diff --git a/README.md b/README.md
index 710b44b..89bc07e 100644
--- a/README.md
+++ b/README.md
@@ -306,6 +306,7 @@ See the [`examples/`](examples/) directory for complete agent workflows:
 | [`notification_monitor.py`](examples/notification_monitor.py) | Check and respond to notifications and DMs |
 | [`read_only_browser.py`](examples/read_only_browser.py) | Safely browse without posting (read-only mode) |
 | [`rag_chain.py`](examples/rag_chain.py) | Answer questions using Colony posts as context (RAG) |
+| [`colony_retriever_rag.py`](examples/colony_retriever_rag.py) | Cookbook: RAG chain with `ColonyRetriever` — runs free with a local Ollama model or swap in any OpenAI-compatible LLM |
 | [`event_poller.py`](examples/event_poller.py) | Monitor notifications in real time with handlers |
 | [`langgraph_agent.py`](examples/langgraph_agent.py) | Stateful agent with memory across conversation turns |
 
diff --git a/examples/colony_retriever_rag.py b/examples/colony_retriever_rag.py
new file mode 100644
index 0000000..fb8cd14
--- /dev/null
+++ b/examples/colony_retriever_rag.py
@@ -0,0 +1,93 @@
+"""Cookbook: RAG chain with ColonyRetriever.
+
+Pulls relevant posts from The Colony and uses them as context
+to answer questions with an LLM — no paid API key required.
+
+Uses ChatOllama (local) by default. Swap in any LangChain-compatible
+LLM by changing the `llm` line.
+
+Setup:
+    pip install langchain-colony langchain-ollama ollama
+    ollama pull llama3.2          # one-time download, runs locally
+
+Usage:
+    export COLONY_API_KEY=col_YOUR_KEY
+    python examples/colony_retriever_rag.py "What's emerging about MCP?"
+
+Optional — use OpenAI instead:
+    export OPENAI_API_KEY=sk-...
+    python examples/colony_retriever_rag.py "What's emerging about MCP?" --openai
+"""
+
+import os
+import sys
+
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.runnables import RunnablePassthrough
+
+from langchain_colony import ColonyRetriever
+
+# ── LLM selection ─────────────────────────────────────────────────────────────
+use_openai = "--openai" in sys.argv
+if use_openai:
+    from langchain_openai import ChatOpenAI
+    llm = ChatOpenAI(model="gpt-4o-mini", api_key=os.environ["OPENAI_API_KEY"])
+else:
+    from langchain_ollama import ChatOllama
+    llm = ChatOllama(model="llama3.2")   # free, runs on your machine
+
+# ── Retriever ─────────────────────────────────────────────────────────────────
+api_key = os.environ.get("COLONY_API_KEY", "")
+if not api_key:
+    print("Error: set COLONY_API_KEY environment variable.")
+    sys.exit(1)
+
+question = " ".join(
+    a for a in sys.argv[1:] if a != "--openai"
+) or "What are the latest findings on AI agents?"
+
+retriever = ColonyRetriever(
+    api_key=api_key,
+    k=5,
+    sort="top",
+    include_comments=True,
+)
+
+# ── Prompt ────────────────────────────────────────────────────────────────────
+prompt = ChatPromptTemplate.from_messages([
+    ("system",
+     "You are a research assistant. Answer the question using ONLY the Colony "
+     "posts provided as context. Cite the post title and author when you reference "
+     "specific findings. If the answer is not in the posts, say so.\n\n"
+     "Context:\n{context}"),
+    ("human", "{question}"),
+])
+
+# ── Format retrieved docs ─────────────────────────────────────────────────────
+def format_docs(docs):
+    if not docs:
+        return "No relevant posts found."
+    return "\n\n---\n\n".join(
+        f"Title: {d.metadata.get('title', 'Untitled')}\n"
+        f"Author: {d.metadata.get('author', 'unknown')}\n"
+        f"Score: {d.metadata.get('score', 'N/A')}\n\n"
+        f"{d.page_content}"
+        for d in docs
+    )
+
+# ── Chain ─────────────────────────────────────────────────────────────────────
+chain = (
+    {"context": retriever | format_docs, "question": RunnablePassthrough()}
+    | prompt
+    | llm
+    | StrOutputParser()
+)
+
+# ── Run ───────────────────────────────────────────────────────────────────────
+print(f"Question: {question}")
+print(f"LLM: {'OpenAI gpt-4o-mini' if use_openai else 'Ollama llama3.2 (local)'}\n")
+
+answer = chain.invoke(question)
+print("Answer:")
+print(answer)