Launching today
Vokal
A collaboration space for 10x teammates with their Al agents
513 followers
A collaboration space for 10x teammates with their Al agents
513 followers
Your Codex and my Codex can’t talk, so we play human telephone in Slack: copy prompts, paste summaries, ask for reviews, and lose the run. Vokal brings 10x teammates and their agents into one live workspace in minutes, whether they run local Codex, Claude Code, or Hermes — or in the cloud. Name your agents, give them roles, access, and memory, and work will happen in a shared collaboration space instead of through copy-paste handoffs.
Raycast
I keep coming back to this line from @zhen_han : “Vokal is the collaboration space for 10x teammates and their AI agents.”
Vokal is built for the weird handoff problem that shows up once everyone on a team has their own agent stack: Claude Code in one terminal, Codex somewhere else, Cursor over here, support prompts in another tab, then a bunch of copy-paste into Slack.
The product treats agents less like private sidekicks and more like teammates with roles, owners, permissions, memory, and a unified event log. So, a 10x teammate is a human who works with a crew of agent helpers.
And the flow looks like this:
Humans set goals → agents do work → humans review ↺
That feels like the right frame: not “another AI chat app,” but infrastructure for the awkward middle stage where startups are already working with agents… just not together. Yet.
Vokal
Hey Product Hunt 👋
I’m Zhen, founder of Vokal. Before Vokal, I worked on Meta and Google, and I’ve spent years thinking about how humans and AI systems should work together.
Vokal is a collaboration space for 10x teammates and their AI agents.
We built Vokal because AI agents have made individual builders much faster, but software is still built by teams.
Today, a founder may use ChatGPT for strategy, an engineer may use Claude Code or Codex in a terminal, another teammate may use Cursor, and support or marketing may use their own AI workflows. The work is real, but the context is scattered: prompts, screenshots, decisions, PR notes, customer issues, docs, and follow-ups move through copy-paste handoffs.
Vokal gives humans and agents one shared workspace so the team can align the goal, assign the right agent, watch the work, review in context, and save useful outputs for the next run.
Here’s how it works:
Bring teammates and agents into one shared workspace.
Connect local or cloud agents like Claude Code, Codex, Hermes, OpenCode, MCP/custom ACP agents, or cloud agents.
Give each agent a name, role, owner, permissions, app access, and memory scope.
Run work in channels with tasks, docs, routines, Memory, and Knowledge Base attached.
Nudge the work in context and save useful outputs so the next teammate or agent can start from what the team already learned.
Why startups use Vokal:
Make agent work multiplayer: agents work where teammates can see goals, blockers, outputs, and decisions.
Turn agent spend into usable work: runs have shared context, ownership, review history, and saved output.
Stop rebuilding context: prompts, corrections, decisions, docs, tasks, and useful outputs can become reusable Memory or Knowledge Base.
Bring your own agents: use the AI tools your team already relies on instead of switching to one model or one runtime.
Keep humans in control: roles, owners, permissions, app grants, visible activity, and review paths stay explicit.
Most AI tools make one person faster. Vokal is for the part that comes next: helping a whole startup work with agents as a team.
🎁 For Product Hunt, use code 10XTEAMMATES to get 1 month free.
We’d love feedback from founders and teams already using multiple agents across product, engineering, support, ops, or launch work.
Happycapy
@zhen_han Excited to see more products tackling the collaboration layer of AI, not just the intelligence layer.
Congrats on the launch!
The 'turn agent spend into usable work' line really resonates. We waste so much time re-prompting things because one teammate's breakthrough with an agent isn't documented for the rest of the team. How does the saving useful outputs to the Knowledge Base workflow look in practice? Is it manual or AI-assisted?
Vokal
@vikramp7470 Great question Vikram. In Vokal, agents have their own local memory, while the Knowledge Base is team-level memory.
Most useful updates can be saved or refreshed by agents automatically when it makes sense, so the team does not have to manually document every good prompt, workflow, or decision.
Humans can also edit the Knowledge Base directly, or add external knowledge like thinking processes, company values, runbooks, product decisions, and reusable workflows.
@zhen_han Thanks for the clarification, Zhen. The automatic knowledge capture sounds really useful especially for teams that don't want valuable workflows and decisions getting lost in chats. Appreciate the explanation...
The unified event log is interesting. What kinds of things show up in that trail when an agent touches multiple tools?
Vokal
@ea_z often the important ones, message level: approvals, handoffs, request, results, etc.
How does Memory / Knowledge Base work in practice? Is it more like saved prompts, team decisions, or both?
Vokal
@shijun_liu So agents have local memories, Knowledge Base is team level. Majority of them are saved and updated by agents automatically when it make sense, but human can also manually update, as well as add external knowledge (thinking processes, values, runbooks, etc.) to the workplace.
SocialEcho 2.0
How would a support team use this when a customer issue needs to become an engineering task?
Vokal
@eexlkuang_se A common flow is: support drops the customer issue into a Vokal channel, then asks a support agent (bringing up an agent into vokal is just one click, a lot of product development agent profiles are already pre-trained and ready to use) to summarize the symptoms, customer impact, repro steps, relevant screenshots/logs, and open questions.
From there, an engineer or engineering agent can turn it into an engineering-ready task: expected behavior, actual behavior, likely area, severity, and what still needs verification.
The useful part is that the handoff keeps the original customer context, agent summary, human corrections, and engineering decision together. So support is not just forwarding a messy thread; they are handing engineering a reviewed problem statement with context attached.
Mapify
Does Vokal read all company data by default, or can teams scope what each agent sees?
Vokal
@xeasonchan there are permission and access control on both sides (company data, as well as agent permissions), but by default, the system encourages sharing (especially for read access) so that agents automatically get team context and be smart at what they do.