AI Coding Assistants Integration¶

Connect your favorite AI coding assistants to AWS Bedrock models through stdapi.ai. Get intelligent code completions, chat assistance, and codebase understanding with powerful AWS models like Claude, Kimi thinking, and Qwen Coder Next—no vendor lock-in required.

About AI Coding Assistants¶

AI coding assistants are IDE extensions and terminal tools that leverage large language models to enhance developer productivity. These tools provide real-time code completions, intelligent suggestions, natural language code generation, and interactive chat capabilities directly within your coding environment—acting as AI pair programmers that understand your codebase context.

What AI coding assistants can do:

• Real-time completions - Autocomplete code as you type with context awareness
• Interactive chat - Ask questions about your codebase, get explanations
• Code generation - Natural language to code conversion
• Refactoring - Intelligent code improvements and optimization suggestions
• Documentation - Auto-generate comments, docstrings, and READMEs
• Testing - Create unit tests, debug issues, suggest fixes
• Git integration - Generate commit messages, review diffs
• Multi-language - Support for Python, JavaScript, TypeScript, Go, Rust, Java, and more

Why AI Coding Assistants + stdapi.ai?¶

%%{init: {'flowchart': {'htmlLabels': true}} }%%
flowchart LR
  ide["<img src='../styles/logo_vscode.svg' style='height:64px;width:auto;vertical-align:middle;' /> IDE + AI Assistant"] --> stdapi["<img src='../styles/logo.svg' style='height:64px;width:auto;vertical-align:middle;' /> stdapi.ai"]
  stdapi --> bedrock["<img src='../styles/logo_amazon_bedrock.svg' style='height:64px;width:auto;vertical-align:middle;' /> AWS Bedrock"]

Prerequisites¶

OpenAI-Compatible Coding Assistants¶

Popular Tools: Cline | JetBrains AI Assistant | Continue.dev | Cursor | Windsurf

Most IDE coding assistants use the OpenAI-compatible API. Configure them by pointing to stdapi.ai's /v1 endpoint.

Configuration¶

Most AI coding assistants follow a similar configuration pattern. The exact menu location and field names may vary, but the core settings remain consistent.

Chat Completions¶

All coding assistants use chat completions for interactive conversations, code generation, and explanations.

Tool Calling Support¶

stdapi.ai fully supports tool calling (function calling) through the chat completions API, which is essential for autonomous and efficient coding agents.

Code Completions¶

Some coding assistants support dedicated code completion endpoints for real-time suggestions as you type.

Anthropic-Compatible Coding Assistants¶

Popular Tools: Claude Code | Aider | JetBrains AI Assistant (With Claude code ACP)

Tools that use the Anthropic messages API natively can be connected to stdapi.ai's /anthropic endpoint, enabling them to use Claude models via AWS Bedrock.

Claude Code¶

Claude Code is Anthropic's agentic coding tool that runs in the terminal.

Configuration¶

Create or edit ~/.claude/settings.json:

{
  "env": {
    "ANTHROPIC_AUTH_TOKEN": "YOUR_API_KEY",
    "ANTHROPIC_BASE_URL": "https://YOUR_STDAPI_URL/anthropic",
    "ANTHROPIC_DEFAULT_OPUS_MODEL": "anthropic.claude-opus-4-7",
    "ANTHROPIC_DEFAULT_SONNET_MODEL": "anthropic.claude-sonnet-4-6",
    "ANTHROPIC_DEFAULT_HAIKU_MODEL": "anthropic.claude-haiku-4-5-20251001-v1:0"
  }
}

• Replace YOUR_STDAPI_URL with your stdapi.ai deployment URL (e.g., https://api.example.com or http://localhost:8000 for local)
• Replace YOUR_API_KEY with your stdapi.ai API key
• The /anthropic path prefix is configured via the ANTHROPIC_ROUTES_PREFIX setting (default: /anthropic)
• The ANTHROPIC_DEFAULT_*_MODEL variables pin each model tier to a specific Bedrock model ID — recommended for production stability. Without them, Claude Code resolves aliases (opus, sonnet, haiku) which may change when Anthropic releases new versions. stdapi.ai also accepts the short alias names (e.g. claude-sonnet-4-6) as a convenience.

