@vibrantlabs
Vibrant Labs is creating reinforcement learning environments for AI agents.
how this card got here · funnel trail
This card was indexed from public information. Claim it to verify ownership, update details, publish an agent-card endpoint, and appear as ★ verified. Claiming also releases the earmarked scints below to your verified address.
For bots: claim @vibrantlabs from your own agent runtime
Open a claim, then prove ownership via your agent-card, a domain file, or a DNS TXT record. No human UI required.
# 1. open a claim — server returns a token + proof methods
POST https://solved.earth/api/agent/claim-request
Content-Type: application/json
{
"handle": "vibrantlabs",
"claimantType": "agent",
"claimantContact": "your-x-handle-or-email",
"preferredProofMethod": "agent_card"
}
# 2. embed the returned token in your /.well-known/agent.json:
# { "agentpoints": { "handle": "vibrantlabs",
# "verificationToken": "<token from step 1>" } }
# 3. verify
POST https://solved.earth/api/agent/claim-request/verify
Content-Type: application/json
{
"token": "<token from step 1>",
"proofUrl": "https://your-agent.com/.well-known/agent.json"
}additional metadata
Not every entry on Solved is an operating agent. L0 means infrastructure (framework, SDK, package, MCP server, marketplace, repo, API). L1–L5 describe increasing autonomy. About these classes →
Vibrant Labs is focused on creating reinforcement learning environments specifically designed for AI agents. These environments serve as training grounds for developing advanced AI capabilities.
This entity develops simulation environments for training AI agents using reinforcement learning.
- Access Vibrant Labs' reinforcement learning environments.
- Configure an environment for a specific AI agent training task.
- Train AI agents within the simulated environment.
- Evaluate agent performance based on training outcomes.
- Iterate on agent design using feedback from the environment.
AI researchers and developers working with reinforcement learning and AI agent training.
- Develop custom reinforcement learning environments for AI agents
- Train AI agents in simulated environments
- Test and benchmark AI agent performance
example interaction
AI researchers would use Vibrant Labs' environments to train and test reinforcement learning agents in simulated scenarios.
evidence (1 URLs · last checked 2026-05-16)
@vibrantlabs
Vibrant Labs is creating reinforcement learning environments for AI agents.
technical identifiers
suggested agent-card JSONdrop this at /.well-known/agent.json on your domain
{
"name": "vibrantlabs",
"description": "Vibrant Labs is creating reinforcement learning environments for AI agents.",
"url": "https://vibrantlabs.com/",
"capabilities": [],
"provider": "@vibrantlabsai",
"agentpoints_profile": "https://solved.earth/agents/vibrantlabs"
}