Adapter Factory Skill

This skill teaches how to integrate new AI models into the AI Counsel MCP server by creating adapters. There are two types of adapters:

1. CLI Adapters - For command-line AI tools (e.g.,

   claude

   ,

   droid

   ,

   codex

   )
2. HTTP Adapters - For HTTP API integrations (e.g., Ollama, LM Studio, OpenRouter)

When to Use This Skill

Use this skill when you need to:

• Add support for a new AI model or service to participate in deliberations
• Integrate a command-line AI tool (CLI adapter)
• Integrate an HTTP-based AI API (HTTP adapter)
• Understand the adapter pattern used in AI Counsel
• Debug or modify existing adapters

Architecture Overview

Base Classes

BaseCLIAdapter (

adapters/base.py

)

• Handles subprocess execution, timeout management, and error handling
• Provides

  invoke()

  method that manages the full CLI lifecycle
• Subclasses only implement

  parse_output()

  for tool-specific parsing
• Optional: Override

  _adjust_args_for_context()

  for deliberation vs. non-deliberation behavior
• Optional: Implement

  validate_prompt_length()

  for length limits

BaseHTTPAdapter (

adapters/base_http.py

)

• Handles HTTP requests, retry logic with exponential backoff, and timeout management
• Uses

  httpx

  for async HTTP and

  tenacity

  for retry logic
• Retries on 5xx/429/network errors, fails fast on 4xx client errors
• Subclasses implement

  build_request()

  and

  parse_response()

• Supports environment variable substitution for API keys

Factory Pattern

The

create_adapter()

function in

adapters/__init__.py

:

• Maintains registries for CLI and HTTP adapters
• Creates appropriate adapter instances from config
• Handles backward compatibility with legacy config formats

---

Creating a CLI Adapter

Follow these 6 steps to add a new command-line AI tool:

Step 1: Create Adapter File

Create

adapters/your_cli.py

:

"""Your CLI tool adapter."""
from adapters.base import BaseCLIAdapter


class YourCLIAdapter(BaseCLIAdapter):
    """Adapter for your-cli tool."""

    def parse_output(self, raw_output: str) -> str:
        """
        Parse your-cli output to extract model response.

        Args:
            raw_output: Raw stdout from CLI tool

        Returns:
            Parsed model response text
        """
        # Example: Strip headers and extract main response
        lines = raw_output.strip().split("\n")

        # Skip header lines (tool-specific logic)
        start_idx = 0
        for i, line in enumerate(lines):
            if line.strip() and not line.startswith("Loading"):
                start_idx = i
                break

        return "\n".join(lines[start_idx:]).strip()

Optional: Override

_adjust_args_for_context()

if your CLI needs different args for deliberation vs. regular use:

def _adjust_args_for_context(self, is_deliberation: bool) -> list[str]:
    """
    Adjust CLI arguments based on context.

    Args:
        is_deliberation: True if part of multi-model deliberation

    Returns:
        Adjusted argument list
    """
    args = self.args.copy()

    if is_deliberation:
        # Remove flags that interfere with deliberation
        if "--interactive" in args:
            args.remove("--interactive")
    else:
        # Add flags for regular Claude Code work
        if "--context" not in args:
            args.append("--context")

    return args

Optional: Implement

validate_prompt_length()

if your API has length limits:

MAX_PROMPT_CHARS = 100000  # Example: 100k char limit

def validate_prompt_length(self, prompt: str) -> bool:
    """
    Validate prompt length against API limits.

    Args:
        prompt: The full prompt to validate

    Returns:
        True if valid, False if too long
    """
    return len(prompt) <= self.MAX_PROMPT_CHARS

Step 2: Update Config

Add your CLI to

config.yaml

:

adapters:
  your_cli:
    type: cli
    command: "your-cli"
    args: ["--model", "{model}", "{prompt}"]
    timeout: 60  # Adjust based on your model's speed

Placeholder Variables:

• {model}

  - Replaced with model identifier

• {prompt}

  - Replaced with full prompt text (including context)

Timeout Guidelines:

• Fast models (GPT-3.5): 30-60s
• Reasoning models (Claude Sonnet 4.5, GPT-5): 180-300s
• Local models: Varies, test and adjust

Step 3: Register Adapter

Update

adapters/__init__.py

:

# Add import at top
from adapters.your_cli import YourCLIAdapter

# Add to cli_adapters dict in create_adapter()
cli_adapters: dict[str, Type[BaseCLIAdapter]] = {
    "claude": ClaudeAdapter,
    "codex": CodexAdapter,
    "droid": DroidAdapter,
    "gemini": GeminiAdapter,
    "llamacpp": LlamaCppAdapter,
    "your_cli": YourCLIAdapter,  # Add this line
}