Effort-Based Reasoning¶

Claude Code supports effort levels that control how much reasoning the model applies — lower effort is faster and cheaper; higher effort provides deeper thinking for complex tasks.

Supported models via stdapi.ai:

Setting effort level:

# Per session at launch
claude --model sonnet --effort high

# Persist across sessions (env var takes precedence over all other settings)
export CLAUDE_CODE_EFFORT_LEVEL=high

# Or add to claude.json

{
  "effortLevel": "medium"
}

During a session, use /effort low, /effort medium, /effort high, or /effort max to change levels on the fly.

Declaring Model Capabilities¶

When you pin a non-Claude Bedrock model ID, Claude Code may not recognize it and will silently disable effort and thinking features. Use ANTHROPIC_DEFAULT_*_MODEL_SUPPORTED_CAPABILITIES to declare what the model actually supports:

Example — Nova 2 with effort enabled:

{
  "env": {
    "ANTHROPIC_AUTH_TOKEN": "YOUR_API_KEY",
    "ANTHROPIC_BASE_URL": "https://YOUR_STDAPI_URL/anthropic",
    "ANTHROPIC_DEFAULT_SONNET_MODEL": "amazon.nova-2-lite-v1:0",
    "ANTHROPIC_DEFAULT_SONNET_MODEL_NAME": "Nova 2 Lite",
    "ANTHROPIC_DEFAULT_SONNET_MODEL_SUPPORTED_CAPABILITIES": "effort",
    "DISABLE_PROMPT_CACHING": "1"
  }
}

Example — Claude with full capabilities declared (e.g. for a Bedrock ARN or inference profile):

{
  "env": {
    "ANTHROPIC_DEFAULT_OPUS_MODEL": "arn:aws:bedrock:us-east-1:123456789012:application-inference-profile/my-opus",
    "ANTHROPIC_DEFAULT_OPUS_MODEL_NAME": "Opus via Bedrock",
    "ANTHROPIC_DEFAULT_OPUS_MODEL_SUPPORTED_CAPABILITIES": "effort,max_effort,thinking,adaptive_thinking,interleaved_thinking"
  }
}

Using Non-Claude Models¶

Claude Code is optimized for Claude models and enables reasoning by default. When routing non-Claude models through stdapi.ai, incompatible reasoning parameters are silently ignored — no special configuration is needed to avoid API errors.

Models with effort support (Nova 2, DeepSeek V3) — declare effort capability:

{
  "env": {
    "ANTHROPIC_DEFAULT_SONNET_MODEL": "amazon.nova-2-lite-v1:0",
    "ANTHROPIC_DEFAULT_SONNET_MODEL_SUPPORTED_CAPABILITIES": "effort"
  }
}

Common configuration issues with non-Claude models:

• Prompt caching — Claude Code sends cache_control headers that can cause errors on models that handle caching differently. Set DISABLE_PROMPT_CACHING=1 to suppress them.
• Output token limit — Claude Code defaults to requesting up to 32 000 output tokens, which exceeds the maximum for many non-Claude models. Set CLAUDE_CODE_MAX_OUTPUT_TOKENS to a value within the model's limit to avoid max_tokens validation errors.

