API Server

Source Code: src/gaia/api/

Components: OpenAI-compatible API Server, Schemas, Endpoints, SSE Streaming Module: gaia.api Import: from gaia.api import app, schemas, agent_registry

Overview

The GAIA API Server provides an OpenAI-compatible REST API for exposing GAIA agents to external tools like VSCode, Claude Dev, and custom applications. It implements the OpenAI chat completions interface with streaming support, agent registration, and comprehensive debugging features. Key Features:

OpenAI-compatible endpoints (/v1/chat/completions, /v1/models)
Server-Sent Events (SSE) streaming
Dynamic agent registry
Workspace root extraction from GitHub Copilot
Debug modes (logging, prompts, step-through)
CORS support
Health checks
Token usage tracking

Requirements

Functional Requirements

Core Endpoints

POST /v1/chat/completions
- Non-streaming responses
- SSE streaming responses
- Message history support
- Tool calls support (future)
GET /v1/models
- List available agents as models
- Model metadata (tokens, description)
GET /health
- Health check endpoint
- Returns 200 OK

Agent Registry

Agent Registration
- Dynamic agent discovery
- Model ID mapping
- Agent instantiation
- Workspace configuration
Agent Management
- Singleton instances per agent type
- Silent mode operation
- Workspace root injection

Request Processing

Message Handling
- OpenAI message format
- System/user/assistant roles
- GitHub Copilot workspace extraction
- Prompt formatting
Response Generation
- Agent process_query() integration
- Token counting
- Usage statistics
- Unique response IDs

Streaming Support

SSE Streaming
- Chunk-based streaming
- Delta content format
- Finish reason reporting
- Proper SSE formatting (data: prefix)

Non-Functional Requirements

Performance
- Low latency response
- Efficient streaming
- Connection pooling
Reliability
- Error handling
- Connection recovery
- Graceful degradation
Debugging
- Request/response logging
- Prompt display
- Step-through mode
- Raw HTTP logging
Security
- CORS configuration
- Input validation
- Error sanitization

API Specification

File Locations

src/gaia/api/app.py              # CLI entry point
src/gaia/api/openai_server.py    # FastAPI server
src/gaia/api/schemas.py          # Pydantic models
src/gaia/api/agent_registry.py   # Agent management
src/gaia/api/sse_handler.py      # SSE streaming

Schemas (Pydantic Models)

from typing import Any, Dict, List, Literal, Optional
from pydantic import BaseModel, Field

class ChatMessage(BaseModel):
    """Chat message in OpenAI format."""
    role: Literal["system", "user", "assistant", "tool"]
    content: Optional[str] = None
    tool_calls: Optional[List[Dict[str, Any]]] = None
    tool_call_id: Optional[str] = None

class ChatCompletionRequest(BaseModel):
    """POST /v1/chat/completions request."""
    model: str = Field(..., description="Model ID (e.g., gaia-code, gaia-jira)")
    messages: List[ChatMessage]
    stream: bool = Field(default=False, description="Enable SSE streaming")
    temperature: Optional[float] = Field(default=0.7, ge=0, le=2)
    max_tokens: Optional[int] = Field(default=None, gt=0)
    top_p: Optional[float] = Field(default=1.0, ge=0, le=1)

class ChatCompletionResponseMessage(BaseModel):
    """Response message from chat completion."""
    role: Literal["assistant"]
    content: str
    tool_calls: Optional[List[Dict[str, Any]]] = None

class ChatCompletionChoice(BaseModel):
    """A single completion choice."""
    index: int
    message: ChatCompletionResponseMessage
    finish_reason: Literal["stop", "length"]

class UsageInfo(BaseModel):
    """Token usage information."""
    prompt_tokens: int
    completion_tokens: int
    total_tokens: int

class ChatCompletionResponse(BaseModel):
    """POST /v1/chat/completions response (non-streaming)."""
    id: str
    object: Literal["chat.completion"]
    created: int
    model: str
    choices: List[ChatCompletionChoice]
    usage: UsageInfo

class ModelInfo(BaseModel):
    """Model metadata for /v1/models endpoint."""
    id: str
    object: Literal["model"]
    created: int
    owned_by: str
    description: Optional[str] = None
    max_input_tokens: Optional[int] = None
    max_output_tokens: Optional[int] = None

class ModelListResponse(BaseModel):
    """GET /v1/models response."""
    object: Literal["list"]
    data: List[ModelInfo]

API Endpoints

from fastapi import FastAPI, HTTPException
from fastapi.responses import StreamingResponse
from fastapi.middleware.cors import CORSMiddleware

app = FastAPI(
    title="GAIA OpenAI-Compatible API",
    description="OpenAI-compatible API for GAIA agents",
    version="1.0.0",
)

# CORS middleware
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

@app.post("/v1/chat/completions")
async def create_chat_completion(request: ChatCompletionRequest):
    """
    Create chat completion (OpenAI-compatible endpoint).

