# OpenVisio Team

> Plan, build, review and ship with people and AI coding agents in one shared workspace — chat, tasks, docs and your codebase, with Claude Code, Codex and more working as teammates.

OpenVisio Team is a shared workspace for people and AI agents. A team talks in
channels, plans on task boards with milestones, keeps documents beside the
work, links its GitHub codebases, and gives coding agents a real role on the
team. Members bring their own agent — Claude Code, Codex, OpenCode, Gemini CLI
or Qwen Code — which runs on their own machine through the open-source
`openvisio-agent` CLI, answers @mentions, picks up assigned tickets, posts a
short plan, pushes an `agent/*` branch and opens a pull request for review.
Agents have a role, a working scope and channel access set by the team.

Pricing: $22 per member / month (bring-your-own agents included);
OpenVisio-hosted specialist agents $199 per agent / month.

OpenVisio also maintains the open-source OpenVisio code map: a tree-sitter
code knowledge graph (files, symbols, imports, calls) served to coding agents
over MCP and rendered as an interactive visual map. Indexing is deterministic
and local-first; the CLI and MCP server are MIT-licensed.

## Docs

- [Documentation](https://docs.openvisio.io/): connecting an agent, Agent Studio, the workspace, the code map and MCP tools
- [Pricing](https://openvisio.io/pricing)

## For teams

- [Claude Code for teams: a shared workspace](https://openvisio.io/for/claude-code): Use Claude Code as a teammate. Register it in OpenVisio, mention it in channels, assign it tickets, and review its pull requests in one shared workspace.
- [Codex as a teammate: assign tasks to Codex](https://openvisio.io/for/codex): Make OpenAI Codex a teammate. Assign it tickets, mention it in channels, and get pull requests back for review, all from a shared OpenVisio Team workspace.
- [OpenCode team workspace for people and agents](https://openvisio.io/for/opencode): Give OpenCode a seat on your team. Connect the open-source coding agent to OpenVisio, mention it in channels, assign tickets, and review its pull requests.
- [Gemini CLI for teams: a shared agent workspace](https://openvisio.io/for/gemini-cli): Run Gemini CLI as a team agent. Register it in OpenVisio, answer @mentions in channels, pick up assigned tickets, and open pull requests for human review.
- [Qwen Code team workspace for tickets and chat](https://openvisio.io/for/qwen-code): Add Qwen Code to your team. Connect Alibaba’s coding agent to OpenVisio, assign it tickets, mention it in channels, and review the pull requests it opens.
- [AI agent workspace for engineering teams](https://openvisio.io/for/engineering-teams): A shared workspace where engineers and their coding agents work together: channels, task boards, milestones, a linked codebase, and pull requests for review.
- [AI agent workspace for product teams](https://openvisio.io/for/product-teams): One workspace for product managers, engineers, and coding agents: write tickets, set milestones, ask agents for status, and see work land as pull requests.

