HTTP API Reference

https://api.plexor.dev

plx_user_abc123_a1b2c3d4e5f6g7h8i9j0k1l2m3n4o5p6

curl -X POST https://api.plexor.dev/v1/compress \
  -H "Content-Type: application/json" \
  -H "X-Plexor-Key: YOUR_API_KEY" \
  -d '{
    "prompt": "Please help me write a function that calculates the factorial of a number. The function should handle edge cases like negative numbers and zero.",
    "mode": "balanced"
  }'

{
  "original": {
    "text": "Please help me write a function that calculates...",
    "tokens": 52
  },
  "compressed": {
    "text": "Write factorial function. Handle negative, zero.",
    "tokens": 26
  },
  "compression_ratio": 0.5,
  "tokens_saved": 26,
  "estimated_savings_usd": 0.000078,
  "mode_used": "balanced",
  "techniques_applied": ["llmlingua-2"],
  "classification": {
    "category": "code",
    "confidence": 0.90,
    "task_type": "code_generation",
    "reasoning": "Code generation verb with code artifact detected.",
    "method": "fast_path",
    "suggested_provider": "deepseek"
  }
}

{
  "id": "msg_01XFDUDYJgAACzvnptvVoYEL",
  "type": "message",
  "role": "assistant",
  "content": [
    {
      "type": "text",
      "text": "Hello! I'm doing well, thank you for asking. How can I help you today?"
    }
  ],
  "model": "claude-3-5-sonnet-20241022",
  "stop_reason": "end_turn",
  "usage": {
    "input_tokens": 12,
    "output_tokens": 24
  },
  // Plexor Labs extension fields
  "plexor_provider_used": "deepseek",
  "plexor_session_id": "gw_01HQVX8K9JNMKQP3R4STUVWX",
  "plexor_cost_usd": 0.00012,
  "plexor_savings_usd": 0.00288
}

{
  "optimized_messages": [...],
  "original_tokens": 12,
  "optimized_tokens": 10,
  "tokens_saved": 2,
  "compression_ratio": 0.833,
  "recommended_provider": "deepseek",
  "classification": {
    "category": "code",
    "confidence": 0.90,
    "task_type": "code_generation",
    "reasoning": "Code generation verb with code artifact detected.",
    "method": "fast_path",
    "suggested_provider": "deepseek"
  }
}

curl -X POST https://api.plexor.dev/gateway/anthropic/v1/messages \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "claude-3-5-sonnet-20241022",
    "max_tokens": 1024,
    "stream": true,
    "messages": [
      {"role": "user", "content": "Write a haiku about programming"}
    ]
  }'

{
  "type": "error",
  "error": {
    "type": "invalid_request_error",
    "message": "Invalid model name: 'gpt-5-ultra'. See supported models at https://docs.plexor.ai/models"
  }
}

curl -X POST https://api.plexor.dev/gateway/anthropic/v1/messages \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "X-Plexor-Mode: balanced" \
  -d '{
    "model": "claude-3-5-sonnet-20241022",
    "max_tokens": 1024,
    "messages": [
      {
        "role": "user",
        "content": "Explain the concept of recursion in programming."
      }
    ],
    "system": "You are a helpful programming tutor. Explain concepts clearly with examples."
  }'

import requests

# Configuration
API_KEY = "YOUR_API_KEY"
BASE_URL = "https://api.plexor.dev"

# Make the request
response = requests.post(
    f"{BASE_URL}/gateway/anthropic/v1/messages",
    headers={
        "Content-Type": "application/json",
        "Authorization": f"Bearer {API_KEY}",
        "X-Plexor-Mode": "balanced",
    },
    json={
        "model": "claude-3-5-sonnet-20241022",
        "max_tokens": 1024,
        "messages": [
            {
                "role": "user",
                "content": "What is the capital of France?"
            }
        ]
    }
)

# Handle the response
if response.status_code == 200:
    data = response.json()
    print(f"Response: {data['content'][0]['text']}")
    print(f"Provider used: {data.get('plexor_provider_used', 'unknown')}")
    print(f"Cost: ${data.get('plexor_cost_usd', 0):.6f}")
else:
    print(f"Error: {response.status_code} - {response.text}")

import anthropic

# Point the Anthropic SDK at Plexor Labs
client = anthropic.Anthropic(
    api_key="YOUR_PLEXOR_API_KEY",
    base_url="https://api.plexor.dev/gateway/anthropic"
)

# Use the SDK normally - requests are routed through Plexor Labs
message = client.messages.create(
    model="claude-3-5-sonnet-20241022",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Hello, how are you?"}
    ]
)

print(message.content[0].text)

const API_KEY = 'YOUR_API_KEY';
const BASE_URL = 'https://api.plexor.dev';

async function chat(message) {
  const response = await fetch(
    `${BASE_URL}/gateway/anthropic/v1/messages`,
    {
      method: 'POST',
      headers: {
        'Content-Type': 'application/json',
        'Authorization': `Bearer ${API_KEY}`,
        'X-Plexor-Mode': 'balanced',
      },
      body: JSON.stringify({
        model: 'claude-3-5-sonnet-20241022',
        max_tokens: 1024,
        messages: [
          { role: 'user', content: message }
        ]
      })
    }
  );

  if (!response.ok) {
    throw new Error(`HTTP error: ${response.status}`);
  }

  const data = await response.json();

  console.log('Response:', data.content[0].text);
  console.log('Provider:', data.plexor_provider_used);
  console.log('Cost: $' + (data.plexor_cost_usd || 0).toFixed(6));

  return data;
}

// Example usage
chat('Explain JavaScript closures in simple terms.')
  .catch(console.error);

import OpenAI from 'openai';

// Point the OpenAI SDK at Plexor Labs' OpenAI-compatible endpoint
const client = new OpenAI({
  apiKey: 'YOUR_PLEXOR_API_KEY',
  baseURL: 'https://api.plexor.dev/gateway/openai/v1',
});

async function main() {
  const completion = await client.chat.completions.create({
    model: 'gpt-4o',
    messages: [
      { role: 'user', content: 'Write a haiku about coding.' }
    ],
  });

  console.log(completion.choices[0].message.content);
}

main();

import anthropic

client = anthropic.Anthropic(
    api_key="YOUR_PLEXOR_API_KEY",
    base_url="https://api.plexor.dev/gateway/anthropic"
)

# Stream the response
with client.messages.stream(
    model="claude-3-5-sonnet-20241022",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Write a short story about a robot learning to paint."}
    ]
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)

print()  # Final newline