> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://respan.ai/docs/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://respan.ai/docs/_mcp/server.

# Smolagents (tracing)

> Trace Smolagents workflows with Respan — auto-instrumented spans, gateway routing, and full observability.

[Smolagents](https://huggingface.co/docs/smolagents) is HuggingFace's lightweight agent framework that lets you build agents with code-based actions. It focuses on simplicity and supports both code agents and tool-calling agents. Respan gives you full observability over every agent run, planning step, code execution, tool call, and LLM generation — and gateway routing through the OpenAI-compatible Respan endpoint.

#### Set up Respan

Create an account at [platform.respan.ai](https://platform.respan.ai) and grab an [API key](https://platform.respan.ai/platform/gateway/api-keys).

Run `npx @respan/cli setup` to set up with your coding agent.

#### Use Respan Gateway

See [Smolagents gateway setup](/docs/gateway/smolagents) to route this integration through the Respan gateway.

#### Example projects

* [Example projects root](https://github.com/respanai/respan-example-projects)

## Setup

#### Install packages

```bash
pip install respan-ai respan-instrumentation-smolagents
```

#### Set environment variables

```bash
export OPENAI_API_KEY="YOUR_OPENAI_API_KEY"
export RESPAN_API_KEY="YOUR_RESPAN_API_KEY"
```

`OPENAI_API_KEY` is used for LLM requests. `RESPAN_API_KEY` is used to export traces to Respan.

#### Initialize and run

```python
from respan import Respan, workflow
from respan_instrumentation_smolagents import SmolagentsInstrumentor
from smolagents import CodeAgent, DuckDuckGoSearchTool, LiteLLMModel

respan = Respan(instrumentations=[SmolagentsInstrumentor()])

model = LiteLLMModel(model_id="openai/gpt-4o-mini")
agent = CodeAgent(
    tools=[DuckDuckGoSearchTool()],
    model=model,
)

@workflow(name="smolagents_code_agent_workflow")
def run_agent():
    return agent.run("What is the latest news about AI agents?")

result = run_agent()
print(result)
respan.shutdown()
```

#### View your trace

Open the [Traces page](https://platform.respan.ai/platform/traces) to see your Smolagents workflow, identified by `smolagents_code_agent_workflow`, with planning steps, code execution, tool calls, and LLM generations.

## Configuration

| Parameter             | Type           | Default | Description                                                            |
| --------------------- | -------------- | ------- | ---------------------------------------------------------------------- |
| `api_key`             | `str \| None`  | `None`  | Falls back to `RESPAN_API_KEY` env var.                                |
| `base_url`            | `str \| None`  | `None`  | Falls back to `RESPAN_BASE_URL` env var.                               |
| `instrumentations`    | `list`         | `[]`    | Plugin instrumentations to activate (e.g. `SmolagentsInstrumentor()`). |
| `customer_identifier` | `str \| None`  | `None`  | Default customer identifier for all spans.                             |
| `metadata`            | `dict \| None` | `None`  | Default metadata attached to all spans.                                |
| `environment`         | `str \| None`  | `None`  | Environment tag (e.g. `"production"`).                                 |

## Attributes

### In Respan()

Set defaults at initialization — these apply to all spans.

```python
from respan import Respan
from respan_instrumentation_smolagents import SmolagentsInstrumentor

respan = Respan(
    instrumentations=[SmolagentsInstrumentor()],
    customer_identifier="user_123",
    metadata={"service": "smolagents-api", "version": "1.0.0"},
)
```

### With propagate_attributes

Override per-request using a context scope.

```python
from respan import Respan, propagate_attributes
from respan_instrumentation_smolagents import SmolagentsInstrumentor

respan = Respan(instrumentations=[SmolagentsInstrumentor()])

def handle_request(user_id: str, message: str):
    with propagate_attributes(
        customer_identifier=user_id,
        thread_identifier="conv_abc_123",
        metadata={"plan": "pro"},
    ):
        print(agent.run(message))
```

| Attribute             | Type   | Description                                           |
| --------------------- | ------ | ----------------------------------------------------- |
| `customer_identifier` | `str`  | Identifies the end user in Respan analytics.          |
| `thread_identifier`   | `str`  | Groups related messages into a conversation.          |
| `metadata`            | `dict` | Custom key-value pairs. Merged with default metadata. |