README.md

September 3, 2026 · View on GitHub

Theoria

Theoria

English | 简体中文

Put every AI agent on the same starting line

A local-first desktop workspace for parallel AI coding agents and reproducible evaluations

Privacy first. All data stays local, with application records stored in SQLite.

Tauri 2 React 19 TypeScript 7.0 Rust Stable Desktop

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Parallel agent runs in Theoria
Run multiple agents on the same task and follow their status, responses, and tool calls in real time
Agent run board in Theoria
Monitor every Agent task at a glance with searchable lifecycle columns
Agent result summary in Theoria
Compare completion, latency, token usage, tool calls, and file changes side by side
Skill management in Theoria
Manage skills centrally and mount them into workspaces
Benchmark management in Theoria
Organize reproducible benchmark comparisons

About Theoria

Theoria is a desktop workspace for AI coding agents. It gives every selected agent the same workspace snapshot and an isolated execution directory, allowing Codex, Claude Code, OpenCode, and WorkBuddy to solve one task in parallel while their progress, results, and file changes remain easy to compare.

It supports everyday multi-agent development workflows and provides consistent environments, run history, and skill configuration for repeatable capability evaluations.

Warning

This project is not yet complete and remains under active development.

Highlights

CapabilityDescription
Parallel agentsRun up to six agents on one task, including Codex, Claude Code, OpenCode, and WorkBuddy
Isolated executionStart from an immutable workspace snapshot and give each agent its own working directory
Observable progressFollow status, streaming output, tool calls, token usage, and duration; stop one agent or all of them
Continued collaborationPreserve agent sessions and send follow-up prompts to every agent or a selected subset
Result comparisonCollect final responses and file changes, then persist run history for later review
Skill managementManage skills from local or Git sources and mount them into one or more workspaces
Benchmark workflowsOrganize reproducible comparisons around fixed snapshots, test cases, and selected agents
Local-first dataKeep workspaces on the local file system and persist application records in SQLite
InternationalizationUse the built-in Simplified Chinese and English interfaces with localized frontend and backend errors