LLM Goose 🪿

LLM-powered testing, traces, and live chat for agent workflows

Goose is a Python library, CLI, and web dashboard for testing and debugging LLM agents.
Scaffold a gooseapp/, point Goose at your real query function, write goose.case(...) tests, then expose tools and live chat when you want the full dashboard loop.

Visit the landing page

What it looks like

Goose dashboard overview

Goose testing detail

Why Goose?

Natural-language expectations – Describe the behavior you want and let Goose validate it.
Tool call assertions – Check what your agent actually did, not just what it said.
Full execution traces – Inspect messages, tool calls, tool outputs, and validation results.
Live chat for iteration – Try agents in the dashboard while you develop.
Hot reload – Re-run against updated code without restarting the app.

Choose your path

Framework-agnostic quickstart – integrate Goose into an existing Python app, regardless of framework: docs/getting-started.md
LangChain / LangGraph integration – keep your existing LangChain-style agent and add Goose around it: docs/integrations/langchain.md

If you are starting from zero, the framework-agnostic quickstart is the default path.

Core workflow

Scaffold the app with goose init
Point Goose at query(...) -> AgentResponse in gooseapp/conftest.py
Write cases in gooseapp/tests/ with goose.case(...)
Run the first loop with goose test list and goose test run
Expand into tools, chat, and hot reload through gooseapp/app.py

Install

Required for the first test run:

pip install llm-goose

Optional for the browser UI:

npm install -g @llm-goose/dashboard-cli

`goose init` creates the path

goose init

gooseapp/
├── README.md
├── __init__.py
├── app.py
├── conftest.py
└── tests/
    ├── __init__.py
    └── test_example.py

app.py configures tools, live chat agents, and hot reload
conftest.py wires the Goose fixture to your real query function
tests/ holds the cases you run from the CLI or dashboard

See docs/goose-init.md for the full scaffold contract.

Minimal first test

from goose.testing import Goose


def test_agent_responds(goose: Goose) -> None:
    goose.case(
        query="Hello, what can you help me with?",
        expectations=[
            "Agent responds with a greeting or acknowledgment",
            "Agent describes its capabilities or offers assistance",
        ],
    )

goose is injected from the fixture you register in gooseapp/conftest.py. The full query -> fixture -> test path is documented in docs/getting-started.md.

Key commands

goose init                  # scaffold gooseapp/
goose test list gooseapp.tests
goose test run gooseapp.tests
goose api
goose-dashboard

Where to go next

Framework-agnostic quickstart: docs/getting-started.md
LangChain / LangGraph integration: docs/integrations/langchain.md
Scaffold details: docs/goose-init.md
Writing tests: docs/testing.md
Running the API and CLI loop: docs/running-goose.md
Using the dashboard: docs/dashboard.md

License

MIT License – see LICENSE for full text.

Metadata

Release files for llm-goose 0.2.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for llm-goose 0.2.0
File	Size	Uploaded
llm_goose-0.2.0.tar.gz	57.3 kB	Apr 12, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for llm-goose 0.2.0
File	Interpreter	ABI	Platform	Reset
llm_goose-0.2.0-py3-none-any.whl 74.0 kB Apr 12, 2026	Python 3	none	any	Details

Total release size: 131.3 kB

Release files / llm_goose-0.2.0.tar.gz

Download URL	llm_goose-0.2.0.tar.gz
Size	57.3 kB
Tags	Source
SHA-256 checksum How to use checksums	`1322eeaf1f8ac9ec3a72d11707229cb8969ac261313d8d8b9fc41d3a367ad813`
BLAKE2b-256 checksum How to use checksums	`101eef07a52d34e25a6da090d6e0e28ea7be4bce325b694f1ea8929fbb640f43`
Upload date	Apr 12, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.2.0 CPython/3.13.6`

Release files / llm_goose-0.2.0-py3-none-any.whl

Download URL	llm_goose-0.2.0-py3-none-any.whl
Size	74.0 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`7e63057df2420d1df3d3e5d72a5146fe3dbf87b022969cd75762cfbef96ca48d`
BLAKE2b-256 checksum How to use checksums	`4ed3796f6db2b368302de74ee47e90bcb8cc776f3c4d9758061916e44ca2fbe5`
Upload date	Apr 12, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.2.0 CPython/3.13.6`