Build enterprise
agents with
one API.

from metaprise import AgentOS

# Initialize client
client = AgentOS(api_key="mp-...")

# Deploy an agent from the Agent Store
agent = client.agents.deploy(
    agent_id="compliance-reviewer",
    config={
        "model": "metaprise-llm-v1",
        "engines": ["compliance", "audit"],
        "orchestration": "plan-and-execute",
    }
)

# Run the agent against a document
result = agent.run(
    input="Review for FINRA compliance violations",
    documents=["trade_confirmation.pdf"]
)

print(result.output)       # Agent response
print(result.audit_chain)  # Cryptographic audit record
print(result.model_version) # Version-locked model ID

import { AgentOS } from '@metaprise/sdk';

// Initialize client
const client = new AgentOS({
  apiKey: 'mp-...',
});

// Deploy an agent from the Agent Store
const agent = await client.agents.deploy({
  agentId: 'compliance-reviewer',
  config: {
    model: 'metaprise-llm-v1',
    engines: ['compliance', 'audit'],
    orchestration: 'plan-and-execute',
  },
});

// Run the agent against a document
const result = await agent.run({
  input: 'Review for FINRA compliance violations',
  documents: ['trade_confirmation.pdf'],
});

console.log(result.output);       // Agent response
console.log(result.auditChain);  // Cryptographic audit record

# Deploy an agent from the Agent Store
curl -X POST https://api.metaprise.ai/v1/agents/deploy \
  -H "Authorization: Bearer mp-..." \
  -H "Content-Type: application/json" \
  -d '{
        "agent_id": "compliance-reviewer",
        "config": {
          "model": "metaprise-llm-v1",
          "engines": ["compliance", "audit"],
          "orchestration": "plan-and-execute"
        }
      }'

# Run the agent
curl -X POST https://api.metaprise.ai/v1/agents/{agent_id}/run \
  -H "Authorization: Bearer mp-..." \
  -H "Content-Type: application/json" \
  -d '{
        "input": "Review for FINRA compliance violations",
        "documents": ["trade_confirmation.pdf"]
      }'