The Infrastructure of Uncensored Intelligence

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.shannon-ai.com/v1"
)

response = client.chat.completions.create(
    model="shannon-3",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello, Shannon!"}
    ],
    max_tokens=1024
)

print(response.choices[0].message.content)

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: 'YOUR_API_KEY',
  baseURL: 'https://api.shannon-ai.com/v1'
});

const response = await client.chat.completions.create({
  model: 'shannon-3',
  messages: [
    { role: 'system', content: 'You are a helpful assistant.' },
    { role: 'user', content: 'Hello, Shannon!' }
  ],
  max_tokens: 1024
});

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

package main

import (
    "context"
    "fmt"
    openai "github.com/sashabaranov/go-openai"
)

func main() {
    config := openai.DefaultConfig("YOUR_API_KEY")
    config.BaseURL = "https://api.shannon-ai.com/v1"
    client := openai.NewClientWithConfig(config)

    resp, err := client.CreateChatCompletion(
        context.Background(),
        openai.ChatCompletionRequest{
            Model: "shannon-3",
            Messages: []openai.ChatCompletionMessage{
                {Role: "system", Content: "You are a helpful assistant."},
                {Role: "user", Content: "Hello, Shannon!"},
            },
            MaxTokens: 1024,
        },
    )
    if err != nil {
        panic(err)
    }
    fmt.Println(resp.Choices[0].Message.Content)
}

curl -X POST "https://api.shannon-ai.com/v1/chat/completions" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "shannon-3",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "Hello, Shannon!"}
    ],
    "max_tokens": 1024
  }'

Authorization: Bearer YOUR_API_KEY

x-api-key: YOUR_API_KEY
anthropic-version: 2023-06-01

from openai import OpenAI
import json

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.shannon-ai.com/v1"
)

# Define available tools/functions
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get current weather for a location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "City name, e.g., 'Tokyo'"
                    },
                    "unit": {
                        "type": "string",
                        "enum": ["celsius", "fahrenheit"]
                    }
                },
                "required": ["location"]
            }
        }
    }
]

response = client.chat.completions.create(
    model="shannon-3",
    messages=[{"role": "user", "content": "What's the weather in Tokyo?"}],
    tools=tools,
    tool_choice="auto"
)

# Check if model wants to call a function
if response.choices[0].message.tool_calls:
    tool_call = response.choices[0].message.tool_calls[0]
    print(f"Function: {tool_call.function.name}")
    print(f"Arguments: {tool_call.function.arguments}")

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: 'YOUR_API_KEY',
  baseURL: 'https://api.shannon-ai.com/v1'
});

const tools = [
  {
    type: 'function',
    function: {
      name: 'get_weather',
      description: 'Get current weather for a location',
      parameters: {
        type: 'object',
        properties: {
          location: { type: 'string', description: "City name" },
          unit: { type: 'string', enum: ['celsius', 'fahrenheit'] }
        },
        required: ['location']
      }
    }
  }
];

const response = await client.chat.completions.create({
  model: 'shannon-3',
  messages: [{ role: 'user', content: "What's the weather in Tokyo?" }],
  tools,
  tool_choice: 'auto'
});

if (response.choices[0].message.tool_calls) {
  const toolCall = response.choices[0].message.tool_calls[0];
  console.log('Function:', toolCall.function.name);
  console.log('Arguments:', toolCall.function.arguments);
}

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.shannon-ai.com/v1"
)

# Force JSON output with schema
response = client.chat.completions.create(
    model="shannon-3",
    messages=[
        {"role": "user", "content": "Extract: John Doe, 30 years old, engineer"}
    ],
    response_format={
        "type": "json_schema",
        "json_schema": {
            "name": "person_info",
            "schema": {
                "type": "object",
                "properties": {
                    "name": {"type": "string"},
                    "age": {"type": "integer"},
                    "occupation": {"type": "string"}
                },
                "required": ["name", "age", "occupation"]
            }
        }
    }
)

import json
data = json.loads(response.choices[0].message.content)
print(data)  # {"name": "John Doe", "age": 30, "occupation": "engineer"}

