with its sample size and survivorship flag.
+
+Run
+---
+    pip install crewai crewai-tools requests
+    export OPENAI_API_KEY=sk-...       # or ANTHROPIC_API_KEY + tweak
+    export CHART_LIBRARY_KEY=cl_...    # chartlibrary.io/developers
+    python cohort_research_crew.py NVDA 2024-06-18
+"""
+from __future__ import annotations
+
+import json
+import os
+import sys
+
+import requests
+
+try:
+    from crewai import Agent, Crew, Process, Task
+    from crewai.tools import tool
+except ImportError:  # pragma: no cover
+    raise SystemExit("pip install crewai crewai-tools requests")
+
+CHART_BASE = "https://chartlibrary.io"
+CHART_KEY = os.environ["CHART_LIBRARY_KEY"]
+H = {"Authorization": f"Bearer {CHART_KEY}", "Content-Type": "application/json"}
+
+
+# ── Tools ──────────────────────────────────────────────────
+
+@tool("get_cohort_distribution")
+def get_cohort(symbol: str, date: str, same_sector: bool = False,
+               same_vix_bucket: bool = False, same_trend: bool = False) -> str:
+    """Return historical forward-return distribution for a chart pattern.
+    Returns JSON with cohort_id (use with other tools), return/MAE/MFE/RV
+    percentiles at 5d and 10d, sample size, and survivorship flag.
+
+    Args:
+        symbol: Ticker e.g. 'NVDA'
+        date: ISO date e.g. '2024-06-18'
+        same_sector: restrict to same sector as anchor
+        same_vix_bucket: restrict to same VIX regime
+        same_trend: restrict to same SPY trend regime
+    """
+    filters = {}
+    if same_sector:
+        filters["sector"] = "same_as_anchor"
+    regime = {}
+    if same_vix_bucket: regime["same_vix_bucket"] = True
+    if same_trend:      regime["same_trend"] = True
+    if regime:
+        filters["regime"] = regime
+    body = {
+        "anchor": {"symbol": symbol, "date": date},
+        "filters": filters, "horizons": [5, 10],
+        "top_k": 500, "include_path_stats": True,
+    }
+    r = requests.post(f"{CHART_BASE}/api/v1/cohort", headers=H, json=body, timeout=30)
+    return json.dumps(r.json(), default=str)
+
+
+@tool("explain_cohort_filters")
+def explain_cohort(cohort_id: str, horizon: int = 5) -> str:
+    """Rank which additional filter would shift the distribution most for
+    a previously-returned cohort. Call AFTER get_cohort_distribution.
+    """
+    r = requests.get(
+        f"{CHART_BASE}/api/v1/cohort/{cohort_id}/explain",
+        headers=H, params={"horizon": horizon}, timeout=30,
+    )
+    return json.dumps(r.json(), default=str)
+
+
+@tool("refine_cohort_with_filters")
+def refine_cohort(cohort_id: str, same_vix_bucket: bool = False,
+                  same_trend: bool = False) -> str:
+    """Narrow a stored cohort with an extra regime filter. Sub-second.
+    Returns a new cohort_id + updated distributions.
+    """
+    extra = {}
+    regime = {}
+    if same_vix_bucket: regime["same_vix_bucket"] = True
+    if same_trend:      regime["same_trend"] = True
+    if regime: extra["regime"] = regime
+    r = requests.post(
+        f"{CHART_BASE}/api/v1/cohort/{cohort_id}/filter",
+        headers=H, json={"extra_filters": extra, "include_path_stats": True},
+        timeout=30,
+    )
+    return json.dumps(r.json(), default=str)
+
+
+# ── Agents ─────────────────────────────────────────────────
+
+researcher = Agent(
+    role="Quantitative Pattern Researcher",
+    goal=(
+        "For a given (symbol, date) setup, produce a fully-grounded set of "
+        "conditional distribution statistics. Every number you return must "
+        "come from a tool call; never synthesize forward-return statistics."
+    ),
+    backstory=(
+        "You are a quantitative researcher who prioritizes honest base rates "
+        "over plausible narrative. You know shape-only similarity is noisy, "
+        "so you always refine the cohort with regime/sector filters and "
+        "quote sample size plus survivorship in every claim."
+    ),
+    tools=[get_cohort, explain_cohort, refine_cohort],
+    verbose=True,
+    allow_delegation=False,
+)
+
+analyst = Agent(
+    role="Portfolio Analyst",
+    goal=(
+        "Turn the researcher's numeric findings into a 150-word briefing "
+        "for a discretionary portfolio manager. Every statistic must be "
+        "cited with its sample size, and survivorship must be disclosed."
+    ),
+    backstory=(
+        "You are a long-tenured analyst. You never trust forward-return "
+        "numbers without sample size. You explicitly disclose survivorship "
+        "because you've been burned by hidden bias before."
+    ),
+    tools=[],  # no direct data access — analyst works only from the researcher's output
+    verbose=True,
+    allow_delegation=False,
+)
+
+
+# ── Tasks ──────────────────────────────────────────────────
+
+def build_tasks(symbol: str, date: str) -> list[Task]:
+    return [
+        Task(
+            description=(
+                f"Research the {symbol} setup on {date}. Steps:\n"
+                f"  1. Call get_cohort_distribution({symbol!r}, {date!r}) with no filters.\n"
+                f"  2. Note cohort_id, sample size, survivorship, and 5d/10d return/MAE/MFE/RV percentiles.\n"
+                f"  3. Call explain_cohort_filters(cohort_id) to identify which filter "
+                f"     (same_sector, same_vix_bucket, same_trend) shifts the distribution most.\n"
+                f"  4. Call refine_cohort_with_filters(cohort_id, <winning_filter>=True) to narrow.\n"
+                f"  5. Report the baseline vs refined comparison as structured JSON-like prose. "
+                f"Do not write narrative — just the numbers."
+            ),
+            expected_output=(
+                "A numeric-only report listing: baseline cohort stats (n, survivorship, "
+                "5d percentiles, 10d percentiles), explain rankings, refined cohort stats, "
+                "and the magnitude of shift caused by the winning filter."
+            ),
+            agent=researcher,
+        ),
+        Task(
+            description=(
+                "Write a 150-word PM briefing on the setup based solely on the researcher's "
+                "findings. Include sample size for every statistic cited. Disclose survivorship "
+                "(how many delisted names were in the cohort). End with a one-sentence "
+                "sizing/risk implication. Do NOT invent any number not in the research output."
+            ),
+            expected_output="A 150-word briefing with embedded n= citations and a closing sizing line.",
+            agent=analyst,
+        ),
+    ]
+
+
+# ── Entry point ────────────────────────────────────────────
+
+def main():
+    symbol = sys.argv[1] if len(sys.argv) > 1 else "NVDA"
+    date = sys.argv[2] if len(sys.argv) > 2 else "2024-06-18"
+    crew = Crew(
+        agents=[researcher, analyst],
+        tasks=build_tasks(symbol, date),
+        process=Process.sequential,
+        verbose=True,
+    )
+    result = crew.kickoff()
+    print("\n─── FINAL BRIEFING ───")
+    print(result)
+
+
+if __name__ == "__main__":
+    main()