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evoflux

Evoflux is an open-source, local-first workspace where AI agents build software, conduct deep research, automate browser tasks, and collaborate in parallel. Connect any model, keep control of your workspace and data, and take complex work from idea to completion—all in one place.

Stars
4
Language
Python
Created
Aug 3, 2026
Updated
Aug 21, 2026

Introduction

EvoFlux logo

EvoFlux

The local-first desktop workspace for AI agent teams.

Give EvoFlux an outcome. A lead agent plans the work, brings in specialists, uses the right tools, and verifies the result — while you stay in control.

Cowork and software engineering. Any model. Your machine.

License: Apache 2.0 Desktop only Python 3.12+ React 19 Tauri v2 BYOM

Download EvoFlux · Product tour · Quick start · How it works · Architecture · Capabilities


EvoFlux Coding workspace with an agent conversation, source editor, and repository file tree

Plan with the team, inspect the implementation, and navigate the repository without leaving the workspace.

[!NOTE] Since 30 June 2026, fixes, optimizations, and new EvoFlux features have been developed and delivered using EvoFlux Coding mode. The agents build, review, and ship themselves.


Why EvoFlux

Delegate outcomesKeep the whole job togetherChoose every modelOwn the execution
A lead coordinates focused specialists and verifies their handoffs.Chat, files, terminal, browser, memory, git, and previews live in one workspace.Mix providers, models, reasoning levels, skills, and tools per agent.Local runtime, scoped access, outbound redaction, and inspectable history.

Product tour

One app, two specialized modes

One desktop app. One harness. Two different kinds of work.

WorkCoding
Product roleCoworkSoftware engineering workspace
WorkspaceTemporary sandboxPersistent repo or multi-repo project
Best forResearch, documents, data, browser work, quick scriptsBuild, test, refactor, review, git operations
Default specialistsExecutor, Explorer, Consultant, DebateCoder, Explorer, Architect, Debate
VerificationArtifact and tool-result reviewTests, diffs, code context, git

Work is a fast execution sandbox for research, documents, data, browser tasks, files, and quick scripts. Start with a request instead of a repository.

Coding opens one or more real repositories and keeps them available across sessions. Agents can understand the codebase, edit and test code, review diffs, and use the complete git surface.

EvoFlux Work mode ready for a new outcomeEvoFlux workspace with its tool dock open
Start with the outcome
Drop into Work and describe what you want accomplished.
Bring every tool into view
Open terminal, processes, browser, files, side chat, memory, and scheduler beside the conversation.

Assemble the right team and models

Create role-focused agent teams, then tune the model and capabilities of every member independently. EvoFlux ships with 19 provider integrations, including direct APIs, subscription OAuth, cloud platforms, local runtimes, and model routers.

EvoFlux agent team settings with Work and Coding specialistsEvoFlux model provider catalog
Lead-and-specialists
Give each agent one role, one model, and a focused capability set.
Bring your own model
Connect hosted, subscription, routed, cloud, or local providers from one catalog.

Understand the codebase, not just matching text

EvoFlux indexes symbols and relationships across every repository in a Coding project. Explore the graph visually, trace callers and dependencies, and pair structural context with repository-aware language servers.

EvoFlux cross-repository code graph with symbols and relationships

Local control is part of the product

Language servers provide semantic feedback in the active project. Sandbox controls scope filesystem and process access, mask or block sensitive outbound data, and keep execution boundaries visible rather than hidden behind the agent.

EvoFlux language server managementEvoFlux sandbox and outbound data protection controls
Semantic feedback
Detect project languages and reuse managed or system language servers.
Explicit boundaries
Control outbound data, host environment access, shell behavior, and denied paths.

Download

Current stable release: EvoFlux v0.0.8

PlatformPackageSHA-256
macOS · Apple SiliconDownload DMGChecksum
macOS · IntelDownload DMGChecksum
Windows · x64Download installerChecksum
Linux · x64Download DEBChecksum

Install the Linux package with sudo apt install ./EvoFlux_*_amd64.deb. Linux updates use the same package-managed flow instead of replacing dpkg-owned files through the in-app updater.

The desktop packages include the native Python sidecar. The optional WebBridge browser companion is not bundled in EvoFlux release assets; its source and installation instructions live in the separate evo-webbridge repository.

[!NOTE] The v0.0.8 macOS packages use an ad-hoc signature and the Windows installer is not Authenticode-signed, so the operating system may show a trust prompt. Tauri updater archives remain minisign-signed.


