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Axon

(★ 155)

OpenTelemetry-native LLM observability CLI. Point any OTEL exporter at it and watch your LLM/agent traces in real time.

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# Axon

<p align="center">
  <img src="dashboard/public/favicon.svg" alt="Axon Logo" width="240"/>
</p>

Axon is a local observability tool for LangChain and OpenTelemetry-instrumented AI agents. It receives trace data from your application, stores it on your machine, and gives you a real-time dashboard to monitor and debug every LLM call, tool invocation, and chain execution. No cloud account is required and no data leaves your environment.

---

### Features

**Overview - shows your OTLP endpoint and integration snippets the moment Axon starts**
![Overview](https://raw.githubusercontent.com/langchain-tracer/Axon/main/docs/screenshots/overview.png)

### Trace Views

**Transcript — renders the run as a conversation with tool call inputs and outputs inline**
![Transcript](https://raw.githubusercontent.com/langchain-tracer/Axon/main/docs/screenshots/transcript.png)

**Waterfall — each span as a horizontal bar on a shared time axis with a detail panel**
![Waterfall](https://raw.githubusercontent.com/langchain-tracer/Axon/main/docs/screenshots/waterfall.png)

**Tree — parent-child span hierarchy with type icons, model names, and token counts**
![Tree](https://raw.githubusercontent.com/langchain-tracer/Axon/main/docs/screenshots/tree.png)

**Raw — full JSON of every span with a copy button**
![Raw](https://raw.githubusercontent.com/langchain-tracer/Axon/main/docs/screenshots/Raw.png)

---

## How it works

Axon runs entirely on your machine. When you start it, a single server comes up that handles two responsibilities: it accepts incoming OTLP trace data from your application, and it serves the dashboard that visualises those traces. The server writes all trace data to a local SQLite database scoped to your project directory. The dashboard connects to the server over Server-Sent Events so new spans appear in real time as your agent runs.

The tool is distributed as three packages that work together. `@axon-ai/cli` is the command-line interface you interact with. `@axon-ai/backend` is the Express server and database layer. `@axon-ai/dashboard` is the React web interface. When you install the CLI, it bundles the other two so you only need one install.

---

## Getting started

**Prerequisites**

- Node.js 18 or higher
- A LangChain or OpenTelemetry-instrumented application running in development

**Install the CLI**

```bash
npm install -g @axon-ai/cli
```

**Initialise your project**

Run this inside your project directory. It creates a `.axon-ai/` folder that holds your configuration and local trace database.

```bash
axon-ai init --project my-app
```

**Start Axon**

```bash
axon-ai start
```

This launches the backend server and opens the dashboard in your browser. By default the server runs on port 4000. The OTLP ingest endpoint is available at `http://localhost:4000/v1/traces`.

**Point your application at Axon**

For Node.js applications using `@axon-ai/langchain-tracer`:

```bash
npm install @axon-ai/langchain-tracer
```

```js
import { createAutoTracer } from '@axon-ai/langchain-tracer';
createAutoTracer({ endpoint: 'http://localhost:4000' });
```

For Python applications using OpenLLMetry:

```bash
pip install traceloop-sdk
```

```python
from traceloop.sdk import Traceloop
Traceloop.init(
  app_name="my-agent",
  api_endpoint="http://localhost:4000",
)
```

Run your application and switch to the dashboard. Traces appear automatically as your agent executes.

---

## CLI reference

| Command | Description |
|---|---|
| `axon-ai init` | Initialise Axon in the current directory |
| `axon-ai start` | Start the backend and dashboard |
| `axon-ai status` | Check whether services are running |
| `axon-ai stop` | Stop all running services |
| `axon-ai version` | Show version information |

**Options for `axon-ai init`**

- `--project <name>` sets the project name used to group traces. Defaults to `default`.
- `--auto-start` launches the dashboard immediately after initialisation.

**Options for `axon-ai start`**

- `--port <number>` sets the port for both the server and dashboard. Defaults to `4000`.
- `--no-open` prevents the browser from opening automatically.
- `--project <name>` tags incoming traces with a project name.

---

## Configuration

`axon-ai init` creates a `.axon-ai/config.json` file in your project root. You can edit it directly.

```json
{
  "project": "my-app",
  "backend": {
    "port": 4000,
    "host": "localhost"
  }
}
```

Trace data is stored in `.axon-ai/traces.db`. This file is local to your machine and is not committed to source control. Add `.axon-ai/traces.db` to your `.gitignore` if you want to keep it out of your repository.

---

## Docker

If you prefer not to install Node.js, you can run the full stack with Docker Desktop.

```bash
make start      # start backend and dashboard
make stop       # stop all services
make logs       # stream logs from all containers
make restart    # restart services
make clean      # remove containers and volumes
```

The dashboard is available at `http://localhost:8080` and the OTLP endpoint at `http://localhost:3000/v1/traces`.

To reset the trace database:

```bash
make stop
docker volume rm axon_axon-data
make start
```

---

## Troubleshooting

**Port already in use**

Another process is using the default port. Pass a different port with `axon-ai start --port 5000`.

**Dashboard does not open**

Run `axon-ai status` to confirm the server started successfully. If it did, open `http://localhost:4000` manually in your browser.

**Traces are not appearing**

Confirm your application's OTLP exporter is pointing at the correct endpoint (`http://localhost:<port>/v1/traces`). Check the terminal where `axon-ai start` is running for any ingestion errors.

**Database issues**

If the database becomes corrupted, stop Axon, delete `.axon-ai/traces.db`, and restart. All historical traces will be lost but the service will recover cleanly.

---

## Contributing

Contributions are welcome from developers of all experience levels.

To get started, fork the repository. The project is a Node.js monorepo with three workspaces: `packages/cli`, `backend`, and `dashboard`. Install dependencies from the root with `npm install`. You can run the full stack in development mode with `npm run dev`, which starts the backend and dashboard in parallel with hot reload.

When making changes, run `npm test` to execute the test suite before submitting a pull request. Please keep pull requests focused on a single concern and include a clear description of what the change does and why.

---

## License

This project is licensed under the [MIT License](LICENSE).