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: 'YOUR_API_KEY',
  baseURL: 'https://api.shannon-ai.com/v1'
});

const response = await client.chat.completions.create({
  model: 'shannon-3',
  messages: [
    { role: 'user', content: 'Extract: John Doe, 30 years old, engineer' }
  ],
  response_format: {
    type: 'json_schema',
    json_schema: {
      name: 'person_info',
      schema: {
        type: 'object',
        properties: {
          name: { type: 'string' },
          age: { type: 'integer' },
          occupation: { type: 'string' }
        },
        required: ['name', 'age', 'occupation']
      }
    }
  }
});

const data = JSON.parse(response.choices[0].message.content);
console.log(data); // { name: "John Doe", age: 30, occupation: "engineer" }

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.shannon-ai.com/v1"
)

# Enable streaming for real-time responses
# Thinking models stream reasoning_content first, then content
stream = client.chat.completions.create(
    model="GLM-5.2-3BIT-REAP",
    messages=[
        {"role": "user", "content": "Write a short poem about AI"}
    ],
    stream=True
)

for chunk in stream:
    delta = chunk.choices[0].delta
    if getattr(delta, "reasoning_content", None):
        print(delta.reasoning_content, end="", flush=True)  # thinking stream
    if delta.content:
        print(delta.content, end="", flush=True)

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: 'YOUR_API_KEY',
  baseURL: 'https://api.shannon-ai.com/v1'
});

// Enable streaming for real-time responses
// Thinking models stream reasoning_content first, then content
const stream = await client.chat.completions.create({
  model: 'GLM-5.2-3BIT-REAP',
  messages: [
    { role: 'user', content: 'Write a short poem about AI' }
  ],
  stream: true
});

for await (const chunk of stream) {
  const delta = chunk.choices[0]?.delta;
  if (delta?.reasoning_content) process.stdout.write(delta.reasoning_content); // thinking stream
  if (delta?.content) process.stdout.write(delta.content);
}

from openai import OpenAI

client = OpenAI(api_key="YOUR_API_KEY", base_url="https://api.shannon-ai.com/v1")

response = client.responses.create(
    model="shannon-3",
    instructions="You are a concise assistant.",
    input="Summarize the three-way handshake in two sentences.",
    reasoning={"effort": "low"},
)
print(response.output_text)

# Tool loop: function_call items come back in response.output; answer them
# with function_call_output items on the next call.

import OpenAI from 'openai';

const client = new OpenAI({ apiKey: 'YOUR_API_KEY', baseURL: 'https://api.shannon-ai.com/v1' });

const response = await client.responses.create({
  model: 'shannon-3',
  instructions: 'You are a concise assistant.',
  input: 'Summarize the three-way handshake in two sentences.',
  reasoning: { effort: 'low' },
});
console.log(response.output_text);

from openai import OpenAI

client = OpenAI(api_key="YOUR_API_KEY", base_url="https://api.shannon-ai.com/v1")

# reasoning_effort: off | low | medium | high. Read by the hosted open-weight
# models, where it sets how long the solve pass thinks. Shannon tiers accept the
# field and pick their own depth -- GET /v1/models reports which is which.
stream = client.chat.completions.create(
    model="GLM-5.2-3BIT-REAP",
    reasoning_effort="medium",
    stream=True,
    messages=[{"role": "user", "content": "Is 221 prime?"}],
)
for chunk in stream:
    delta = chunk.choices[0].delta
    if getattr(delta, "reasoning_content", None):
        print(delta.reasoning_content, end="", flush=True)   # thinking
    if delta.content:
        print(delta.content, end="", flush=True)             # answer

import OpenAI from 'openai';

const client = new OpenAI({ apiKey: 'YOUR_API_KEY', baseURL: 'https://api.shannon-ai.com/v1' });

// On /v1/messages the same knob is thinking: { type: 'enabled', budget_tokens }.
const stream = await client.chat.completions.create({
  model: 'GLM-5.2-3BIT-REAP',
  reasoning_effort: 'medium', // read by the hosted open-weight models
  stream: true,
  messages: [{ role: 'user', content: 'Is 221 prime?' }],
});
for await (const chunk of stream) {
  const d = chunk.choices[0]?.delta ?? {};
  if (d.reasoning_content) process.stdout.write(d.reasoning_content);
  if (d.content) process.stdout.write(d.content);
}

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.shannon-ai.com/v1"
)

