urika 0.2.0

Multi-agent scientific analysis platform

Getting Started · Agent System · Models & Privacy · Notifications · CLI Reference · Interactive TUI · Dashboard

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Early Development — Urika is under active development. Expect frequent updates, bug fixes, and new features. Check back regularly or run urika setup to see if a new version is available. Bug reports and feedback welcome at GitHub Issues.

Urika uses multiple AI agents to autonomously explore analytical approaches for your dataset and research question. It creates experiments, tries different methods, evaluates results, searches relevant literature, and builds custom tools when needed. Everything is documented automatically — experiment labbooks, project-level reports, key findings, and slide presentations you can view in any browser. Each experiment's methods, metrics, and observations are tracked in structured records that agents use to plan the next step.

Currently supports the Claude Agent SDK (Anthropic), including local models via Ollama. Adapters for OpenAI Agents SDK, Google Agent Development Kit (ADK), and PI are planned for upcoming releases.

Runs on Linux, macOS, and Windows 11. For local/private model setups (Ollama, vLLM, LiteLLM), see Models & Privacy.

Installation

Prerequisites

1. Python >= 3.11
2. Claude Pro or Max account (recommended) — or an Anthropic API key
3. Claude Code CLI — install and log in first:

npm install -g @anthropic-ai/claude-code
claude login                    # opens browser to authenticate

Install Urika

Recommended: install from source. Urika is under active development with frequent updates. Installing from source gives you the latest features and fixes:

git clone https://github.com/xkiwilabs/Urika.git
cd Urika
pip install -e ".[dev]"
urika setup                     # check installation, detect hardware, optionally install DL

To update, just git pull from the repo.

Alternative: install from PyPI (pre-release — may lag behind source):

pip install urika

The default install includes the Textual TUI, visualization, ML, statistics, knowledge pipeline, and notification support. Deep learning (torch, transformers) is optional: pip install "urika[dl]".

See Getting Started for full details.

Quickstart

urika new my-study --data ./my_data.csv    # create a project (interactive)
urika run my-study --dry-run                # preview the planned pipeline first
urika run my-study                          # run experiments
urika finalize my-study                     # produce final report
urika                                       # launch the interactive TUI
urika --classic                             # or use the classic REPL

See the Getting Started guide for a full walkthrough. Agent-generated code runs as you — see Security Model before running unfamiliar projects.

How It Works

flowchart TD
    A["urika new\nProject Builder"] --> B["Scans data, profiles,\ningests knowledge"]
    B --> C{"How to run?"}

    C -- "Single experiment\n(guided)" --> D["urika run"]
    C -- "Multiple experiments\n(autonomous)" --> META["urika run --max-experiments N\nAutonomous Mode"]

    D --> LOOP
    META --> LOOP

    subgraph LOOP ["Experiment Loop (per experiment)"]
        direction TB
        P["Planning Agent\ndesigns method"] --> TA["Task Agent\nwrites code, runs tools"]
        TA --> EV["Evaluator\nscores against criteria"]
        EV --> Q{Criteria met?}
        Q -- No --> ADV["Advisor Agent\nanalyzes, proposes next"]
        ADV --> P
        Q -- "Yes\n(--review-criteria)" --> RC["Advisor reviews\ncriteria"]
        RC -- "raises bar" --> P
        RC -- "confirms" --> REPORT
        Q -- Yes --> REPORT["Generate Reports"]
    end

    D -- "after experiment" --> REVIEW["User reviews results\ndecides next step"]
    REVIEW -- "run again" --> D

    META -- "advisor decides\nnext experiment" --> LOOP

    REPORT --> FIN["urika finalize\nFinalizer Agent"]
    FIN --> OUT["Standalone methods\nFinal report & presentation\nReproduce scripts"]

    TA -. "needs tool" .-> TB["Tool Builder"]
    P -. "needs literature" .-> LIT["Literature Agent"]
    TB -.-> TA
    LIT -.-> P

Eleven agents work together. Each experiment runs autonomously — agents plan, execute, evaluate, and iterate without intervention. You choose how to manage the between-experiment flow:

• Guided (urika run) — agents run one experiment autonomously, then you review results and decide what to try next. Best for exploratory work and complex domains where human judgment matters between experiments.
• Fully autonomous (urika run --max-experiments N) — the system runs multiple experiments back-to-back, with the advisor agent deciding what to try next. Best when you've provided detailed context (see Prompts and Context).

