Update terminology

src/crewai/crew.py

@@ -94,6 +94,7 @@ class Crew(BaseModel):
         default_factory=TaskOutputStorageHandler
     )
 
+    name: Optional[str] = Field(default="")
     cache: bool = Field(default=True)
     model_config = ConfigDict(arbitrary_types_allowed=True)
     tasks: List[Task] = Field(default_factory=list)

src/crewai/pipeline/pipeline.py

@@ -5,15 +5,28 @@ from pydantic import BaseModel, Field
 
 from crewai.crew import Crew
 from crewai.crews.crew_output import CrewOutput
+from crewai.pipeline.pipeline_output import PipelineOutput
 
 """
 Pipeline Terminology:
 Pipeline: The overall structure that defines a sequence of operations.
 Stage: A distinct part of the pipeline, which can be either sequential or parallel.
+Run: A specific execution of the pipeline for a given set of inputs, representing a single instance of processing through the pipeline.
 Branch: Parallel executions within a stage (e.g., concurrent crew operations).
-Stream: The journey of an individual input through the entire pipeline.
+Trace: The journey of an individual input through the entire pipeline.
 
 Example pipeline structure:
+crew1 >> crew2 >> crew3
+
+This represents a pipeline with three sequential stages:
+1. crew1 is the first stage, which processes the input and passes its output to crew2.
+2. crew2 is the second stage, which takes the output from crew1 as its input, processes it, and passes its output to crew3.
+3. crew3 is the final stage, which takes the output from crew2 as its input and produces the final output of the pipeline.
+
+Each input creates its own run, flowing through all stages of the pipeline.
+Multiple runs can be processed concurrently, each following the defined pipeline structure.
+
+Another example pipeline structure:
 crew1 >> [crew2, crew3] >> crew4
 
 This represents a pipeline with three stages:
@@ -21,8 +34,8 @@ This represents a pipeline with three stages:
 2. A parallel stage with two branches (crew2 and crew3 executing concurrently)
 3. Another sequential stage (crew4)
 
-Each input creates its own stream, flowing through all stages of the pipeline.
-Multiple streams can be processed concurrently, each following the defined pipeline structure.
+Each input creates its own run, flowing through all stages of the pipeline.
+Multiple runs can be processed concurrently, each following the defined pipeline structure.
 """
 
 
@@ -31,44 +44,43 @@ class Pipeline(BaseModel):
         ..., description="List of crews representing stages to be executed in sequence"
     )
 
-    async def process_streams(
-        self, stream_inputs: List[Dict[str, Any]]
+    async def process_runs(
+        self, run_inputs: List[Dict[str, Any]]
     ) -> List[List[CrewOutput]]:
         """
-        Process multiple streams in parallel, with each stream going through all stages.
+        Process multiple runs in parallel, with each run going through all stages.
         """
+        pipeline_output = PipelineOutput()
 
-        async def process_single_stream(
-            stream_input: Dict[str, Any]
-        ) -> List[CrewOutput]:
-            print("current_input in stream", stream_input)
+        async def process_single_run(run_input: Dict[str, Any]) -> List[CrewOutput]:
+            print("current_input in run", run_input)
             stage_outputs = []
 
             for stage in self.stages:
                 if isinstance(stage, Crew):
                     # Process single crew
-                    stage_output = await stage.kickoff_async(inputs=stream_input)
+                    stage_output = await stage.kickoff_async(inputs=run_input)
                     stage_outputs = [stage_output]
                 else:
                     # Process each crew in parallel
                     parallel_outputs = await asyncio.gather(
-                        *[crew.kickoff_async(inputs=stream_input) for crew in stage]
+                        *[crew.kickoff_async(inputs=run_input) for crew in stage]
                     )
                     stage_outputs = parallel_outputs
 
                 # Convert all CrewOutputs from stage into a dictionary for next stage
-                # and update original stream_input dictionary with new values
+                # and update original run_input dictionary with new values
                 stage_output_dicts = [output.to_dict() for output in stage_outputs]
                 for stage_dict in stage_output_dicts:
-                    stream_input.update(stage_dict)
-                    print("UPDATING stream_input - new values:", stream_input)
+                    run_input.update(stage_dict)
+                    print("UPDATING run_input - new values:", run_input)
 
-            # Return all CrewOutputs from this stream
+            # Return all CrewOutputs from this run
             return stage_outputs
 
-        # Process all streams in parallel
+        # Process all runs in parallel
         return await asyncio.gather(
-            *(process_single_stream(input_data) for input_data in stream_inputs)
+            *(process_single_run(input_data) for input_data in run_inputs)
         )
 
     def __rshift__(self, other: Any) -> "Pipeline":
@@ -89,4 +101,4 @@ class Pipeline(BaseModel):
 async def run_pipeline(
     pipeline: Pipeline, inputs: List[Dict[str, Any]]
 ) -> List[List[CrewOutput]]:
-    return await pipeline.process_streams(inputs)
+    return await pipeline.process_runs(inputs)

tests/pipeline/test_pipeline.py

@@ -80,7 +80,7 @@ async def test_pipeline_process_streams_single_input(mock_crew_factory):
     mock_crew = mock_crew_factory()
     pipeline = Pipeline(stages=[mock_crew])
     input_data = [{"key": "value"}]
-    pipeline_result = await pipeline.process_streams(input_data)
+    pipeline_result = await pipeline.process_runs(input_data)
 
     mock_crew.kickoff_async.assert_called_once_with(inputs={"key": "value"})
     for stream_result in pipeline_result:
@@ -104,14 +104,12 @@ async def test_pipeline_process_streams_multiple_inputs(mock_crew_factory):
     mock_crew = mock_crew_factory()
     pipeline = Pipeline(stages=[mock_crew])
     input_data = [{"key1": "value1"}, {"key2": "value2"}]
-    pipeline_result = await pipeline.process_streams(input_data)
+    pipeline_result = await pipeline.process_runs(input_data)
 
     assert mock_crew.kickoff_async.call_count == 2
     assert len(pipeline_result) == 2
-    for stream_result in pipeline_result:
-        assert all(
-            isinstance(stream_output, CrewOutput) for stream_output in stream_result
-        )
+    for run_result in pipeline_result:
+        assert all(isinstance(run_output, CrewOutput) for run_output in run_result)
 
 
 @pytest.mark.asyncio
@@ -126,7 +124,7 @@ async def test_pipeline_with_parallel_stages(mock_crew_factory):
     pipeline = Pipeline(stages=[crew1, [crew2, crew3]])
     input_data = [{"initial": "data"}]
 
-    pipeline_result = await pipeline.process_streams(input_data)
+    pipeline_result = await pipeline.process_runs(input_data)
 
     crew1.kickoff_async.assert_called_once_with(
         inputs={"initial": "data", "key": "value"}
@@ -188,13 +186,13 @@ async def test_pipeline_data_accumulation(mock_crew_factory):
 
     pipeline = Pipeline(stages=[crew1, crew2])
     input_data = [{"initial": "data"}]
-    pipeline_result = await pipeline.process_streams(input_data)
+    pipeline_result = await pipeline.process_runs(input_data)
 
     assert len(pipeline_result) == 1
     print("RESULT: ", pipeline_result)
-    for stream_result in pipeline_result:
-        print("STREAM RESULT: ", stream_result)
-        assert stream_result[0].json_dict == {
+    for run_result in pipeline_result:
+        print("RUN RESULT: ", run_result)
+        assert run_result[0].json_dict == {
             "initial": "data",
             "key1": "value1",
             "key2": "value2",