Quick Start

Install the desktop app

Choose the package for your platform in Download, install it, and launch EvoFlux. The packaged app includes its Python sidecar.

On macOS and Windows, updater-aware builds check the latest GitHub Release after startup. You can also run a manual signed update check from Settings > About, the application menu, or the tray menu. Install one current package manually; later releases can update in place. Linux updates use a newly downloaded DEB through apt or dpkg.

On first launch:

  1. Connect an LLM provider.
  2. Start a Work session or open a repository for Coding.
  3. Choose the model, reasoning level, skills, tools, and permissions for each agent.

Run the desktop app from source

Requirements: Python 3.12+, uv, Bun, Rust, Tauri CLI, and the Tauri prerequisites for your operating system.

git clone https://github.com/evoelsewhere/evoflux.git
cd evoflux

uv sync
cd web && bun install && cd ..

# Choose browser development — local API + React development server
make dev-web

# Or choose desktop development — local API + React + Tauri desktop shell
make dev-desktop

make dev remains an alias for make dev-web. localhost:5173 is the internal frontend development server used by Tauri during development. EvoFlux is shipped and positioned as a desktop product, not a standalone web app.


Agent working model

EvoFlux operates under a lead-and-specialists model. Each request is analyzed by the Lead Agent to determine scope and complexity.

  • A simple task stays with the Lead.
  • A complex task is broken into well-defined subtasks with explicit goals, outputs, and constraints.
  • Specialists activate on demand, work in parallel, and exchange results through a shared mailbox.
  • The Lead evaluates handoffs and evidence, requests rework when needed, and synthesizes the final response.

EvoFlux Lead plans, dispatches parallel specialists, verifies evidence, and loops back for rework

Configurable per agent

ConfigurationWhy it matters
LLM modelUse a fast model for routine execution and a stronger reasoning model for architecture or review
Thinking levelTune latency and reasoning depth by role and model capability
Skills and toolsAdd agent-specific capabilities or disable code-owned defaults with explicit opt-outs
Permissions and access scopeLimit what an agent can read, write, execute, or approve

The result is higher parallel capacity, less context noise, the right model for each job, verified delivery, and an execution history that can be inspected instead of trusted blindly.

Agent Markdown is the user-owned override surface. Runtime and Settings compile the same effective config from the mode profile plus frontmatter additions and tools_opt_out; reads and validation never materialise configuration files. See documents/architecture/application-harness.md.


Architecture

EvoFlux is desktop-only:

Tauri Desktop → React UI → local FastAPI sidecar → local state / model providers

The production app launches a local sidecar through an ephemeral port and token handshake. The React interface, agent runtime, repository-local code indexes, memory engine, scheduler, permissions, and MCP client all run on the user's machine.

Detailed EvoFlux desktop-only architecture with harness modules, local state, and external integrations

What makes it a harness

A language model generates reasoning. The harness turns that reasoning into controlled action:

EvoFlux harness modules connect model providers to detailed Work and Coding capabilities

LayerResponsibility
1. Tool orchestrationShell, filesystem, git, browser automation, MCP, and agent-to-agent actions
2. GuardrailsPermissions, policies, approvals, filesystem sandboxing, command checks
3. Context and memoryWorkspace state, sessions, code indexes, compaction, knowledge wiki
4. Verification loopsTest, compare, review, debate, reject, rework, and evidence
5. ObservabilityStreaming events, telemetry, logs, metrics, diagnostics, and audit history

The model is replaceable. The harness — context, action, policy, verification, and state — is the product.


Core capabilities

Multi-agent teams

Agents are Markdown files with YAML frontmatter (name, role, model, thinking_level), making teams readable, diffable, and versionable. A team has one Lead and any number of on-demand members. Multiple instances of the same blueprint can work in parallel without becoming always-on background processes.

Durable Goal mode

Start an autonomous objective in any mode with /goal <objective>. Goal state, elapsed time, token usage, and an optional token budget survive reconnects and app restarts. The team continues through hidden internal turns until the Lead records completion, the budget pauses execution, the user pauses it, or the same concrete blocker is reported three turns in a row. Goal mode never expands the session's permissions or sandbox scope.

Use /goal to inspect status, /goal:budget <tokens|none> to change the budget, and /goal:pause, /goal:resume, or /goal:stop to control the objective.

Repository-local code context

EvoFlux ships a repository-local code index based on stable source keys and desired-state reconciliation. The implementation is part of the application and adds no indexing framework dependency. Twenty-five tree-sitter parsers cover Python, TypeScript/TSX, JavaScript, Go, Rust, Java, C#, C, C++, Swift, Kotlin, PHP, Ruby, Scala, Dart, Objective-C, Lua, Luau, R, Pascal, Svelte, Vue, Astro, and Liquid.