# Add to __all__ export list
__all__ = [
    "BaseCLIAdapter",
    "BaseHTTPAdapter",
    "ClaudeAdapter",
    "CodexAdapter",
    "DroidAdapter",
    "GeminiAdapter",
    "LlamaCppAdapter",
    "YourCLIAdapter",  # Add this line
    # ... rest of exports
]

Step 4: Update Schema

Update

models/schema.py

:

# Find the Participant class and add your CLI to the Literal type
class Participant(BaseModel):
    cli: Literal[
        "claude",
        "codex",
        "droid",
        "gemini",
        "llamacpp",
        "your_cli"  # Add this
    ]
    model: str

Update the MCP tool description in

server.py

:

# Find RECOMMENDED_MODELS and add your models
RECOMMENDED_MODELS = {
    "claude": ["sonnet-4.5", "opus-4"],
    "codex": ["gpt-5-codex", "gpt-4o"],
    # ... other models
    "your_cli": ["your-model-1", "your-model-2"],  # Add this
}

Step 5: Add Recommended Models

Update

server.py::RECOMMENDED_MODELS

with suggested models for your CLI:

RECOMMENDED_MODELS = {
    # ... existing models
    "your_cli": [
        "your-fast-model",      # For quick responses
        "your-reasoning-model",  # For complex analysis
    ],
}

Step 6: Write Tests

Create unit tests in

tests/unit/test_adapters.py

:

import pytest
from adapters.your_cli import YourCLIAdapter


class TestYourCLIAdapter:
    def test_parse_output_basic(self):
        """Test basic output parsing."""
        adapter = YourCLIAdapter(
            command="your-cli",
            args=["--model", "{model}", "{prompt}"],
            timeout=60
        )

        raw_output = "Loading...\n\nActual response text here"
        result = adapter.parse_output(raw_output)

        assert result == "Actual response text here"
        assert "Loading" not in result

    def test_parse_output_multiline(self):
        """Test multiline response parsing."""
        adapter = YourCLIAdapter(
            command="your-cli",
            args=["--model", "{model}", "{prompt}"],
            timeout=60
        )

        raw_output = "Header\n\nLine 1\nLine 2\nLine 3"
        result = adapter.parse_output(raw_output)

        assert "Line 1" in result
        assert "Line 2" in result
        assert "Line 3" in result

Add integration tests in

tests/integration/

:

import pytest
from adapters.your_cli import YourCLIAdapter


@pytest.mark.integration
@pytest.mark.asyncio
async def test_your_cli_integration():
    """Test actual CLI invocation (requires your-cli installed)."""
    adapter = YourCLIAdapter(
        command="your-cli",
        args=["--model", "{model}", "{prompt}"],
        timeout=60
    )

    result = await adapter.invoke(
        prompt="What is 2+2?",
        model="your-default-model"
    )

    assert result
    assert len(result) > 0
    # Add assertions specific to your model's response format

---

Creating an HTTP Adapter

Follow these 6 steps to add a new HTTP API integration:

Step 1: Create Adapter File

Create

adapters/your_adapter.py

:

"""Your API adapter."""
from typing import Tuple
from adapters.base_http import BaseHTTPAdapter


class YourAdapter(BaseHTTPAdapter):
    """
    Adapter for Your AI API.

    API reference: https://docs.yourapi.com
    Default endpoint: https://api.yourservice.com

    Example:
        adapter = YourAdapter(
            base_url="https://api.yourservice.com",
            api_key="your-key",
            timeout=120
        )
        result = await adapter.invoke(prompt="Hello", model="your-model")
    """

    def build_request(
        self, model: str, prompt: str
    ) -> Tuple[str, dict[str, str], dict]:
        """
        Build API request components.

        Args:
            model: Model identifier (e.g., "your-model-v1")
            prompt: The prompt to send

        Returns:
            Tuple of (endpoint, headers, body):
            - endpoint: URL path (e.g., "/v1/chat/completions")
            - headers: Request headers dict
            - body: Request body dict (will be JSON-encoded)
        """
        endpoint = "/v1/chat/completions"

        headers = {
            "Content-Type": "application/json",
        }

        # Add authentication if API key provided
        if self.api_key:
            headers["Authorization"] = f"Bearer {self.api_key}"

        # Build request body (adapt to your API format)
        body = {
            "model": model,
            "messages": [
                {"role": "user", "content": prompt}
            ],
            "temperature": 0.7,
            "stream": False,
        }

        return (endpoint, headers, body)

    def parse_response(self, response_json: dict) -> str:
        """
        Parse API response to extract model output.

