bat-cli 2026.6.25a1

CLI tool to interact with BAT agents

bat-cli

A CLI tool for creating, building, and evaluating BAT agent projects.

Prerequisites

• Python 3.12+ and uv installed
• Docker installed (required for bat build and bat push)
• For evaluation commands: an existing BAT agent root containing agent.json, config.yaml, and pyproject.toml

---

Installation

Option A — install system-wide with uv tool (recommended)

Installs bat into an isolated environment and puts the executable on your PATH, so it is available from any directory.

# from PyPI
uv tool install bat-cli


Make sure the uv tools bin directory is on your `PATH` (uv prints the path on first
install; this is usually `~/.local/bin`):

```bash
uv tool update-shell      # adds the uv tools dir to your shell profile

Then verify:

bat --help

To upgrade or remove later:

uv tool upgrade bat-cli
uv tool uninstall bat-cli

Option B — install into a virtual environment with uv pip

Use this when you want bat scoped to a specific project/venv rather than installed globally.

uv venv                      # create .venv (skip if you already have one)
source .venv/bin/activate    # .venv\Scripts\activate on Windows

# from PyPI
uv pip install bat-cli

# or from a local checkout (run from the cli/ directory)
uv pip install .             # add -e for an editable/development install

bat is available whenever that virtual environment is active:

bat --help

Option C — run without installing (development)

From the cli/ directory:

uv sync --group dev
uv run bat --help

All examples below show bat ...; replace with uv run bat ... when using this option.

---

Command Tree

bat
├── init
│   └── agent
│       ├── <name>
│       ├── --clients, -c
│       ├── --output-dir, -o
│       ├── --force, -f
│       ├── --port
│       ├── --model
│       └── --model-provider
├── add
│   └── client
│       ├── <clients>
│       └── --force, -f
├── set
│   └── env
│       ├── --port
│       ├── --model
│       ├── --model-provider
│       ├── --docker-registry
│       └── --repo
├── eval
│   ├── init
│   │   └── --force, -f
│   ├── run
│   ├── show
│   └── plot
│       ├── --folder, -f
│       └── --filter, -F
├── build
│   ├── --context, -C
│   ├── --docker-registry
│   ├── --repo
│   ├── --version
│   └── --no-cache
├── push
│   ├── --context, -C
│   ├── --docker-registry
│   ├── --repo
│   └── --version
└── version

Built-in help is available at every level:

bat --help
bat init agent --help
bat eval --help
bat build --help

---

Workflows

1. Create a new agent

bat init agent my_agent

# specific output directory
bat init agent my_agent --output-dir .

# pre-generate LLM clients
bat init agent my_agent --clients reformulator,planner,executor

# set the port/model/provider written to config.yaml
bat init agent my_agent --port 9900 --model gpt-4o-mini --model-provider openai

2. Add clients to an existing agent

Run from the agent root (must contain src/llm_clients/):

bat add client planner,executor

# overwrite existing files
bat add client planner,executor --force

3. Update agent settings

Run from the agent root (must contain config.yaml). The runtime values (--port, --model, --model-provider) are written into config.yaml (endpoint.port, model.name, model.provider); the Docker defaults (--docker-registry, --repo) are written into .env:

# endpoint.port / model.name / model.provider in config.yaml
bat set env --port 8080 --model gpt-4o-mini --model-provider openai

# Docker defaults for build/push, written to .env
bat set env --docker-registry hub.bubbleran.com --repo orama/labs/my-agent

4. Build and push a Docker image

# --version is used both as the image tag and as the VERSION build arg (default: latest)
bat build --context ./my_agent --docker-registry hub.bubbleran.com --repo orama/labs/my-agent --version latest

# no-cache build with a specific version
bat build --context ./my_agent --repo orama/labs/my-agent --version 1.0.0 --no-cache

bat push --context ./my_agent --docker-registry hub.bubbleran.com --repo orama/labs/my-agent --version latest

The image reference is always {registry}/{repo}:{version}.

If BAT_DOCKER_REGISTRY and BAT_DOCKER_REPO are already set in .env or the shell, --docker-registry and --repo can be omitted.

Precedence (both --docker-registry / --repo):

1. CLI flag
2. Shell environment variable (BAT_DOCKER_REGISTRY / BAT_DOCKER_REPO)
3. .env file in the current directory
4. Hardcoded default (default_registry / default-repository/<project-name>)

5. Run evaluation

Run all eval commands from an existing agent root (must contain agent.json, config.yaml, and pyproject.toml):

# scaffold evaluation files
bat eval init

# inspect the resolved configuration
bat eval show

# run evaluation
bat eval run

eval init creates:

• eval/eval.yaml
• eval/input/tasks.json
• eval/output/

Minimal eval/eval.yaml:

evaluation:
  dataset: eval/input/tasks.json # default path if omitted
  output_dir: eval/output # default path if omitted
  agent_startup_timeout_s: 45
  agent_shutdown_timeout_s: 10
  k: 1
  qualitative: false # set true to enable LLM judge scoring

models:
  - provider: openai
    model: your-model-name
  - provider: ollama
    model: your-local-model
    base_url: http://localhost:11434

# required only when qualitative: true
judge:
  provider: ollama
  model: local-judge-model
  base_url: http://localhost:11434
  # api_key_env: BAT_JUDGE_API_KEY      # env var name holding the judge's API key

Notes:

• bat eval run starts the agent via uv run . from the agent root and waits until the agent's config.yaml endpoint (endpoint.url:port) accepts a TCP connection, so the agent project must have its dependencies installed (its own .venv). The eval reads that endpoint from the agent's config.yaml; it is no longer configured in eval.yaml.
• models entries may also be written as "<provider>:<model>" strings.
• For models that require an API key, set it in the agent's .env under <PROVIDER>_API_KEY (e.g. OPENAI_API_KEY).

6. Plot evaluation metrics

bat eval plot reads the metrics.json files produced by eval run and renders charts. Point --folder at an evaluation output directory; each sub-folder containing a metrics.json is treated as one run.

# plot every run found under the output folder
bat eval plot --folder eval/output

# restrict the per-task charts to task ids containing a substring
bat eval plot --folder eval/output --filter smoke

Charts are saved back into the given folder. --filter only narrows the per-task charts; summary charts always cover all runs.