Document ajet-swarm top as alias for ajet-swarm overwatch

Add mentions of the `ajet-swarm top` alias across documentation to help
users discover this shorter alternative command.

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

Author: binary-husky

README.md

@@ -47,7 +47,7 @@ Let's begin with the simplest example: a math agent with a tool call. This is a
 
 Let's begin with the simplest AgentJet Swarm example: also a math agent. In this case, you can use any GPU-less laptop to train the model remotely.
 
-1. Start swarm server and begin swarm overwatch: `ajet-swarm start` and `ajet-swarm overwatch`. (Alternative: if you are a fan of docker, use our [prebuilt docker image here](docs/en/ajet-swarm-docker.md) without setting up dependencies)
+1. Start swarm server and begin swarm overwatch: `ajet-swarm start` and `ajet-swarm overwatch` (or `ajet-swarm top`). (Alternative: if you are a fan of docker, use our [prebuilt docker image here](docs/en/ajet-swarm-docker.md) without setting up dependencies)
 2. From your laptop (or swarm server localhost), run [this simple script](https://github.com/modelscope/AgentJet/blob/main/tutorial/example_math_swarm/math.py) to begin training:
     ```python
     AJET_SWARM_URL="http://swarm-server-ip:10086" python ./tutorial/example_math_swarm/math.py

docs/en/ajet-swarm-docker.md

@@ -70,7 +70,7 @@ The command launches two background processes with `&`:
 
 | Process | What it does |
 |---|---|
-| `ajet-swarm overwatch` | Starts the **real-time TUI monitor** in the foreground. Displays the current server state (OFFLINE / BOOTING / ROLLING / WEIGHT_SYNCING), active episodes, and rollout statistics. |
+| `ajet-swarm overwatch` | Starts the **real-time TUI monitor** in the foreground. Displays the current server state (OFFLINE / BOOTING / ROLLING / WEIGHT_SYNCING), active episodes, and rollout statistics. Tip: `ajet-swarm top` is an alias. |
 | `ajet-swarm start` | Starts the **Swarm Server** itself — initializes VERL training loop, vLLM inference engine, and the FastAPI HTTP server on port `10086`. |
 | `NO_COLOR=1 LOGURU_COLORIZE=NO` | Disables ANSI color codes in the server log so the log file `swarm_server.log` is readable as plain text. |
 | `&>/workspace/log/swarm_server.log` | Redirects both stdout and stderr of the server process to the log file (which is persisted to your host machine via the volume mount). |

docs/en/swarm.md

@@ -38,6 +38,7 @@ Notes:
     ```bash
     ajet-swarm overwatch --swarm-url=http://localhost:10086
     ```
+    Tip: `ajet-swarm top` is an alias for `ajet-swarm overwatch`.
 
 3. changing customized port (default port is 10086):
     ```bash

docs/en/swarm_best_practice.md

@@ -15,6 +15,7 @@ everything need to begin from bring the swarm server alive.
 We recommend launching server together with a swarm monitor:
 ```bash
 # this command start swarm server and a TUI to monitor the swarm server
+# tip: ajet-swarm top is an alias for ajet-swarm overwatch
 (ajet-swarm start &> ajet-swarm-server.log) & (ajet-swarm overwatch)
 ```
 

docs/en/swarm_deepdive.md

@@ -117,7 +117,7 @@ Practical tips:
 
 - **Treat YAML as the source of truth**: you can inspect it with `yaml_job.dump_job_as_yaml("./config.yaml")` and load overrides via `yaml_job.build_job_from_yaml("./config.yaml")`.
 - **Idempotency**: `auto_sync_train_config_and_start_engine()` is designed to be safe if the engine is already **ROLLING** (it will do nothing) and will wait if the engine is **BOOTING / WEIGHT_SYNCING**.
-- **Monitoring**: run `ajet-swarm overwatch --swarm-url=http://your-swarm-server:10086` (or `python -m ajet.launcher --swarm-overwatch=...`) to watch the server states and rollout pool.
+- **Monitoring**: run `ajet-swarm overwatch --swarm-url=http://your-swarm-server:10086` (or `ajet-swarm top`, which is an alias) to watch the server states and rollout pool.
 - **Full AgentJetJob arguments**: run `help(AgentJetJob)` or check `ajet/copilot/job.py` for all available parameters including `max_prompt_length`, `max_response_length`, `max_model_len`, `mini_batch_num`, `lora_rank`, `lora_alpha`, `lora_target_modules`, and more.
 
 ### Episode Running

docs/en/swarm_workflow.md

@@ -76,7 +76,7 @@ A swarm workflow has three phases per training step:
 ```bash
 # Start server + TUI monitor (in two terminals, or combined):
 ajet-swarm start &
-ajet-swarm overwatch
+ajet-swarm overwatch  # or: ajet-swarm top
 ```
 
 The server starts with `ajet_swarm_default.yaml` (see [Swarm Mode Config Chain](../configuration/#swarm-mode-config-chain)). No `--conf` is needed for the default setup.

docs/en/tune_your_first_agent.md

@@ -463,6 +463,7 @@ On your GPU machine (or cluster), start the Swarm Server:
 ajet-swarm start
 
 # Open the monitoring dashboard in another terminal
+# tip: ajet-swarm top is an alias for ajet-swarm overwatch
 ajet-swarm overwatch --swarm-url=http://localhost:10086
 ```
 

docs/index.md

@@ -33,7 +33,7 @@ Let's begin with the simplest example: a math agent with a tool call. This is a
 
 Let's begin with the simplest AgentJet Swarm example: also a math agent. In this case, you can use any GPU-less laptop to train the model remotely.
 
-1. Start swarm server and begin swarm overwatch: `ajet-swarm start` and `ajet-swarm overwatch`. (Alternative: if you are a fan of docker, use our [prebuilt docker image here](en/ajet-swarm-docker.md) without setting up dependencies)
+1. Start swarm server and begin swarm overwatch: `ajet-swarm start` and `ajet-swarm overwatch` (or `ajet-swarm top`). (Alternative: if you are a fan of docker, use our [prebuilt docker image here](en/ajet-swarm-docker.md) without setting up dependencies)
 2. From your laptop (or swarm server localhost), run [this simple script](https://github.com/modelscope/AgentJet/blob/main/tutorial/example_math_swarm/math.py) to begin training:
     ```python
     AJET_SWARM_URL="http://swarm-server-ip:10086" python ./tutorial/example_math_swarm/math.py