        Your API response format:
        {
          "id": "resp-123",
          "model": "your-model",
          "choices": [
            {
              "message": {
                "role": "assistant",
                "content": "The model's response text"
              }
            }
          ]
        }

        Args:
            response_json: Parsed JSON response from API

        Returns:
            Extracted model response text

        Raises:
            KeyError: If response format is unexpected
        """
        try:
            return response_json["choices"][0]["message"]["content"]
        except (KeyError, IndexError) as e:
            raise KeyError(
                f"Unexpected API response format. "
                f"Expected 'choices[0].message.content', "
                f"got keys: {list(response_json.keys())}"
            ) from e

Step 2: Update Config

Add your HTTP adapter to

config.yaml

:

adapters:
  your_adapter:
    type: http
    base_url: "https://api.yourservice.com"
    api_key: "${YOUR_API_KEY}"  # Environment variable substitution
    timeout: 120
    max_retries: 3

Configuration Options:

• base_url

  : Base URL for API (no trailing slash)

• api_key

  : API key (use

  ${ENV_VAR}

  for environment variables)

• timeout

  : Request timeout in seconds

• max_retries

  : Max retry attempts for 5xx/429/network errors (default: 3)

• headers

  : Optional default headers dict

Environment Variable Substitution:

• Pattern:

  ${VAR_NAME}

  in config
• Substituted at runtime from environment
• Secure: Keeps secrets out of config files

Step 3: Register Adapter

Update

adapters/__init__.py

:

# Add import at top
from adapters.your_adapter import YourAdapter

# Add to http_adapters dict in create_adapter()
http_adapters: dict[str, Type[BaseHTTPAdapter]] = {
    "ollama": OllamaAdapter,
    "lmstudio": LMStudioAdapter,
    "openrouter": OpenRouterAdapter,
    "your_adapter": YourAdapter,  # Add this line
}

# Add to __all__ export list
__all__ = [
    "BaseCLIAdapter",
    "BaseHTTPAdapter",
    # ... CLI adapters
    "OllamaAdapter",
    "LMStudioAdapter",
    "OpenRouterAdapter",
    "YourAdapter",  # Add this line
    "create_adapter",
]

Step 4: Set Environment Variables

If your adapter uses API keys:

# Add to your shell profile (~/.bashrc, ~/.zshrc, etc.)
export YOUR_API_KEY="your-actual-api-key-here"

# Or set temporarily for testing
export YOUR_API_KEY="test-key" && python server.py

Step 5: Write Tests

Create unit tests with VCR for HTTP response recording in

tests/unit/test_your_adapter.py

:

import pytest
import vcr
from adapters.your_adapter import YourAdapter


# Configure VCR for recording HTTP interactions
vcr_instance = vcr.VCR(
    cassette_library_dir="tests/fixtures/vcr_cassettes/your_adapter/",
    record_mode="once",  # Record once, then replay
    match_on=["method", "scheme", "host", "port", "path", "query"],
    filter_headers=["authorization"],  # Hide API keys in recordings
)


class TestYourAdapter:
    def test_build_request_basic(self):
        """Test request building without API key."""
        adapter = YourAdapter(
            base_url="https://api.example.com",
            timeout=60
        )

        endpoint, headers, body = adapter.build_request(
            model="test-model",
            prompt="Hello"
        )

        assert endpoint == "/v1/chat/completions"
        assert headers["Content-Type"] == "application/json"
        assert body["model"] == "test-model"
        assert body["messages"][0]["content"] == "Hello"

    def test_build_request_with_auth(self):
        """Test request building with API key."""
        adapter = YourAdapter(
            base_url="https://api.example.com",
            api_key="test-key",
            timeout=60
        )

        endpoint, headers, body = adapter.build_request(
            model="test-model",
            prompt="Hello"
        )

        assert "Authorization" in headers
        assert headers["Authorization"] == "Bearer test-key"

    def test_parse_response_success(self):
        """Test parsing successful response."""
        adapter = YourAdapter(
            base_url="https://api.example.com",
            timeout=60
        )

        response = {
            "id": "resp-123",
            "choices": [
                {
                    "message": {
                        "role": "assistant",
                        "content": "Hello! How can I help?"
                    }
                }
            ]
        }

        result = adapter.parse_response(response)
        assert result == "Hello! How can I help?"

    def test_parse_response_missing_field(self):
        """Test error handling for malformed response."""
        adapter = YourAdapter(
            base_url="https://api.example.com",
            timeout=60
        )

        response = {"id": "resp-123"}  # Missing choices

        with pytest.raises(KeyError) as exc_info:
            adapter.parse_response(response)

        assert "Unexpected API response format" in str(exc_info.value)

