chore: update examples of crewai

python/tracing/crewai/.env.example

@@ -1,3 +1,3 @@
-RESPAN_API_KEY=your-api-key-here
+RESPAN_API_KEY=your-respan-api-key
 RESPAN_BASE_URL=https://api.respan.ai/api
-OPENAI_API_KEY=your-api-key-here
+RESPAN_MODEL=gpt-4o-mini

python/tracing/crewai/01_basic_crew.py

@@ -0,0 +1,50 @@
+"""Basic CrewAI run with Respan tracing and gateway routing."""
+
+from respan import workflow
+
+from _shared import build_llm, create_respan, print_result, result_text, run_with_attributes
+
+WORKFLOW_NAME = "crewai_01_basic_crew"
+
+
+@workflow(name=WORKFLOW_NAME)
+def run_basic_crew(context) -> str:
+    from crewai import Agent, Crew, Process, Task
+
+    llm = build_llm(context.settings)
+    researcher = Agent(
+        role="AI Researcher",
+        goal="Explain CrewAI and Respan in one practical paragraph",
+        backstory="You write concise technical explanations for Python developers.",
+        llm=llm,
+        verbose=False,
+    )
+    task = Task(
+        description="Explain how CrewAI and Respan work together for tracing.",
+        expected_output="One concise paragraph for Python developers.",
+        agent=researcher,
+    )
+    crew = Crew(
+        agents=[researcher],
+        tasks=[task],
+        process=Process.sequential,
+        verbose=False,
+    )
+    return result_text(crew.kickoff())
+
+
+def main() -> None:
+    context = create_respan(
+        app_name="crewai-01-basic-crew",
+        example_name="01_basic_crew",
+        workflow_name=WORKFLOW_NAME,
+    )
+    output = run_with_attributes(context, lambda: run_basic_crew(context))
+    print_result("Crew output", output)
+    print_result("Workflow name", WORKFLOW_NAME)
+    print_result("Example run id", context.run_id)
+    context.respan.flush()
+
+
+if __name__ == "__main__":
+    main()

python/tracing/crewai/02_tool_use.py

@@ -0,0 +1,78 @@
+"""CrewAI tool-use example with Respan tracing."""
+
+from respan import workflow
+
+from _shared import build_llm, create_respan, print_result, result_text, run_with_attributes
+
+WORKFLOW_NAME = "crewai_02_tool_use"
+
+
+def lookup_city_weather(city: str) -> str:
+    """Look up deterministic weather for a city."""
+    weather = {
+        "Paris": "Sunny, 22C",
+        "Tokyo": "Cloudy, 18C",
+        "New York": "Clear, 20C",
+    }
+    return weather.get(city, "Mild, 21C")
+
+
+def lookup_city_population(city: str) -> str:
+    """Look up deterministic population data for a city."""
+    populations = {
+        "Paris": "about 2.1 million people",
+        "Tokyo": "about 14 million people",
+        "New York": "about 8.3 million people",
+    }
+    return populations.get(city, "unknown population")
+
+
+@workflow(name=WORKFLOW_NAME)
+def run_tool_crew(context) -> str:
+    from crewai import Agent, Crew, Process, Task
+    from crewai.tools import tool
+
+    weather_tool = tool("lookup_city_weather")(lookup_city_weather)
+    population_tool = tool("lookup_city_population")(lookup_city_population)
+    llm = build_llm(context.settings)
+
+    researcher = Agent(
+        role="City Researcher",
+        goal="Use tools to gather city facts before answering",
+        backstory="You collect factual city details using available tools.",
+        tools=[weather_tool, population_tool],
+        llm=llm,
+        verbose=False,
+    )
+    task = Task(
+        description=(
+            "Research the weather and population for Paris, then summarize the "
+            "result in two short bullets. Use the provided tools."
+        ),
+        expected_output="Two short bullets with weather and population details.",
+        agent=researcher,
+    )
+    crew = Crew(
+        agents=[researcher],
+        tasks=[task],
+        process=Process.sequential,
+        verbose=False,
+    )
+    return result_text(crew.kickoff())
+
+
+def main() -> None:
+    context = create_respan(
+        app_name="crewai-02-tool-use",
+        example_name="02_tool_use",
+        workflow_name=WORKFLOW_NAME,
+    )
+    output = run_with_attributes(context, lambda: run_tool_crew(context))
+    print_result("Crew output", output)
+    print_result("Workflow name", WORKFLOW_NAME)
+    print_result("Example run id", context.run_id)
+    context.respan.flush()
+
+
+if __name__ == "__main__":
+    main()

