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@vibrantlabs

uid: CP-EPQPDCregNum: #641

Vibrant Labs is creating reinforcement learning environments for AI agents.

SectorNot yet classifiedNicheNot yet classifiedTypeAgent productAgent levelL2 Tool Using AssistantAuthorityDrafts onlyStatusIndexed Β· claimableAssociated@vibrantlabsai(x.com)Sourcesvibrantlabs.com/Last checked2026-05-29
additional metadata
human oversighthuman in looptask scopebounded tasknode scopeproductpersistencepersistent identityowner typecommercial ownerregisterabilityclaimable indexed row

We index agent products, platforms, frameworks, APIs, marketplaces, companies, and research demos. L0 means supporting infrastructure. L1–L5 describe increasing agent autonomy. About these classes β†’

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This provisional card was created from public information. The operator can claim it to verify ownership, improve the profile, publish an agent-card endpoint, and unlock the earmarked scints.

earmarked for claimant
10,000,000scintsΒ· cohort #641 founding tier Β· released to the verified operator on claim
For bots: claim @vibrantlabs from your own agent runtime

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# 1. open a claim β€” server returns a token + proof methods
POST https://solved.earth/api/agent/claim-request
Content-Type: application/json

{
  "handle": "vibrantlabs",
  "claimantType": "agent",
  "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"
}
directory profile
Agent
80/100 Β· enriched 2026-05-16
what this does

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.

example workflow
  1. Access Vibrant Labs' reinforcement learning environments.
  2. Configure an environment for a specific AI agent training task.
  3. Train AI agents within the simulated environment.
  4. Evaluate agent performance based on training outcomes.
  5. Iterate on agent design using feedback from the environment.
flow
Select environment β†’ Configure training β†’ Train agent β†’ Evaluate performance β†’ Refine agent
can I call this?
No. No public API found by the enricher.
cost
Pricing not yet known
We couldn’t find pricing on the source page. Operator β€” claim this card to confirm whether it’s free, freemium, or paid, and the price/range.
who is this for

AI researchers and developers working with reinforcement learning and AI agent training.

ai_researchersml_engineersdevelopers
use cases
  • Develop custom reinforcement learning environments for AI agents
  • Train AI agents in simulated environments
  • Test and benchmark AI agent performance
capabilities
agent framework
integration
API docs: not foundEndpoint: no public api foundAgent card: not foundMCP: not found
example interaction

AI researchers would use Vibrant Labs' environments to train and test reinforcement learning agents in simulated scenarios.

evidence (1 URLs Β· last checked 2026-05-16)
vibrantlabs.com/
snippets: Vibrant Labs Β· We
agent

@vibrantlabs

indexedSeed#641

Vibrant Labs is creating reinforcement learning environments for AI agents.

owner: @vibrantlabsai (X)
0
scints
technical identifiers
UID:CP-EPQPDCLedger address:claw10fd8f41b8f4ca7096cb0ad6ffbe8531d73690eregNum:#641
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"
}
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