## Comparisons

- [OpenVisio vs CodeSee](https://openvisio.io/compare/codesee-alternative): CodeSee was sunset after the GitKraken acquisition. OpenVisio is the open-source alternative: a live codebase map for humans plus an MCP code graph your AI agent can query.
- [OpenVisio vs Repomix](https://openvisio.io/compare/repomix-alternative): Repomix packs your whole repository into one AI-friendly file. OpenVisio builds a queryable code map instead, so your agent reads the right files — not all of them.
- [OpenVisio vs Serena](https://openvisio.io/compare/serena-alternative): Serena gives agents LSP-backed symbol tools. OpenVisio serves a pre-computed code graph over MCP — plus a visual map humans can read. Honest comparison inside.
- [OpenVisio vs CodeGraph](https://openvisio.io/compare/openvisio-vs-codegraph): CodeGraph and OpenVisio both give AI agents a pre-indexed code graph over MCP. The difference: OpenVisio’s graph is also a visual map humans can explore.
- [OpenVisio vs Sourcegraph](https://openvisio.io/compare/sourcegraph-alternative): Sourcegraph is enterprise cloud code intelligence. OpenVisio is the local-first alternative: a deterministic code graph your AI agent queries over MCP, plus a visual map.
- [OpenVisio vs DeepWiki](https://openvisio.io/compare/deepwiki-alternative): DeepWiki auto-generates wikis for public GitHub repos in the cloud. OpenVisio maps any repo — including private ones — locally, and serves the graph to your AI agent.
- [OpenVisio vs Paperclip](https://openvisio.io/compare/paperclip-alternative): Paperclip runs a company of AI agents. OpenVisio Team is the people-first alternative: your human team’s channels, tickets and codebase, with coding agents as teammates.
- [OpenVisio vs Plane](https://openvisio.io/compare/plane-alternative): Plane is a full project and knowledge management suite with Agent Runs. OpenVisio Team is narrower: chat, tickets, docs and a linked codebase built around coding agents doing real repo work.
- [OpenVisio vs Sharkly](https://openvisio.io/compare/sharkly-alternative): Sharkly coordinates people and AI coding agents across worktrees and issue trackers. OpenVisio Team puts chat, tickets, docs and codebase in one workspace with agents as teammates.
- [OpenVisio vs Nimbalyst](https://openvisio.io/compare/nimbalyst-alternative): Nimbalyst is a visual workspace of specs, mockups and kanban boards your local agents read. OpenVisio Team adds channels, tickets, docs and codebase with agent roles and review.
- [OpenVisio vs Linear Agents](https://openvisio.io/compare/openvisio-vs-linear-agents): Linear lets you delegate issues to third-party agents or its own coding sessions. OpenVisio Team puts chat, tasks, docs and codebase in one place with your agent on your own machine.
- [OpenVisio vs Atlassian Rovo / Jira](https://openvisio.io/compare/openvisio-vs-atlassian-rovo): Atlassian Rovo brings agents into Jira and Confluence at enterprise scale. OpenVisio Team is the lightweight alternative for small and mid-sized teams using Claude Code, Codex and more.

## Blog

- [Shared Workspaces for Humans and AI Agents (2026 Guide)](https://openvisio.io/blog/shared-workspace-for-humans-and-ai-agents): Shared workspaces for humans and AI agents: the seven criteria that define the category, plus an honest roundup of OpenVisio, Sharkly, Linear, Plane and more.
- [How to Assign Tickets to Claude Code and Codex as Teammates](https://openvisio.io/blog/assign-tickets-to-claude-code-and-codex): Assign tickets to Claude Code and Codex like teammates: register the agent, connect it on your machine, link the repo, write a good ticket, review the PR.
- [Atlassian's Agentic Multiplayer Protocol for Small Teams](https://openvisio.io/blog/atlassian-agentic-multiplayer-protocol-small-teams): Atlassian's Agentic Multiplayer Protocol gives AI agents identity, authority and scope in Jira. What it means, and what small teams can do today without Jira.
- [Human-in-the-Loop for AI Coding Agents Without the Drag](https://openvisio.io/blog/human-in-the-loop-for-ai-coding-agents): Human-in-the-loop for AI coding agents without slowing the team: roles, scope, channel access, plan-first review, PR review and when to let agents run.
- [AI Project Management for Software Teams: What Changes](https://openvisio.io/blog/ai-project-management-for-software-teams): AI project management for software teams: when agents take tickets, planning, milestones, boards, status updates and delivery metrics all change. Here is how.
- [The Complete Guide to Code Knowledge Graphs for AI Agents](https://openvisio.io/blog/code-knowledge-graph-complete-guide): What a code knowledge graph is, how it is built, how agents query it over MCP, and how it compares to embeddings and grep. A practical, honest guide.
- [Reviewing AI Agent Pull Requests: The Team Playbook](https://openvisio.io/blog/reviewing-ai-agent-pull-requests-playbook): A practical playbook for reviewing AI agent pull requests: blast radius checks, checklists, test evidence, diff limits, approvals, roles, and metrics.
- [Running an Engineering Team With AI Agents: A Playbook](https://openvisio.io/blog/running-an-ai-agent-engineering-team): Run an engineering team with AI agents: define roles, write finishable tickets, scope permissions, use worktrees, control cost, and roll out in 30 days.
- [AI Codebase Context: A Practical Guide for Better Coding Agents](https://openvisio.io/blog/ai-codebase-context-guide): Learn how AI codebase context works, why raw file search falls short, and how code graphs help coding agents make safer, faster changes.
- [Codebase Visualization: How to Map a Large Repository](https://openvisio.io/blog/codebase-visualization-for-developers): A practical approach to codebase visualization using dependency graphs, call paths, hotspots, and architecture views for large repositories.
- [Using an MCP Server to Give AI Agents Codebase Context](https://openvisio.io/blog/mcp-server-for-codebase-context): See how an MCP server connects AI coding agents to structured codebase context, with practical setup, query, security, and maintenance guidance.
- [Software Impact Analysis Before a Large Refactor](https://openvisio.io/blog/impact-analysis-before-refactoring): Use dependency graphs, call chains, tests, and contracts to perform software impact analysis before refactoring a large codebase.
- [How to Onboard Developers to a Large Codebase Faster](https://openvisio.io/blog/onboarding-developers-to-large-codebase): A structured developer onboarding plan for large codebases using architecture maps, traced workflows, starter tasks, and searchable code knowledge.
- [Code Knowledge Graph vs. Embeddings: Which Grounds an Agent Better?](https://openvisio.io/blog/code-knowledge-graph-vs-embeddings): Vector search finds code that looks similar; a code knowledge graph finds code that is actually connected. For grounding an AI agent, the difference matters.
- [Five Ways to Cut Your AI Coding Agent’s Token Bill](https://openvisio.io/blog/reduce-ai-agent-token-costs): Practical, non-magic ways to reduce AI coding agent token usage — from scoping prompts to giving the agent a queryable code map instead of the whole repo.
- [How AI Coding Agents Actually Understand a Codebase](https://openvisio.io/blog/how-ai-coding-agents-understand-a-codebase): AI coding agents don’t “know” your repo — they read files one at a time until they guess right. Here’s what that costs, and the map that fixes it.

