diff --git a/docs/essay.md b/docs/essay.md
new file mode 100644
index 0000000..1d19420
--- /dev/null
+++ b/docs/essay.md
@@ -0,0 +1,150 @@
+---
+layout: default
+title: "Essay"
+parent: "Example"
+nav_order: 1
+---
+
+# Summarization + QA agent for Paul Graham Essay
+
+```python
+from minillmflow import *
+import openai, os, yaml
+
+# Minimal LLM wrapper
+def call_llm(prompt):
+    openai.api_key = "YOUR_API_KEY_HERE"
+    r = openai.ChatCompletion.create(
+        model="gpt-4o",
+        messages=[{"role": "user", "content": prompt}]
+    )
+    return r.choices[0].message.content
+
+shared = {"data": {}, "summary": {}}
+
+# Load data into shared['data']
+class LoadData(Node):
+    def prep(self, shared):
+        path = "./miniLLMFlow/data/PaulGrahamEssaysLarge"
+        for fn in os.listdir(path):
+            with open(os.path.join(path, fn), 'r') as f:
+                shared['data'][fn] = f.read()
+    def exec(self, res): pass
+    def post(self, s, pr, er): pass
+
+LoadData().run(shared)
+
+# Summarize one file
+class SummarizeFile(Node):
+    def prep(self, s): return s['data'][self.params['filename']]
+    def exec(self, content): return call_llm(f"{content} Summarize in 10 words.")
+    def post(self, s, pr, sr): s["summary"][self.params['filename']] = sr
+
+node_summ = SummarizeFile()
+node_summ.set_params({"filename":"addiction.txt"})
+node_summ.run(shared)
+
+# Map-Reduce summarization
+class MapSummaries(BatchNode):
+    def prep(self, s):
+        text = s['data'][self.params['filename']]
+        return [text[i:i+10000] for i in range(0, len(text), 10000)]
+    def exec(self, chunk):
+        return call_llm(f"{chunk} Summarize in 10 words.")
+    def post(self, s, pr, er):
+        s["summary"][self.params['filename']] = [f"{i}. {r}" for i,r in enumerate(er)]
+
+class ReduceSummaries(Node):
+    def prep(self, s): return s["summary"][self.params['filename']]
+    def exec(self, chunks): return call_llm(f"{chunks} Combine into 10 words summary.")
+    def post(self, s, pr, sr): s["summary"][self.params['filename']] = sr
+
+map_summ = MapSummaries()
+reduce_summ = ReduceSummaries()
+map_summ >> reduce_summ
+
+flow = Flow(start=map_summ)
+flow.set_params({"filename":"before.txt"})
+flow.run(shared)
+
+# Summarize all files
+class SummarizeAllFiles(BatchFlow):
+    def prep(self, s): return [{"filename":fn} for fn in s['data']]
+
+SummarizeAllFiles(start=flow).run(shared)
+
+# QA agent
+class FindRelevantFile(Node):
+    def prep(self, s):
+        q = input("Enter a question: ")
+        filenames = list(s['summary'].keys())
+        file_summaries = [f"- '{fn}': {s['summary'][fn]}" for fn in filenames]
+        return q, filenames, file_summaries
+
+    def exec(self, p):
+        q, filenames, file_summaries = p
+        if not q:
+            return {"think":"no question", "has_relevant":False}
+        
+        resp = call_llm(f"""
+Question: {q} 
+Find the most relevant file from: {file_summaries}
+If none, explain why
+
+Output in code fence:
+```yaml
+think: >
+    reasoning about relevance
+has_relevant: true/false
+most_relevant: filename if relevant
+```""")
+        yaml_str = resp.split("```yaml")[1].split("```")[0].strip()
+        result = yaml.safe_load(yaml_str)
+        
+        # Validate response
+        assert isinstance(result, dict)
+        assert "think" in result
+        assert "has_relevant" in result
+        assert isinstance(result["has_relevant"], bool)
+        
+        if result["has_relevant"]:
+            assert "most_relevant" in result
+            assert result["most_relevant"] in filenames
+            
+        return result
+
+    def exec_fallback(self, p, exc): return {"think":"error","has_relevant":False}
+    def post(self, s, pr, res):
+        q, _ = pr
+        if not q:
+            print("No question asked"); return "end"
+        if res["has_relevant"]:
+            s["question"], s["relevant_file"] = q, res["most_relevant"]
+            print("Relevant file:", res["most_relevant"])
+            return "answer"
+        else:
+            print("No relevant file:", res["think"])
+            return "retry"
+
+class AnswerQuestion(Node):
+    def prep(self, s):
+        return s['question'], s['data'][s['relevant_file']]
+    def exec(self, p):
+        q, txt = p
+        return call_llm(f"Question: {q}\nText: {txt}\nAnswer in 50 words.")
+    def post(self, s, pr, ex):
+        print("Answer:", ex)
+
+class NoOp(Node): pass
+
+frf = FindRelevantFile(max_retries=3)
+aq = AnswerQuestion()
+noop = NoOp()
+
+frf - "answer" >> aq >> frf
+frf - "retry"  >> frf
+frf - "end"    >> noop
+
+qa = Flow(start=frf)
+qa.run(shared)
+```
\ No newline at end of file
diff --git a/docs/example.md b/docs/example.md
new file mode 100644
index 0000000..934daf3
--- /dev/null
+++ b/docs/example.md
@@ -0,0 +1,6 @@
+---
+layout: default
+title: "Example"
+nav_order: 5
+has_children: true
+---
\ No newline at end of file
diff --git a/docs/index.md b/docs/index.md
index f1c97f3..1e94552 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -61,4 +61,4 @@ We model the LLM workflow as a **Nested Directed Graph**:
 
 ## Example Projects
 
-- Coming soon ... 
+- [Summarization + QA agent for Paul Graham Essay](./essay.md)
diff --git a/docs/llm.md b/docs/llm.md
index 9f906ea..2633d5b 100644
--- a/docs/llm.md
+++ b/docs/llm.md
@@ -1,7 +1,7 @@
 ---
 layout: default
 title: "LLM Wrapper"
-parent: "Preparation"
+parent: "Details"
 nav_order: 1
 ---
 
diff --git a/docs/preparation.md b/docs/preparation.md
index 9e8cbf9..6e1c0fe 100644
--- a/docs/preparation.md
+++ b/docs/preparation.md
@@ -1,6 +1,6 @@
 ---
 layout: default
-title: "Preparation"
+title: "Details"
 nav_order: 3
 has_children: true
 ---
\ No newline at end of file
diff --git a/docs/tool.md b/docs/tool.md
index 2a5eb5a..ecc8e13 100644
--- a/docs/tool.md
+++ b/docs/tool.md
@@ -1,7 +1,7 @@
 ---
 layout: default
 title: "Tool"
-parent: "Preparation"
+parent: "Details"
 nav_order: 2
 ---