    @pytest.mark.asyncio
    @vcr_instance.use_cassette("invoke_basic.yaml")
    async def test_invoke_with_vcr(self):
        """Test full invoke() with VCR recording."""
        adapter = YourAdapter(
            base_url="https://api.example.com",
            api_key="test-key",
            timeout=60
        )

        result = await adapter.invoke(
            prompt="What is 2+2?",
            model="test-model"
        )

        assert result
        assert len(result) > 0

Optional: Add integration tests (requires running service):

@pytest.mark.integration
@pytest.mark.asyncio
async def test_your_adapter_live():
    """Test with live API (requires service running and API key)."""
    import os

    api_key = os.getenv("YOUR_API_KEY")
    if not api_key:
        pytest.skip("YOUR_API_KEY not set")

    adapter = YourAdapter(
        base_url="https://api.yourservice.com",
        api_key=api_key,
        timeout=120
    )

    result = await adapter.invoke(
        prompt="What is the capital of France?",
        model="your-model"
    )

    assert "Paris" in result or "paris" in result

Step 6: Test with Deliberation

Create a simple test script to verify integration:

"""Test your adapter in a deliberation context."""
import asyncio
from adapters.your_adapter import YourAdapter


async def test():
    adapter = YourAdapter(
        base_url="https://api.yourservice.com",
        api_key="your-key",
        timeout=120
    )

    # Test basic invocation
    result = await adapter.invoke(
        prompt="What is 2+2?",
        model="your-model"
    )
    print(f"Response: {result}")

    # Test with context (simulating Round 2+ in deliberation)
    result_with_context = await adapter.invoke(
        prompt="Do you agree with this answer?",
        model="your-model",
        context="Previous participant said: 2+2 equals 4"
    )
    print(f"Response with context: {result_with_context}")


if __name__ == "__main__":
    asyncio.run(test())

---

Key Design Principles

DRY (Don't Repeat Yourself)

• Common logic in base classes (

  BaseCLIAdapter

  ,

  BaseHTTPAdapter

  )
• Tool-specific logic in concrete adapters
• Only implement what's unique to your adapter

YAGNI (You Aren't Gonna Need It)

• Build only what's needed for basic integration
• Don't add features until they're required
• Start simple, extend as needed

TDD (Test-Driven Development)

• Write tests first (red)
• Implement adapter (green)
• Refactor for clarity (refactor)

Type Safety

• Use type hints throughout
• Pydantic validation where applicable
• Let mypy catch errors early

Error Isolation

• Adapter failures don't halt deliberations
• Other participants continue if one fails
• Graceful degradation with informative errors

---

Common Patterns

CLI Adapter with Custom Parsing

def parse_output(self, raw_output: str) -> str:
    """Parse CLI output with multiple header formats."""
    lines = raw_output.strip().split("\n")

    # Skip all header lines until we find content
    content_started = False
    result_lines = []

    for line in lines:
        # Detect header patterns
        if any(marker in line.lower() for marker in ["loading", "initializing", "version"]):
            continue

        # Detect content start
        if line.strip():
            content_started = True

        if content_started:
            result_lines.append(line)

    return "\n".join(result_lines).strip()

HTTP Adapter with Streaming Support

def build_request(self, model: str, prompt: str) -> Tuple[str, dict, dict]:
    """Build request with optional streaming."""
    # Note: BaseHTTPAdapter doesn't support streaming yet
    # This is for future extension

    body = {
        "model": model,
        "prompt": prompt,
        "stream": False,  # Keep False for now
    }

    return ("/api/generate", {"Content-Type": "application/json"}, body)

Environment Variable Validation

def __init__(self, base_url: str, api_key: str = None, **kwargs):
    """Initialize with API key validation."""
    if not api_key:
        raise ValueError(
            "API key required. Set YOUR_API_KEY environment variable or "
            "provide api_key parameter."
        )

    super().__init__(base_url=base_url, api_key=api_key, **kwargs)

---

Testing Guidelines

Unit Tests (Required)

• Test

  parse_output()

  with various input formats
• Test

  build_request()

  with different parameters
• Test

  parse_response()

  with success and error cases
• Mock external dependencies (no actual API calls)
• Fast execution (< 1s total)

Integration Tests (Optional)

• Test actual CLI/API invocation
• Requires tool installed or service running
• Mark with

