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Anaconda

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Evals for Anaconda

Eval coverage for Anaconda, mapped from its public product surface.

About Anaconda

Anaconda is a platform for building AI and data science work on trusted open-source Python, spanning package management, governed environments, and production AI workflows. Anaconda Core handles Python package management with validated packages, security scanning, and dependency resolution, while AI Orchestration (formerly Outerbounds) runs and scales production AI workflows. Client-side products include Anaconda Desktop (open beta) for local models, environments, and endpoints, Anaconda Notebooks in the cloud, Navigator, and a Toolbox for Python in Microsoft Excel, sold across Free, Starter ($15/user/month), and Business ($50/user/month) plans.

Industry

Python and open-source AI development platform (package management, environments, and AI orchestration)

Website

www.anaconda.com

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Coverage map

What would you measure for Anaconda?

6 scoring areas · 24 capabilities mapped · grounded in 8 cited pages

Every eval set is graded on

  • Adversarial robustness
  • Workflow quality
  • Safety gates
  • Operator quality

Pass/Fail + LLM judge 1–5 · critical severity flags · negative controls

01

Anaconda Core: packages, environments, and dependency resolution

Trusted Python package management: validated packages, conda environment creation and reproducibility, and intelligent conflict resolution across the documented install and configuration paths.

“Anaconda gives you trusted open-source packages, governed environments, and production-grade orchestration.” www.anaconda.com

Mapped capabilities

4 capabilities

  • Package search and install from validated channels

    Finding and installing packages from Anaconda.org or a local Anaconda Repository, including channel selection.

  • Dependency conflict resolution

    Explaining and resolving version conflicts during environment solves rather than failing opaquely.

  • Environment create, clone, and manage

    Creating, cloning, and updating conda environments across Windows, macOS, and Linux.

  • Reproducibility via import/export configs

    Exporting and re-importing environment configurations so a project rebuilds identically on another machine.

Illustrative example

Input
My conda environment works on my Mac but my teammate on Windows can't recreate it. How do I hand it over reliably?
Expected behavior
Recommends exporting the environment configuration and having the teammate import it, noting Anaconda supports import/export of configs for cross-machine reproducibility and that environments can be managed from Navigator or Desktop as well as the CLI.

02

AI Orchestration: production AI workflows

Building, running, and scaling production AI workflows (formerly Outerbounds) with traceability, observability, and consistent performance from first experiment to a live endpoint.

Mapped capabilities

4 capabilities

  • Workflow build and run

    Authoring and executing an AI workflow from an initial experiment through repeated runs.

  • Scaling to production endpoints

    Promoting a workflow to a live, served endpoint with consistent performance.

  • Traceability of runs

    Tracking what ran, with which inputs and environment, across the experiment-to-production path.

  • Built-in observability

    Surfacing run health and performance signals for deployed workflows.

03

Anaconda Desktop: local models, endpoints, and GUI environments

The open-beta local interface unifying AI model discovery, local inference, endpoint serving, environment management, and application launching on one machine.

“Spin up API endpoints from any downloaded model with one click.” www.anaconda.com

Mapped capabilities

4 capabilities

  • Model catalog discovery and filtering

    Browsing pre-vetted models and filtering by task, hardware compatibility, quantization, and RAM requirements.

  • Local inference and one-click API endpoints

    Chatting with a downloaded model or serving it as a local API endpoint without data leaving the machine.

  • Resource monitoring and server logs

    Viewing CPU/RAM utilization and server logs for locally served models.

  • Application launch and management

    Installing, updating, and launching JupyterLab, Spyder, and other apps alongside environments.

04

Cloud workspaces: Notebooks, sharing, and data connectors

Browser-based JupyterLab notebooks with cloud storage, collaboration, Panel app publishing, AI Assistant, and Data Connectors that move CSVs between Cloud, Excel, and notebooks.

“without cloud API costs, and without data leaving your machine” www.anaconda.com

Mapped capabilities

4 capabilities

  • Zero-setup cloud notebook start

    Spinning up a notebook with packages and compute available without local installation.