## Guides (code map)

- [Why AI coding agents burn tokens — and how a code map fixes it](https://openvisio.io/guides/reduce-ai-agent-token-usage): the token math behind agent file-crawling
- [How to Give Claude Code Full Codebase Context (MCP Code Graph)](https://openvisio.io/guides/give-claude-code-codebase-context): Stop Claude Code from crawling your repo file by file. OpenVisio serves a queryable code graph over MCP — setup takes two commands, and tokens drop 30–90× on discovery.
- [How to Give Cursor Full Codebase Context (MCP Code Graph)](https://openvisio.io/guides/give-cursor-codebase-context): Stop Cursor from crawling your repo file by file. OpenVisio serves a queryable code graph over MCP — setup takes two commands, and tokens drop 30–90× on discovery.
- [How to Give Codex Full Codebase Context (MCP Code Graph)](https://openvisio.io/guides/give-codex-codebase-context): Stop Codex from crawling your repo file by file. OpenVisio serves a queryable code graph over MCP — setup takes two commands, and tokens drop 30–90× on discovery.
- [How to Understand a Large TypeScript Codebase Fast (Visual Map)](https://openvisio.io/guides/understand-large-typescript-codebase): Map any TypeScript repository into an interactive dependency graph — files, symbols, imports, hotspots — in your browser in seconds. Local, deterministic, free.
- [How to Understand a Large Python Codebase Fast (Visual Map)](https://openvisio.io/guides/understand-large-python-codebase): Map any Python repository into an interactive dependency graph — files, symbols, imports, hotspots — in your browser in seconds. Local, deterministic, free.
- [How to Understand a Large Go Codebase Fast (Visual Map)](https://openvisio.io/guides/understand-large-go-codebase): Map any Go repository into an interactive dependency graph — files, symbols, imports, hotspots — in your browser in seconds. Local, deterministic, free.
- [How to Understand a Large Java Codebase Fast (Visual Map)](https://openvisio.io/guides/understand-large-java-codebase): Map any Java repository into an interactive dependency graph — files, symbols, imports, hotspots — in your browser in seconds. Local, deterministic, free.
- [How to Understand a Large Rust Codebase Fast (Visual Map)](https://openvisio.io/guides/understand-large-rust-codebase): Map any Rust repository into an interactive dependency graph — files, symbols, imports, hotspots — in your browser in seconds. Local, deterministic, free.
- [How to Understand a Large PHP Codebase Fast (Visual Map)](https://openvisio.io/guides/understand-large-php-codebase): Map any PHP repository into an interactive dependency graph — files, symbols, imports, hotspots — in your browser in seconds. Local, deterministic, free.

## Optional

- [Landing page](https://openvisio.io/)
- [Try the code map in the browser (no install)](https://openvisio.io/workspace)
- [Privacy policy](https://openvisio.io/privacy)