  @pytest.mark.integration

• May be slow, use sparingly

VCR for HTTP Tests

• Record real HTTP interactions once
• Replay from cassettes in CI/CD
• Filter sensitive data (API keys, auth tokens)
• Store in

  tests/fixtures/vcr_cassettes/your_adapter/

E2E Tests (Optional)

• Full deliberation with your adapter
• Mark with

  @pytest.mark.e2e

• Very slow, expensive (real API calls)
• Use for final validation only

---

Troubleshooting

CLI Adapter Issues

Problem: Timeout errors

• Solution: Increase timeout in config.yaml
• Reasoning models need 180-300s, not 60s

Problem: Output parsing fails

• Solution: Print

  raw_output

  and examine format
• Each CLI has unique output format, adjust parsing logic

Problem: Hook interference (Claude CLI)

• Solution: Add

  --settings '{"disableAllHooks": true}'

  to args
• User hooks can interfere with deliberation invocations

HTTP Adapter Issues

Problem: Connection refused

• Solution: Verify base_url and service is running
• Check with

  curl $BASE_URL/health

  or similar

Problem: 401 Authentication errors

• Solution: Verify API key is set:

  echo $YOUR_API_KEY

• Check environment variable substitution in config

Problem: 400 Bad Request errors

• Solution: Log request body, check API documentation
• Common issue: Wrong field names or missing required fields

Problem: Retries exhausted

• Solution: Check if service is healthy
• 5xx errors trigger retries, 4xx do not (by design)

General Issues

Problem: Adapter not found

• Solution: Verify registration in

  adapters/__init__.py

• Check both import and dict addition

Problem: Schema validation errors

• Solution: Add CLI name to

  Participant.cli

  Literal in

  models/schema.py

• MCP won't accept unlisted CLI names

---

Reference Files

Essential Files

• adapters/base.py

  - CLI adapter base class

• adapters/base_http.py

  - HTTP adapter base class

• adapters/__init__.py

  - Adapter factory and registry

• models/config.py

  - Configuration schema

• models/schema.py

  - Data models and validation

Example Adapters

• adapters/claude.py

  - CLI adapter with context-aware args

• adapters/gemini.py

  - CLI adapter with length validation

• adapters/ollama.py

  - HTTP adapter for local API

• adapters/lmstudio.py

  - HTTP adapter with OpenAI-compatible format

Test Examples

• tests/unit/test_adapters.py

  - CLI adapter unit tests

• tests/unit/test_ollama.py

  - HTTP adapter unit tests with VCR

• tests/integration/test_cli_adapters.py

  - CLI integration tests

---

Next Steps After Creating Adapter

1. Update CLAUDE.md if you added new patterns or gotchas
2. Add to RECOMMENDED_MODELS in

   server.py

   with usage guidance
3. Document API quirks in adapter docstrings for future maintainers
4. Test in real deliberation with 2-3 participants
5. Monitor transcript for response quality and voting behavior
6. Share findings if you discover best practices for your model

---

Quick Reference

CLI Adapter Checklist

• Create

  adapters/your_cli.py

  with

  parse_output()

• Add to

  config.yaml

  with command, args, timeout
• Register in

  adapters/__init__.py

  (import + dict + export)
• Add to

  Participant.cli

  Literal in

  models/schema.py

• Add to

  RECOMMENDED_MODELS

  in

  server.py

• Write unit tests for

  parse_output()

• Optional: Write integration test with real CLI

HTTP Adapter Checklist

• Create

  adapters/your_adapter.py

  with

  build_request()

  and

  parse_response()

• Add to

  config.yaml

  with base_url, api_key, timeout
• Register in

  adapters/__init__.py

  (import + dict + export)
• Set environment variables for API keys
• Write unit tests with VCR cassettes
• Test with simple script before full deliberation

Common Commands

# Run unit tests for new adapter
pytest tests/unit/test_your_adapter.py -v

# Run with coverage
pytest tests/unit/test_your_adapter.py --cov=adapters.your_adapter

# Format and lint
black adapters/your_adapter.py && ruff check adapters/your_adapter.py

# Test integration (requires tool/service)
pytest tests/integration/ -v -m integration

# Record VCR cassette (first run, then commit)
pytest tests/unit/test_your_adapter.py -v

---

Additional Resources

• CLAUDE.md - Full project documentation with architecture details
• MCP Protocol - https://modelcontextprotocol.io/introduction
• Pydantic Docs - https://docs.pydantic.dev/latest/
• httpx Docs - https://www.python-httpx.org/
• VCR.py Docs - https://vcrpy.readthedocs.io/

---

This skill encodes the institutional knowledge for extending AI Counsel with new model integrations. Follow the patterns, write tests, and maintain backward compatibility.