Unify flow streaming frame items

docs/edge/en/learn/streaming-runtime-contract.mdx

@@ -28,6 +28,8 @@ frame.timestamp    # event timestamp
 frame.parent_id    # parent event id, when available
 frame.previous_id  # previous event id, when available
 frame.data         # event payload
+frame.event        # alias for frame.data
+frame.content      # printable text for token-like frames, otherwise ""
 ```
 
 The `channel` field is the fastest way to route frames in consumers:
@@ -45,7 +47,7 @@ The `channel` field is the fastest way to route frames in consumers:
 
 ## Stream a Flow
 
-Use `stream_events()` to run a Flow and iterate over all frames:
+Set `stream=True` on a Flow to make `kickoff()` return a stream session:
 
 ```python
 from crewai.flow import Flow, start
@@ -57,18 +59,22 @@ class ReportFlow(Flow):
         return "done"
 
 
-flow = ReportFlow()
-stream = flow.stream_events()
+flow = ReportFlow(stream=True)
+stream = flow.kickoff()
 
 with stream:
-    for frame in stream.events:
-        print(frame.seq, frame.channel, frame.type, frame.data)
+    for chunk in stream:
+        print(chunk.content, end="", flush=True)
+        if chunk.type == "tool_usage_started":
+            print(chunk.event["tool_name"])
 
 result = stream.result
 ```
 
 You must consume the stream before reading `stream.result`. Accessing the result early raises a `RuntimeError` so consumers do not accidentally treat a partial run as complete.
 
+You can also call `flow.stream_events(...)` directly when you want streaming for a single invocation without setting `stream=True` on the Flow instance.
+
 ## Filter by Channel
 
 `StreamSession` exposes channel projections that preserve global frame order within the selected channel:
@@ -78,7 +84,7 @@ stream = flow.stream_events()
 
 with stream:
     for frame in stream.llm:
-        print(frame.data.get("chunk", ""), end="", flush=True)
+        print(frame.content, end="", flush=True)
 
 result = stream.result
 ```
@@ -105,8 +111,8 @@ flow = ReportFlow()
 stream = flow.astream()
 
 async with stream:
-    async for frame in stream.events:
-        print(frame.channel, frame.type)
+    async for chunk in stream.events:
+        print(chunk.channel, chunk.type, chunk.content)
 
 result = stream.result
 ```
@@ -115,14 +121,14 @@ The async session has the same projections as the sync session.
 
 ## Stream a Direct LLM Call
 
-`llm.call(...)` still returns the final assembled result. Use `llm.stream_call(...)` when you want to iterate over chunks as they arrive:
+`llm.call(...)` still returns the final assembled result. Use `llm.stream_events(...)` when you want to iterate over chunks as they arrive while keeping the structured event payload:
 
 ```python
 from crewai import LLM
 
 
 llm = LLM(model="gpt-4o-mini")
-chunks = llm.stream_call(
+stream = llm.stream_events(
     messages=[
         {
             "role": "user",
@@ -131,26 +137,14 @@ chunks = llm.stream_call(
     ]
 )
 
-for chunk in chunks:
-    print(chunk.content, end="", flush=True)
-
-result = chunks.result
-```
-
-Use `llm.stream_events(...)` when a runtime needs the full `StreamFrame` envelope rather than text chunks:
-
-```python
-stream = llm.stream_events("Explain CrewAI streaming in two short sentences.")
-
 with stream:
-    for frame in stream.llm:
-        if frame.type == "llm_stream_chunk":
-            print(frame.data.get("chunk", ""), end="", flush=True)
+    for chunk in stream:
+        print(chunk.content, end="", flush=True)
 
 result = stream.result
 ```
 
-Both methods temporarily enable streaming for the wrapped call and restore the LLM's previous `stream` setting afterward. Provider integrations continue to emit the underlying LLM stream events; these helpers provide a common iterator API over those events for every LLM provider.
+`llm.stream_events(...)` temporarily enables streaming for the wrapped call and restores the LLM's previous `stream` setting afterward. Provider integrations continue to emit the underlying LLM stream events; this helper provides a common iterator API over those events for every LLM provider.
 
 ## Conversational Turns
 
@@ -172,7 +166,7 @@ stream = flow.stream_turn("What can you help me with?", session_id="session-1")
 with stream:
     for frame in stream.events:
         if frame.channel == "llm" and frame.type == "llm_stream_chunk":
-            print(frame.data.get("chunk", ""), end="", flush=True)
+            print(frame.content, end="", flush=True)
 
 reply = stream.result
 ```