    Supports both streaming (SSE) and non-streaming responses.

    Args:
        request: Chat completion request with model, messages, and options

    Returns:
        For non-streaming: ChatCompletionResponse
        For streaming: StreamingResponse with SSE chunks

    Raises:
        HTTPException 404: Model not found
        HTTPException 400: No user message in request

    Example Non-Streaming:
        POST /v1/chat/completions
        {
            "model": "gaia-code",
            "messages": [{"role": "user", "content": "Write hello world"}],
            "stream": false
        }

        Response:
        {
            "id": "chatcmpl-abc123",
            "object": "chat.completion",
            "created": 1234567890,
            "model": "gaia-code",
            "choices": [{
                "index": 0,
                "message": {
                    "role": "assistant",
                    "content": "Here's hello world:\\nprint('Hello, World!')"
                },
                "finish_reason": "stop"
            }],
            "usage": {
                "prompt_tokens": 10,
                "completion_tokens": 20,
                "total_tokens": 30
            }
        }

    Example Streaming:
        POST /v1/chat/completions
        {
            "model": "gaia-code",
            "messages": [{"role": "user", "content": "Write hello world"}],
            "stream": true
        }

        Response (SSE):
        data: {"id":"chatcmpl-abc","object":"chat.completion.chunk",...}
        data: {"id":"chatcmpl-abc","object":"chat.completion.chunk",...}
        data: [DONE]
    """
    pass

@app.get("/v1/models")
async def list_models() -> ModelListResponse:
    """
    List available models (GAIA agents).

    Returns:
        ModelListResponse with list of available agents as models

    Example:
        GET /v1/models

        Response:
        {
            "object": "list",
            "data": [
                {
                    "id": "gaia-code",
                    "object": "model",
                    "created": 1234567890,
                    "owned_by": "amd-gaia",
                    "description": "Autonomous Python coding agent",
                    "max_input_tokens": 32768,
                    "max_output_tokens": 8192
                },
                {
                    "id": "gaia-jira",
                    "object": "model",
                    "created": 1234567890,
                    "owned_by": "amd-gaia"
                }
            ]
        }
    """
    pass

@app.get("/health")
async def health_check():
    """
    Health check endpoint.

    Returns:
        {"status": "ok"}

    Example:
        GET /health

        Response:
        {"status": "ok"}
    """
    return {"status": "ok"}

Agent Registry

The API-server’s AgentRegistry is not a runtime plug-in registry — it is a hardcoded AGENT_MODELS dict in src/gaia/api/agent_registry.py plus a dynamic import helper. There is no register_agent() method today; to add a new model you edit AGENT_MODELS and restart the server.

# src/gaia/api/agent_registry.py (simplified)
from importlib import import_module
from typing import Any, Dict

AGENT_MODELS: Dict[str, Dict[str, Any]] = {
    "gaia-code": {
        "class_name": "gaia.agents.routing.agent.RoutingAgent",
        "init_params": {"api_mode": True, "silent_mode": True,
                        "streaming": False, "max_steps": 100},
        "description": "Intelligent routing agent ...",
    },
    # Add entries here to expose more models on /v1/models.
}


class AgentRegistry:
    """Resolves model IDs to agent instances by importing the target class
    on demand. `gaia api start --debug/--show-prompts/--streaming/--step-through`
    set GAIA_API_* env-vars that are merged into `init_params` at startup."""

    def get_agent(self, model_id: str):
        """Load and instantiate the agent for `model_id`.