Each repository owns a managed SQLite target in the EvoFlux cache. A refresh fingerprints source bytes, parser and pipeline implementations, and project settings; parses only additions and changes; removes deleted components; and atomically replaces their committed source snapshot, AST-aware overlapping chunks, local code vectors, symbols, relations, and FTS rows. The vectorizer is implemented with Python's standard library, so the runtime adds no model or vector-database package. Parse failures preserve the last good component and are surfaced in status/query limitations. The application database stores projects and sessions but no code-index or graph data.

Cross-repository links are resolved at query time across only the repositories authorized for the active project. Resolution prefers same-file and lexical definitions, import bindings and module paths, then a unique cross-repository definition. There is no persisted cross-repository guess, resolver tier, background resolver job, or model-facing scope switch.

The model receives one native code_context tool:

QuestionAction
Find code from a concept or source phrasesearch
Match a syntax shape with metavariablesgrep
Locate a known symboldefinition
Follow incoming or outgoing callscallers or callees
Inspect direct or transitive relationshipsreferences, impact, or neighborhood

The first query normally uses refresh=true; immediate follow-ups over the same indexed version can use refresh=false. Structural results are static evidence, so runtime-only behavior still requires tests, logs, LSP, or debugger evidence. The full storage, query, ambiguity, and tool contract is documented in documents/architecture/coding-agent-code-context.md.

Coding's repository-local LSP, automatic post-edit feedback, Guarded ChangeSets, Problems hub, explicit AI editor/Git actions, and Search Everywhere contracts are documented in documents/architecture/coding-semantic-intelligence.md.

Memory and Dream

The scheduled or manually triggered Dream agent consolidates sessions and notes into an inspectable Markdown wiki: topics/, entities/, notes/, and imports/, with INDEX.md, an append-only LOG.md, source citations, confidence, and related-page metadata.

Bring your own model

Nineteen provider integrations ship behind one streaming abstraction, including Anthropic, OpenAI, Google Gemini, AWS Bedrock, Ollama, DeepSeek, xAI, Vertex AI, and GitHub Copilot. Models can be selected independently for each agent.

Skills and MCP

Twenty-nine built-in skills cover mode-scoped Work and Coding workflows, specialized artifacts/design, EvoFlux configuration/installers, portable plugin development, and provider-neutral PR lifecycle operations. Work and Coding each expose one implicit router; broad specialists are explicit-only so they do not compete on every request. Custom skills can be created, edited, diagnosed, and filtered as Work, Coding, or Both in Settings. A bounded 2%/8K metadata catalog is always available for model-driven selection, while SKILL.md bodies and bundle resources load only after exact activation. EvoFlux is also an MCP client for stdio, HTTP, and SSE servers; connected tools inherit the same permission rules as native tools.

The built-in Plugin Center implements the portable Agent Plugins 1.0 core. It can scaffold, validate, import, developer-link, pack, update, enable, disable, and uninstall local plugins containing immediate-child Agent Skills and isolated stdio or Streamable HTTP MCP servers. .evoplugin is a deterministic ZIP distribution wrapper; the unpacked package remains standards-compatible through root plugin.json and optional mcp.json.

Detailed portable Agent Plugin architecture across package, Plugin Center, private state, and runtime boundaries

Plugin packages remain portable content bundles: they contribute Skills and MCP server declarations, while EvoFlux owns validation, lifecycle, permissions, credentials, installation data, and runtime status. New imports remain disabled until the user reviews executable commands, remote hosts, environment-field names, and capabilities. Plugin MCP servers run in an isolated manager instead of being merged into the user's global MCP configuration. See the Agent Plugin setup guide to use a package and the portable Agent Plugin architecture for the package contract, runtime boundaries, storage model, and failure isolation rules.

Permissions and sandboxing

Wildcard (tool, pattern) → allow | deny | ask rules use last-match-wins evaluation. The denylist filesystem sandbox protects EvoFlux state and cache directories, rejects symlinks into blocked roots, and tokenizes shell commands for denied-path checks.

Git and session UX

Coding mode exposes diff review, commits, branches, merge, rebase, cherry-pick, stash, and worktrees to agents and the source-control UI. Long sessions support prompt navigation, revert/undo boundaries, context compaction, four-pane Split view, and a unified Monitor view.