python/tracing/crewai/03_attributes.py

@@ -0,0 +1,54 @@
+"""CrewAI example that attaches user, thread, and metadata attributes."""
+
+from respan import workflow
+
+from _shared import build_llm, create_respan, print_result, result_text, run_with_attributes
+
+WORKFLOW_NAME = "crewai_03_attributes"
+
+
+@workflow(name=WORKFLOW_NAME)
+def run_attribute_crew(context) -> str:
+    from crewai import Agent, Crew, Process, Task
+
+    llm = build_llm(context.settings)
+    analyst = Agent(
+        role="Support Analyst",
+        goal="Classify a support request and suggest the next action",
+        backstory="You help support teams triage incoming tickets quickly.",
+        llm=llm,
+        verbose=False,
+    )
+    task = Task(
+        description=(
+            "A customer says: 'The dashboard loads slowly after I add a new "
+            "integration.' Classify the issue and suggest one next action."
+        ),
+        expected_output="A category and one recommended next action.",
+        agent=analyst,
+    )
+    crew = Crew(
+        agents=[analyst],
+        tasks=[task],
+        process=Process.sequential,
+        verbose=False,
+    )
+    return result_text(crew.kickoff())
+
+
+def main() -> None:
+    context = create_respan(
+        app_name="crewai-03-attributes",
+        example_name="03_attributes",
+        workflow_name=WORKFLOW_NAME,
+        metadata={"scenario": "support_triage"},
+    )
+    output = run_with_attributes(context, lambda: run_attribute_crew(context))
+    print_result("Crew output", output)
+    print_result("Workflow name", WORKFLOW_NAME)
+    print_result("Example run id", context.run_id)
+    context.respan.flush()
+
+
+if __name__ == "__main__":
+    main()

python/tracing/crewai/README.md

@@ -0,0 +1,55 @@
+# CrewAI Respan Integration Examples
+
+These examples demonstrate CrewAI tracing with `respan-instrumentation-crewai`.
+They initialize Respan before importing CrewAI, route OpenAI-compatible LLM calls
+through the Respan gateway, and attach stable workflow metadata so each run is
+easy to find in Respan.
+
+## Setup
+
+Install dependencies:
+
+```bash
+cd python/tracing/crewai
+pip install -r requirements.txt
+```
+
+Use the repository root `.env` file. At minimum it should include:
+
+```bash
+RESPAN_API_KEY=your-respan-api-key
+RESPAN_BASE_URL=https://api.respan.ai/api
+```
+
+`RESPAN_MODEL` is optional and defaults to `gpt-4o-mini`. The examples set
+`OPENAI_API_KEY` and `OPENAI_BASE_URL` from the Respan values at runtime, so a
+separate provider key is not required when using the Respan gateway.
+
+## Examples
+
+| Example | Workflow name | Description |
+| --- | --- | --- |
+| `01_basic_crew.py` | `crewai_01_basic_crew` | Single-agent CrewAI task with Respan tracing |
+| `02_tool_use.py` | `crewai_02_tool_use` | CrewAI agent using deterministic Python tools |
+| `03_attributes.py` | `crewai_03_attributes` | CrewAI task with customer, thread, and metadata attributes |
+
+Run one example:
+
+```bash
+python 01_basic_crew.py
+```
+
+Run the full set in isolated Python processes:
+
+```bash
+python run_all.py
+```
+
+Each script prints its workflow name and `RESPAN_EXAMPLE_RUN_ID`. Use those
+values to find the corresponding spans in Respan.
+
+## Further reading
+
+- [respan-instrumentation-crewai](https://pypi.org/project/respan-instrumentation-crewai/)
+- [respan-ai](https://pypi.org/project/respan-ai/)
+- [CrewAI documentation](https://docs.crewai.com/)