Setup Install the Braintrust SDK and AgentScope, then set your API keys. The examples below use OpenAI. ```bash uv theme={"theme":{"light":"github-light","dark":"github-dark-dimmed"}} uv add braintrust agentscope ``` ```bash pip theme={"theme":{"light":"github-light","dark":"github-dark-dimmed"}} pip install braintrust agentscope ``` ```bash title=".env" theme={"theme":{"light":"github-light","dark":"github-dark-dimmed"}} BRAINTRUST_API_KEY= OPENAI_API_KEY= ``` Auto-instrumentation To trace AgentScope alongside Braintrust’s other supported libraries, call `braintrust.auto_instrument()` before importing AgentScope. Because AgentScope uses `from agentscope... import` style imports, the call must come first so the patches are in place. ```python Python theme={"theme":{"light":"github-light","dark":"github-dark-dimmed"}} import asyncio import braintrust braintrust.auto_instrument() braintrust.init_logger(project="agentscope-example") # Replace with your project name from agentscope.agent import ReActAgent from agentscope.formatter import OpenAIChatFormatter from agentscope.memory import InMemoryMemory from agentscope.message import Msg from agentscope.model import OpenAIChatModel from agentscope.tool import Toolkit async def main() -> None: agent = ReActAgent( name="Friday", sys_prompt="You are a concise assistant. Answer in one sentence.", model=OpenAIChatModel(model_name="gpt-4o-mini", stream=False), formatter=OpenAIChatFormatter(), toolkit=Toolkit(), memory=InMemoryMemory(), ) response = await agent(Msg(name="user", content="Say hello in exactly two words.", role="user")) print(response.content if response is not None else "(no response)") if __name__ == "__main__": asyncio.run(main()) ``` To trace AgentScope without auto-instrumenting other libraries, use `setup_agentscope()` instead of `braintrust.auto_instrument()`. It initializes a Braintrust logger when one isn’t already active. ```python Python theme={"theme":{"light":"github-light","dark":"github-dark-dimmed"}} from braintrust.integrations.agentscope import setup_agentscope setup_agentscope(project_name="agentscope-example") from agentscope.agent import ReActAgent from agentscope.formatter import OpenAIChatFormatter from agentscope.memory import InMemoryMemory from agentscope.message import Msg from agentscope.model import OpenAIChatModel from agentscope.tool import Toolkit ``` What Braintrust traces Braintrust traces AgentScope runs as nested spans that mirror the actual execution flow. Captured spans: * Agent run spans (`.reply`, such as `Friday.reply`), with the call input, agent class, and response. Both the 1.x `AgentBase.__call__` and 2.x `Agent.reply` paths are instrumented. * Pipeline spans (`sequential_pipeline.run` and `fanout_pipeline.run`), with the names of the participating agents. Child agent, tool, and model spans nest underneath. * Tool execution spans (`.execute`, such as `execute_python_code.execute`), with the tool call, toolkit class, and output, including streamed tool output. * Model call spans (`.call`, such as `OpenAIChat.call`), with messages, tools, and model metadata (model name, provider, and request parameters); response content and token usage (prompt, completion, and total) on completion. * Streaming model responses and tool outputs, aggregated into the final span output, with token metrics on model spans. * Errors on failed agent runs, pipelines, tool calls, and model calls. The integration instruments AgentScope’s built-in OpenAI, DashScope, Anthropic, Ollama, Gemini, and Trinity chat models. Resources * [AgentScope GitHub repository](https://github.com/modelscope/agentscope) * [Braintrust Python SDK reference](/sdks/python/versions/latest) * [Trace LLM calls](/instrument/trace-llm-calls)