# Graphsignal: Observability for AI Stack
[](https://github.com/graphsignal/graphsignal-python/blob/main/LICENSE)
[](https://github.com/graphsignal/graphsignal-python)
Graphsignal is an observability platform for AI agents and LLM-powered applications. It helps developers ensure AI applications run as expected and users have the best experience. With Graphsignal, developers can:
* Trace generations, runs, and sessions with full AI context.
* Score any user interactions and application execution.
* See latency breakdowns and distributions.
* Analyze model API costs for deployments, models, or users.
* Get notified about errors and anomalies.
* Monitor API, compute, and GPU utilization.
[](https://graphsignal.com/)
Learn more at [graphsignal.com](https://graphsignal.com).
## Install
Install Graphsignal library.
```bash
pip install --upgrade graphsignal
```
## Configure
Configure Graphsignal tracer by specifying your API key directly or via `GRAPHSIGNAL_API_KEY` environment variable.
```python
import graphsignal
graphsignal.configure(api_key='my-api-key', deployment='my-app')
```
To get an API key, sign up for a free account at [graphsignal.com](https://graphsignal.com). The key can then be found in your account's [Settings / API Keys](https://app.graphsignal.com/settings/api-keys) page.
Alternatively, you can add Graphsignal tracer at command line, when running your module or script. Environment variables `GRAPHSIGNAL_API_KEY` and `GRAPHSIGNAL_DEPLOYMENT` must be set.
```bash
python -m graphsignal <script>
```
```bash
python -m graphsignal -m <module>
```
## Integrate
Graphsignal **auto-instruments** and traces libraries and frameworks, such as [OpenAI](https://graphsignal.com/docs/integrations/openai/) and [LangChain](https://graphsignal.com/docs/integrations/langchain/). Traces, errors, and data, such as prompts and completions, are automatically recorded and available for analysis at [app.graphsignal.com](https://app.graphsignal.com/).
Refer to the guides below for detailed information on:
* [Session Tracking](https://graphsignal.com/docs/guides/session-tracking/)
* [User Tracking](https://graphsignal.com/docs/guides/user-tracking/)
* [Manual Tracing](https://graphsignal.com/docs/guides/manual-tracing/)
* [Scores and Feedback](https://graphsignal.com/docs/guides/scores-and-feedback/)
* [Cost and Usage Monitoring](https://graphsignal.com/docs/guides/cost-and-usage-monitoring/)
See [API reference](https://graphsignal.com/docs/reference/python-api/) for full documentation.
Some integration examples are available in [examples](https://github.com/graphsignal/examples) repo.
## Analyze
[Log in](https://app.graphsignal.com/) to Graphsignal to monitor and analyze your application and monitor for issues.
## Overhead
Graphsignal tracer is very lightweight. The overhead per trace is measured to be less than 100 microseconds.
## Security and Privacy
Graphsignal tracer can only open outbound connections to `api.graphsignal.com` and send data, no inbound connections or commands are possible.
Payloads, such as prompts and completions, are recorded by default in case of automatic tracing. To disable, set `record_payloads=False` in `graphsignal.configure`.
## Troubleshooting
To enable debug logging, add `debug_mode=True` to `configure()`. If the debug log doesn't give you any hints on how to fix a problem, please report it to our support team via your account.
In case of connection issues, please make sure outgoing connections to `https://api.graphsignal.com` are allowed.
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