Within each experiment, the orchestrator cycles through planning -> task -> evaluator -> advisor each turn. When all experiments are complete, the Finalizer produces standalone deliverables.

See Agent System for details on each agent role.

Scriptable CLI

Every Urika command is fully scriptable -- pass arguments and flags directly, get structured JSON output with --json, and chain commands in shell scripts. No interactive prompts when flags are provided.

# Create and run a project in one script
urika new my-study --data ~/data/scores.csv --question "What predicts outcome?" --mode exploratory
urika run my-study --max-turns 5 --instructions "focus on tree-based models"
urika run my-study --max-experiments 3 --auto
urika finalize my-study --instructions "emphasize the best model"

# Get structured output for custom tooling
urika status my-study --json
urika results my-study --json
urika methods my-study --json

# Remote control via Telegram/Slack while experiments run
# See Notifications docs for setup

This makes it straightforward to build custom workflows, batch processing scripts, CI pipelines, or wrap Urika in your own research tools. See CLI Reference for the full command list.

Privacy and Model Configuration

Each project can configure which models and endpoints its agents use. Three privacy modes:

• Open (default) -- all agents use cloud models via API. No restrictions.
• Private -- all agents use private endpoints only. This can be local models (Ollama), a secure institutional server, or any combination -- whatever stays within your data governance boundary.
• Hybrid -- a private Data Agent reads raw data and outputs sanitized summaries; all other agents run on cloud models for maximum analytical power. Raw data never leaves your private environment. The default hybrid split covers most cases, but you can customize which agents use which endpoints to ensure what needs to be private stays private.

Per-agent model routing lets you optimize for cost (Haiku for simple tasks, Opus for complex reasoning) or compliance (institutional servers for data access, cloud for method design). Different projects can have completely different privacy and model settings.

See above for supported and upcoming SDK adapters.

See Models and Privacy for configuration details.

Documentation

Guide	Description
Getting Started	Installation, requirements, first project
Core Concepts	Projects, experiments, runs, methods, tools, agents
Creating Projects	urika new, data scanning, knowledge ingestion
Prompts and Context	Writing effective descriptions, instructions, knowledge ingestion
Running Experiments	Orchestrator loop, turns, auto mode, resume
Advisor Chat and Instructions	Standalone advisor conversations, steering agents, suggestion-to-run flow
Viewing Results	Reports, presentations, methods, leaderboard
Finalizing Projects	Finalization sequence, standalone methods, reproducibility
Agent System	All 11 agent roles and how they interact
Built-in Tools	18 analysis tools agents use
Knowledge Pipeline	Ingesting papers, PDFs, searching
Models and Privacy	Per-agent model routing, endpoints, hybrid privacy mode
Configuration	urika.toml, criteria, preferences
Project Structure	File layout and what each file does
CLI Reference	Every command with full options
Interactive TUI	TUI interface, slash commands, tab completion, orchestrator chat
Notifications	Email, Slack, Telegram alerts and remote commands
Security Model	Agent-generated code, permission boundaries, secrets, dashboard auth

Citation

If you use Urika in your research or analysis, please acknowledge its use in your publications:

Urika -- Multi-agent scientific analysis platform. Developed by Michael J. Richardson and colleagues at Macquarie University, Sydney, Australia. https://github.com/xkiwilabs/Urika

License

Apache 2.0 -- Free to use, modify, and distribute for any purpose, including commercial use. Includes patent protection for contributors. See the full license for details.