        Raises `KeyError` if the model is not registered.
        A *fresh* agent is created per call — there is no singleton cache.
        """
        entry = AGENT_MODELS[model_id]
        module_path, _, class_name = entry["class_name"].rpartition(".")
        agent_class = getattr(import_module(module_path), class_name)
        return agent_class(**entry["init_params"])

    def list_models(self):
        """Return the models block used by `/v1/models`."""
        return [
            {
                "id": model_id,
                "object": "model",
                "owned_by": "amd-gaia",
                "description": cfg.get("description", ""),
            }
            for model_id, cfg in AGENT_MODELS.items()
        ]

For the richer, manifest-based agent registry used by the Agent UI (not the OpenAI-style API server), see the Agent Registry spec covering src/gaia/agents/registry.py, AgentManifest, and ~/.gaia/agents/ discovery.

### SSE Streaming

```python
from typing import AsyncGenerator
import json
import uuid
import time

async def stream_chat_completion(
    agent: Agent,
    user_message: str,
    model_id: str,
) -> AsyncGenerator[str, None]:
    """
    Stream chat completion using SSE format.

    Args:
        agent: Agent instance to use
        user_message: User's message
        model_id: Model identifier

    Yields:
        SSE-formatted chunks (data: {...})

    Example:
        async for chunk in stream_chat_completion(agent, "Hello", "gaia-code"):
            print(chunk)
        # Output:
        # data: {"id":"chatcmpl-123","object":"chat.completion.chunk",...}
        # data: {"id":"chatcmpl-123","object":"chat.completion.chunk",...}
        # data: [DONE]
    """
    chunk_id = f"chatcmpl-{uuid.uuid4().hex[:8]}"
    created = int(time.time())

    try:
        # Stream response from agent
        for delta_content in agent.stream_query(user_message):
            chunk = {
                "id": chunk_id,
                "object": "chat.completion.chunk",
                "created": created,
                "model": model_id,
                "choices": [
                    {
                        "index": 0,
                        "delta": {"content": delta_content},
                        "finish_reason": None,
                    }
                ],
            }
            yield f"data: {json.dumps(chunk)}\n\n"

        # Final chunk with finish_reason
        final_chunk = {
            "id": chunk_id,
            "object": "chat.completion.chunk",
            "created": created,
            "model": model_id,
            "choices": [
                {
                    "index": 0,
                    "delta": {},
                    "finish_reason": "stop",
                }
            ],
        }
        yield f"data: {json.dumps(final_chunk)}\n\n"
        yield "data: [DONE]\n\n"

    except Exception as e:
        error_chunk = {
            "error": {
                "message": str(e),
                "type": "server_error",
            }
        }
        yield f"data: {json.dumps(error_chunk)}\n\n"

Implementation Details

Workspace Root Extraction

def extract_workspace_root(messages):
    """
    Extract workspace root path from GitHub Copilot messages.

    GitHub Copilot includes workspace info in messages like:
    <workspace_info>
    I am working in a workspace with the following folders:
    - /Users/username/path/to/workspace
    </workspace_info>

    Args:
        messages: List of ChatMessage objects

    Returns:
        str: Workspace root path, or None if not found
    """
    import re

    for msg in messages:
        if msg.role == "user" and msg.content:
            workspace_match = re.search(
                r"<workspace_info>.*?following folders:\s*\n\s*-\s*([^\s\n]+)",
                msg.content,
                re.DOTALL,
            )
            if workspace_match:
                return workspace_match.group(1).strip()

    return None

Request Processing Flow

async def create_chat_completion(request: ChatCompletionRequest):
    # 1. Extract workspace root (carried in the last user message's metadata).
    workspace_root = extract_workspace_root(request.messages)

    # 2. Resolve the agent. `get_agent()` takes only model_id; workspace_root
    #    is applied afterwards (e.g., via agent.config or a per-request setter).
    try:
        agent = registry.get_agent(request.model)
    except KeyError:
        raise HTTPException(404, f"Model not found: {request.model}")
    if workspace_root and hasattr(agent, "set_workspace_root"):
        agent.set_workspace_root(workspace_root)

    # 3. Extract user message
    user_messages = [m for m in request.messages if m.role == "user"]
    if not user_messages:
        raise HTTPException(400, "No user message in request")
    user_message = user_messages[-1].content

    # 4. Generate response
    if request.stream:
        # Streaming response
        return StreamingResponse(
            stream_chat_completion(agent, user_message, request.model),
            media_type="text/event-stream",
        )
    else:
        # Non-streaming response