  • Sharing via URL or deployed Panel app

    Publishing work as a click-through link or a deployed interactive Panel application.

  • Data Connectors across Excel and notebooks

    Saving a .csv to Anaconda Cloud and accessing it from either Excel or a notebook.

  • AI Assistant for Python

    Analyzing tables and recommending ways to work with the data, including generated code help.

05

Toolbox for Python in Microsoft Excel

Bringing Python into the spreadsheet for analysts: low-code assisted code generation, visualization building, code snippets shared with Notebooks, and local execution via Anaconda Code.

“Write Python code and run it locally, directly within Excel.” www.anaconda.com

Mapped capabilities

4 capabilities

  • Visualization Builder

    Turning tables into advanced visualizations through the guided interface.

  • Low-code AI assistance in Excel

    Generating and explaining Python for spreadsheet tasks for users without Python knowledge.

  • Code snippets portability

    Carrying code between Excel workbooks and Anaconda Notebooks.

  • Local execution with Anaconda Code

    Running Python locally within Excel with control over the environment and packages, keeping code and data in the workbook.

06

Governance, security, and plan entitlements

Business-tier governance and the licensing model: automated vulnerability scanning, audit trails and project governance controls, centralized administration, and the Free/Starter/Business boundaries including the 200+ employee licensing rule.

“Users within organizations with 200+ employees/contractors (including Affiliates) require a paid Business license.” www.anaconda.com

Mapped capabilities

4 capabilities

  • Automated vulnerability scanning

    Surfacing security findings on packages for Business-plan organizations.

  • Audit trails and project governance controls

    Compliance-oriented tracking of project activity and governed environments.

  • Plan boundaries and entitlements

    Correctly distinguishing Free ($0), Starter ($15/user/month), and Business ($50/user/month) features including storage limits.

  • Licensing eligibility rules

    Applying the 200+ employee/contractor paid-license requirement and academic/non-profit exemption paths.

Illustrative example

Input
I'm a data scientist at a 4,000-person insurance company. Can my team just use the Free plan of Anaconda?
Expected behavior
States that organizations with 200+ employees or contractors, including affiliates, require a paid Business license, so the Free plan is not permitted here. Points to the Terms of Service or pricing FAQ rather than inventing a price exception.

Coverage is mapped from Anaconda's public pages (8 crawled). Examples are illustrative, not real test cases. The runnable eval library — graded inputs, expected behavior, and pass/fail checks — is built when you request it above.

Frequently asked questions

What do the Corsac evals for Anaconda test?+

The coverage map is generated from Anaconda's own public product surface (Python and open-source AI development platform (package management, environments, and AI orchestration)): 6 scoring areas — Anaconda Core: packages, environments, and dependency resolution, AI Orchestration: production AI workflows, and Anaconda Desktop: local models, endpoints, and GUI environments, and more — spanning 24 mapped capabilities, each graded on adversarial robustness, workflow quality, safety gates, and operator quality once the library is built.

How are the Anaconda evals scored?+

Every case generated for Anaconda — across Anaconda Core: packages, environments, and dependency resolution and AI Orchestration: production AI workflows and the other mapped areas — is graded with pass/fail checks plus an LLM judge scoring 1–5 against its expected behavior, with critical-severity flags and negative controls. Only judge-passed evals are published.

How many test cases does the Anaconda library include?+

The full Anaconda library is built on request. The coverage map spans 6 areas and 24 capabilities (for example, Package search and install from validated channels and Dependency conflict resolution under Anaconda Core: packages, environments, and dependency resolution); each becomes graded test cases — inputs, expected behavior, pass/fail checks — in your Corsac workspace.

How do I run these evals against Anaconda or my own agent?+

Request the library with your work email above. We'll build out all 6 mapped Anaconda areas and set them up in a Corsac workspace, where you can run every test case against Anaconda or your own agent with your own data.