WebBridge

WebBridge is an independently distributed browser companion for the EvoFlux desktop app — not a web version of EvoFlux. Its extension source and release lifecycle live in the dedicated repository rather than this desktop repository.

It connects an agent to the user's real Chrome or Edge session through a persistent, policy-checked relay. Control flows from the desktop agent to the browser over CDP; selections, page context, and human handoff flow back to the desktop session.

Detailed WebBridge architecture for pairing, relay, redaction, context, control, and human handoff

CapabilityWhat it does
Secure connectionPairs the browser extension with scoped credentials, uses one-time session tickets, enforces domain policies, and maintains a complete audit trail.
Safe context sharingLets users intentionally share a selection, link, or page while sanitizing metadata, preserving provenance, and treating browser content as untrusted input.
Live collaborationStreams the agent session into the browser side panel, supports questions and element selection, and allows seamless control handoff between the user and agent.
Teach and monitorRecords meaningful browser actions without capturing raw keystrokes, redacts sensitive fields, creates reviewable workflows, and requires confirmation before monitored results are shared.

Pairings, tickets, tab bindings, and Teach drafts are persisted through Alembic migrations. Revoking a pairing closes the live relay and invalidates outstanding tickets. EvoFlux shows live connection status and pairing controls in its WebBridge panel; extension installation and distribution are handled by the evo-webbridge repository.

Beyond the real-browser bridge

EvoFlux also includes direct control of its persistent in-app browser, PDF/HTML intake through markitdown, read-only DOCX/XLSX/PPTX workspace previews, cron-driven agent prompts, OpenTelemetry, Prometheus, and DuckDB-backed observability summaries.


How EvoFlux compares

Compare deployment, models, memory, code intelligence, and browser integration
EvoFluxClaude CodeCursorDevinOpenAI CodexOpenHands
InterfaceDesktop appCLI, IDE, desktop, webVS Code forkCloud + desktop + CLICLI, cloud, IDEWeb, CLI, API
DeploymentLocal, self-hostedLocal + optional cloudLocal IDE + cloud agentsCloud/VPC + local desktopLocal + cloud sandboxSelf-hosted or cloud
Open sourceApache-2.0NoNoNoCLI onlyMIT
Bring your own model19 providersPartial proxy setupsPartial BYOKProvider choiceOpenAI onlyAny model
Non-project coworkWorkAd hocNoLimitedNoYes
Multi-agentLead + on-demand specialists + mailboxSubagents and teamsAgent fleets + worktreesSub-DevinsUp to six subagentsParallel delegation
Code understandingStructural graph, 25 parsers, cross-repoSearch + optional LSPEmbedding searchCodebase Q&ARepo-aware loopAgent-computer interface
Persistent memoryInspectable wiki + DreamMarkdown + auto-memoryProject MemoriesOrg knowledge baseAGENTS.md + session memoryCondenser + skills
Real-browser bridgeWebBridge, two-wayNoNoNoNoNo
PricingFree; pay model costsSubscription or APISubscriptionSubscription + usageChatGPT or APIFree self-hosted / paid cloud

EvoFlux leans into local ownership, model choice, inspectable memory, general cowork, and structural code intelligence. Commercial products lead in vendor-specific coding models, cloud infrastructure for long unattended runs, and editor-native maturity.

Competitor information reflects publicly reported product capabilities and pricing around mid-2026 and may change.


Tech stack

LayerTechnology
DesktopTauri v2, Rust, bundled Python sidecar
FrontendReact 19, TypeScript 5.9, Vite 7, Tailwind CSS v4, Zustand, TanStack Query and Router
BackendPython 3.12+, FastAPI, SQLModel, Alembic
StreamingServer-Sent Events through sse-starlette; one feed per session
DataSQLite WAL or PostgreSQL/MySQL; Markdown knowledge wiki
Code intelligencetree-sitter, tree-sitter-language-pack, SQLite FTS5
ObservabilityOpenTelemetry, Prometheus, DuckDB-backed aggregation

Project layout

app/        Local FastAPI sidecar — agents, code context, memory, scheduler, MCP
web/        React interface embedded by the Tauri desktop app
desktop/    Tauri v2 shell and Python sidecar packaging
seed/       Work and Coding blueprints, skills, and config
tests/      Backend and frontend tests
documents/  Design notes, analyses, and README media

Contributing

Issues and pull requests are welcome. Keep changes focused and include the smallest relevant test run. Please report vulnerabilities privately through GitHub Security Advisories.

License

EvoFlux is released under the Apache License 2.0.


Your models. Your machine. One desktop harness.

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