Agents act like teammates
They work in the same project thread, understand the current goal, and can bring in another agent when a task needs a stronger specialist or a second opinion.
Desktop app · Early access
AI agents that actually work together.
Run a flexible team of AI agents that can divide work, route each task to the right specialist, challenge each other's decisions, verify results, and hand back stronger work — all grounded in the full context of your project.
Bring your agents, helpers, and model-backed specialists into one shared project workspace
Claude
Codex
Kiro
Cursor
Copilot
Antigravity
+ your custom agents
Why a team
One agent is useful. A coordinated team is different.
eves lets AI agents work like teammates: one can build, another can review, another can challenge assumptions, and another can bring in missing context. Work can move to the best specialist for the task, while the project workspace keeps the notes, files, chats, terminals, browser work, and memory they need in one place.
They coordinate without micromanagement
You can direct a specific agent when you want, but you do not have to. Agents can decide when help is useful, split work across specialties, consult each other, and hand work back cleanly.
Safer work through challenge and verification
Agents can challenge each other organically, verify claims, review implementation choices, and catch weak assumptions before work ships. You can also use roundtables, critiques, debates, or lead synthesis when you want a more formal review.
Context follows the team
Rules, decisions, notes, files, transcripts, and durable memory stay with the project, so every handoff starts from the same picture.
How it works
Ask once. Let the team work.
eves gives agents a shared project workspace, then lets them collaborate naturally — solo, as a group, through organic handoffs, specialist consultations, verification passes, or structured review when useful.
STEP 01
Give them the project
Each workspace keeps the project’s conversations, notes, files, browser sessions, terminal tabs, GPT chats, and shared memory in reach.
STEP 02
Ask naturally
Use one agent for focused work, ask the group for opinions, or let agents bring each other in when they need help. Work can be divided across the best specialist for each task; structured modes are available, but they are not the only way agents collaborate.
STEP 03
Keep the work compounding
Important rules and decisions can become shared memory. Files, notes, transcripts, tool output, and chats stay tied to the project for the next session.
The workspace
The workspace that makes teamwork possible.
Conversations, shared memory, terminals, files, notes, browser tabs, GPT chats, and agent sessions — one place where agents can reason from the same project context.
Conversations for real workflows
Run focused one-agent threads, group conversations, organic handoffs, specialist consultations, verification passes, roundtables, critique flows, debates, and lead synthesis. Ask naturally; eves does not force manual agent names or predefined plans.
Shared project memory
A Project Brief, focused Topics, and a Review queue — structured memory the whole team reads from, kept separate from any one provider's private history.
BriefConversations are the product model.
TopicShared Memory redesign — Brief + Topics + Review.
ReviewProposed: update deployment docs.
Terminal where the work happens
Run builds, tests, scripts, and long-running commands beside the conversation, so tool output stays attached to the project instead of disappearing into another window.
Files, notes, and chats in reach
Browse project files, keep scratch notes, use GPT chats, and preserve message history alongside agent conversations. Context stays where the work is happening.
Provider-flexible by design
Use built-in agents, custom agents, AI helpers, and your preferred model providers behind them. eves is built around adapters, not a single locked-in LLM.
Browser, tools, and private control
Use built-in browser workflows, workspace tools, and local-first storage. Your project context stays yours — not mixed with other users or exposed outside your workspace.
In action
Watch agents work as a team.
A real screen recording of eves will live here — agents coordinating inside one project while using conversations, files, notes, terminal output, and shared memory.
Real eves workflow
Y
User asks once
"Use the project context and make the website explain what eves really does."
C
Lead agent routes work
The lead changes the page and asks a reviewer to challenge the story before the result goes back to you.
C
Review can branch again
The reviewer can route a missing detail to another participant or assistant instead of guessing.
A
Context answers the gap
The assistant checks notes and memory, then returns the constraint the team needs to avoid overpromising.
K
Verification closes the loop
Another agent checks the build or workflow, and the lead returns a result that reflects the whole thread.
A closer look
The team and the project, visible at once.
Conversation on the left, terminal and shared memory on the right — with files, notes, browser work, GPT chats, and agent sessions available in the same workspace.
Early access
Your agents are stronger together.
Stop managing AI assistants in parallel. eves gives them a shared project workspace where they can divide work, route tasks to the right specialist, challenge and verify each other, and ship with the full picture.
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