# Web search is automatically available!
# Shannon will use it when needed for current information

response = client.chat.completions.create(
    model="shannon-3",
    messages=[
        {"role": "user", "content": "What are the latest AI news today?"}
    ],
    # Optionally, explicitly define web_search tool
    tools=[{
        "type": "function",
        "function": {
            "name": "web_search",
            "description": "Search the web for current information",
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {"type": "string", "description": "Search query"}
                },
                "required": ["query"]
            }
        }
    }]
)

print(response.choices[0].message.content)
# Response includes sources and citations

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: 'YOUR_API_KEY',
  baseURL: 'https://api.shannon-ai.com/v1'
});

// Web search is automatically available!
// Shannon will use it when needed for current information

const response = await client.chat.completions.create({
  model: 'shannon-3',
  messages: [
    { role: 'user', content: 'What are the latest AI news today?' }
  ],
  // Optionally, explicitly define web_search tool
  tools: [{
    type: 'function',
    function: {
      name: 'web_search',
      description: 'Search the web for current information',
      parameters: {
        type: 'object',
        properties: {
          query: { type: 'string', description: 'Search query' }
        },
        required: ['query']
      }
    }
  }]
});

console.log(response.choices[0].message.content);
// Response includes sources and citations

import anthropic

client = anthropic.Anthropic(
    api_key="YOUR_API_KEY",
    base_url="https://api.shannon-ai.com"
)

response = client.messages.create(
    model="shannon-3",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Hello, Shannon!"}
    ],
    # Tool use (Anthropic format)
    tools=[{
        "name": "web_search",
        "description": "Search the web",
        "input_schema": {
            "type": "object",
            "properties": {
                "query": {"type": "string"}
            },
            "required": ["query"]
        }
    }]
)

print(response.content[0].text)

import Anthropic from '@anthropic-ai/sdk';

const client = new Anthropic({
  apiKey: 'YOUR_API_KEY',
  baseURL: 'https://api.shannon-ai.com'
});

const response = await client.messages.create({
  model: 'shannon-3',
  max_tokens: 1024,
  messages: [
    { role: 'user', content: 'Hello, Shannon!' }
  ],
  // Tool use (Anthropic format)
  tools: [{
    name: 'web_search',
    description: 'Search the web',
    input_schema: {
      type: 'object',
      properties: {
        query: { type: 'string' }
      },
      required: ['query']
    }
  }]
});

console.log(response.content[0].text);

# Install Claude Code (requires Node.js 18+)
npm install -g @anthropic-ai/claude-code

# Connect to Shannon AI as backend
export ANTHROPIC_BASE_URL=https://api.shannon-ai.com
export ANTHROPIC_API_KEY=sk-YOUR_API_KEY

# Launch Claude Code in bare mode (no Anthropic account needed)
claude

# Or run a one-shot command
claude -p "Explain this codebase"

# Claude Code will use Shannon's Anthropic-compatible API
# for all AI operations: reading files, editing code,
# running tests, and multi-turn tool use.

# Alternative: set env vars permanently in your shell profile
# ~/.bashrc or ~/.zshrc

export ANTHROPIC_BASE_URL=https://api.shannon-ai.com
export ANTHROPIC_API_KEY=sk-YOUR_API_KEY

# Then just run:
claude

# Supported features through Shannon:
# - Multi-turn conversations with full context
# - File reading and editing (tool use)
# - Shell command execution
# - Streaming responses
# - All Claude Code slash commands (/compact, /clear, etc.)

# Install Codex CLI
npm install -g @openai/codex

# Connect to Shannon AI as backend
export OPENAI_BASE_URL=https://api.shannon-ai.com/v1
export OPENAI_API_KEY=sk-YOUR_API_KEY

# Launch Codex
codex

# Or run a one-shot command
codex "fix the bug in main.py"

# Codex uses the Responses API (POST /v1/responses)
# Shannon handles tool calls including:
# - Reading and writing files
# - Running shell commands
# - Multi-turn function calling

# Alternative: set env vars permanently
# ~/.bashrc or ~/.zshrc

export OPENAI_BASE_URL=https://api.shannon-ai.com/v1
export OPENAI_API_KEY=sk-YOUR_API_KEY

# Then just run:
codex

# Supported features through Shannon:
# - Responses API with full tool use
# - Function calling (file read/write, shell exec)
# - Streaming with real-time output
# - Multi-turn conversations
# - All Codex approval modes (suggest, auto-edit, full-auto)