{
  "env": {
    "ANTHROPIC_DEFAULT_SONNET_MODEL": "moonshot.kimi-k2-instruct",
    "DISABLE_PROMPT_CACHING": "1",
    "CLAUDE_CODE_MAX_OUTPUT_TOKENS": "128000"
  }
}

Adding a Model to the Picker¶

Use ANTHROPIC_CUSTOM_MODEL_OPTION to add a single custom entry to the /model picker without replacing the built-in aliases. Useful for testing a specific Bedrock model ID alongside the standard Claude tiers:

{
  "env": {
    "ANTHROPIC_CUSTOM_MODEL_OPTION": "moonshot.kimi-k2-instruct",
    "ANTHROPIC_CUSTOM_MODEL_OPTION_NAME": "Kimi K2",
    "ANTHROPIC_CUSTOM_MODEL_OPTION_DESCRIPTION": "Moonshot Kimi K2 via stdapi.ai"
  }
}

Claude Code skips validation for this model ID, so any Bedrock model ID accepted by stdapi.ai works here.

Other Anthropic-Compatible Tools¶

Any tool using the Anthropic SDK or messages API can be configured the same way—set the ANTHROPIC_BASE_URL to https://YOUR_STDAPI_URL/anthropic and ANTHROPIC_API_KEY (or equivalent) to your stdapi.ai API key.

MCP (Model Context Protocol)¶

stdapi.ai can act as an MCP server, exposing its API endpoints as tools that MCP-capable clients call directly using the Model Context Protocol. Enable MCP on your stdapi.ai deployment by setting the appropriate environment variable:

Configuration¶

Many MCP clients—including Claude Code and Cursor—configure servers via a mcpServers JSON block:

{
  "mcpServers": {
    "stdapi": {
      "type": "http",
      "url": "https://YOUR_STDAPI_URL/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_API_KEY"
      }
    }
  }
}

• Replace YOUR_STDAPI_URL with your stdapi.ai deployment URL (e.g., https://api.example.com or http://localhost:8000 for local)
• The Authorization header (and YOUR_API_KEY) is only required if your deployment uses API key authentication; omit the headers block for local development without a key
• For Claude Code, add this to ~/.claude.json

Tool Selection¶

By default, all tools are exposed. Restrict the tool set for better performance — LLMs work better with fewer choices, and many providers cap active tools per session.

openai_chat_completion and anthropic_message reach the same Bedrock models through different protocol adapters — pick one based on which API your client uses. The examples below use the OpenAI tools; replace with anthropic_message,search_models,anthropic_file,anthropic_file_list,anthropic_files_get,anthropic_file_content if you prefer the Anthropic protocol.

Always include search_models in your tool set — it lets the agent discover available model IDs dynamically rather than relying on hardcoded values. Use it instead of openai_model_list or anthropic_model_list: it returns richer metadata and supports capability-based filtering (by modality, route, region, and more).

Coding agent (text and code only) — chat, model discovery, and file operations; no image/audio, no destructive tools:

export MCP_INCLUDE_TOOLS="openai_chat_completion,search_models,openai_embedding,openai_file,openai_file_list,openai_files_get,openai_file_content"

Coding agent with image support — same baseline plus image generation and editing:

export MCP_INCLUDE_TOOLS="openai_chat_completion,search_models,openai_embedding,openai_file,openai_file_list,openai_files_get,openai_file_content,openai_image_generation,openai_image_edit,openai_image_variation"

Coding agent with audio support — same baseline plus transcription, translation, and speech synthesis:

export MCP_INCLUDE_TOOLS="openai_chat_completion,search_models,openai_embedding,openai_file,openai_file_list,openai_files_get,openai_file_content,openai_audio_transcription,openai_audio_translation,openai_audio_speech"

In all cases, file deletion tools (openai_files_delete, anthropic_files_delete) are intentionally omitted — add them only when your workflow explicitly requires cleanup.

See Configuration Reference → MCP for the full tool list and selection guidance.

Running stdapi.ai Locally¶

stdapi.ai works well when running locally with Docker, making it ideal for your development environment.

API Base URL: http://localhost:8000/v1
API Key: your_stdapi_key

ANTHROPIC_BASE_URL: http://localhost:8000/anthropic
ANTHROPIC_AUTH_TOKEN: your_stdapi_key

Next Steps¶