Databricks Genie CodeDatabricks
|
Muse CodeMeta
|
|||||
Related Products
|
||||||
About
Genie Code is an AI agent built for data teams that analyzes, builds, and maintains complex data workflows inside the Databricks workspace. It autonomously plans and executes multistep tasks while adapting to an organization’s data and governance model, with specialized capabilities across data engineering, data science, machine learning, and business intelligence. Grounded in Unity Catalog metadata, semantics, and governance, it can identify authoritative tables, metrics, and assets, understand dependencies across data and AI systems, and respect existing access controls. For data science, Genie Code can find and clean data, explore datasets, test hypotheses, and generate shareable reports. Machine learning workflows include feature engineering, model training and evaluation, deployment, endpoint configuration, and performance tuning. Data engineers can use natural language to automate ETL workloads, optimize queries, and build Spark Declarative Pipelines.
|
About
Muse Code is Meta’s terminal coding agent, powered by Muse Spark 1.2, for handling complex software engineering tasks across large repositories. The agent can plan changes, write code, validate results, and coordinate multiple persistent subagents during development sessions. Muse Code uses async background agents that stay active throughout a session to reduce repeated information gathering and help complete multi-step tasks with less steering. Its runtime uses a local event log that records model calls, tool runs, approvals, and edits so sessions can be replayed and resumed after failures. Muse Code includes bundled skills such as /plan for approval-gated planning, /grill for stress-testing plans, and /goal for working toward completion. Built for AI developers and software teams, Muse Code helps automate coding workflows, long-running engineering tasks, debugging, and repository-level development.
|
|||||
Platforms Supported
Windows
Not Supported
Mac
Not Supported
Linux
Not Supported
Cloud
Supported
On-Premises
Not Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
|
Platforms Supported
Windows
Supported
Mac
Supported
Linux
Supported
Cloud
Not Supported
On-Premises
Not Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
|
|||||
Audience
Data engineers, data scientists, ML engineers, and analytics teams seeking to automate complex data, machine learning, pipeline, and BI workflows
|
Audience
Software engineers, AI developers, coding agent users, platform teams, DevOps teams, ML engineers, research teams, enterprise development teams, and organizations that need terminal coding agents, repository automation, code generation, debugging, validation, persistent subagents, replay-safe execution, long-running coding workflows, and end-to-end software development support
|
|||||
Support
Phone Support
Supported
24/7 Live Support
Not Supported
Online
Supported
|
Support
Phone Support
Not Supported
24/7 Live Support
Not Supported
Online
Supported
|
|||||
API
Offers API
Not Supported
|
API
Offers API
Supported
|
|||||
Screenshots and Videos |
Screenshots and Videos |
|||||
Pricing
No information available.
Free Version
Not Supported
Free Trial
Supported
|
Pricing
$1.25 per 1M tokens (input)
Standard pricing: $1.25 per million input tokens and $4.25 per million output tokens
Discounted "contributor" tier costing $0.10 per million input tokens and $0.20 per million output tokens for users who agree to share feedback to improve the AI.
Free Version
Supported
Free Trial
Not Supported
|
|||||
Reviews/
|
Reviews/
|
|||||
Pros & Cons from Real UsersPros
Cons
|
||||||
Training
Documentation
Supported
Webinars
Supported
Live Online
Supported
In Person
Not Supported
|
Training
Documentation
Supported
Webinars
Not Supported
Live Online
Not Supported
In Person
Not Supported
|
|||||
Company InformationDatabricks
Founded: 2013
United States
www.databricks.com/product/genie/code
|
Company InformationMeta
Founded: 2004
United States
meta.ai
|
|||||
Alternatives |
Alternatives |
|||||
|
|
|
|||||
|
|
|
|||||
|
|
||||||
|
|
||||||
Categories |
Categories |
|||||
Integrations
Databricks
Supported
Meta AI
Not Supported
Muse Glimmer
Not Supported
Muse Spark
Not Supported
Muse Spark 1.1
Not Supported
Muse Spark 1.2
Not Supported
Muse Spark 1.3
Not Supported
SQL
Supported
|
Integrations
Databricks
Not Supported
Meta AI
Supported
Muse Glimmer
Supported
Muse Spark
Supported
Muse Spark 1.1
Supported
Muse Spark 1.2
Supported
Muse Spark 1.3
Supported
SQL
Not Supported
|
|||||
|
|
|