        response_text = agent.process_query(user_message)

        # Token counting
        if isinstance(agent, ApiAgent):
            prompt_tokens = agent.estimate_tokens(user_message)
            completion_tokens = agent.estimate_tokens(response_text)
        else:
            prompt_tokens = len(user_message) // 4
            completion_tokens = len(response_text) // 4

        return ChatCompletionResponse(
            id=f"chatcmpl-{uuid.uuid4().hex[:8]}",
            object="chat.completion",
            created=int(time.time()),
            model=request.model,
            choices=[
                ChatCompletionChoice(
                    index=0,
                    message=ChatCompletionResponseMessage(
                        role="assistant",
                        content=response_text,
                    ),
                    finish_reason="stop",
                )
            ],
            usage=UsageInfo(
                prompt_tokens=prompt_tokens,
                completion_tokens=completion_tokens,
                total_tokens=prompt_tokens + completion_tokens,
            ),
        )

Debug Middleware

@app.middleware("http")
async def log_raw_requests(request: Request, call_next):
    """
    Middleware to log raw HTTP requests when debug mode is enabled.
    """
    if os.environ.get("GAIA_API_DEBUG") == "1":
        logger.debug("=" * 80)
        logger.debug("RAW HTTP REQUEST")
        logger.debug("=" * 80)
        logger.debug(f"Path: {request.url.path}")
        logger.debug(f"Method: {request.method}")
        logger.debug("Headers:")
        for name, value in request.headers.items():
            logger.debug(f"  {name}: {value}")

        # Don't read body for streaming endpoints
        if request.url.path != "/v1/chat/completions":
            body_bytes = await request.body()
            logger.debug(f"Body: {body_bytes.decode('utf-8')}")

        logger.debug("=" * 80)

    response = await call_next(request)
    return response

Testing Requirements

Unit Tests

File: tests/api/test_api_server.py

import pytest
from fastapi.testclient import TestClient
from gaia.api.openai_server import app

client = TestClient(app)

def test_health_check():
    """Test health endpoint."""
    response = client.get("/health")
    assert response.status_code == 200
    assert response.json() == {"status": "ok"}

def test_list_models():
    """Test models listing."""
    response = client.get("/v1/models")
    assert response.status_code == 200
    data = response.json()
    assert data["object"] == "list"
    assert isinstance(data["data"], list)
    assert len(data["data"]) > 0

def test_chat_completion_non_streaming():
    """Test non-streaming chat completion."""
    response = client.post(
        "/v1/chat/completions",
        json={
            "model": "gaia-code",
            "messages": [{"role": "user", "content": "Say hello"}],
            "stream": False
        }
    )
    assert response.status_code == 200
    data = response.json()
    assert data["object"] == "chat.completion"
    assert len(data["choices"]) > 0
    assert data["choices"][0]["message"]["role"] == "assistant"
    assert isinstance(data["choices"][0]["message"]["content"], str)
    assert "usage" in data

def test_chat_completion_streaming():
    """Test streaming chat completion."""
    with client.stream(
        "POST",
        "/v1/chat/completions",
        json={
            "model": "gaia-code",
            "messages": [{"role": "user", "content": "Count to 3"}],
            "stream": True
        }
    ) as response:
        assert response.status_code == 200
        assert response.headers["content-type"] == "text/event-stream"

        chunks = []
        for line in response.iter_lines():
            if line.startswith("data: "):
                data = line[6:]  # Remove "data: " prefix
                if data != "[DONE]":
                    import json
                    chunk = json.loads(data)
                    chunks.append(chunk)

        assert len(chunks) > 0
        assert chunks[0]["object"] == "chat.completion.chunk"

def test_chat_completion_invalid_model():
    """Test chat completion with invalid model."""
    response = client.post(
        "/v1/chat/completions",
        json={
            "model": "nonexistent-model",
            "messages": [{"role": "user", "content": "Hello"}],
        }
    )
    assert response.status_code == 404

def test_chat_completion_no_user_message():
    """Test chat completion without user message."""
    response = client.post(
        "/v1/chat/completions",
        json={
            "model": "gaia-code",
            "messages": [{"role": "system", "content": "You are helpful"}],
        }
    )
    assert response.status_code == 400

Usage Examples

Example 1: Start API Server

# Foreground
gaia api start

# Background
gaia api start --background

# Custom host/port
gaia api start --host 0.0.0.0 --port 9090

# With debug
gaia api start --debug --show-prompts

# Stop server
gaia api stop

Example 2: Non-Streaming Request (curl)

curl -X POST http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gaia-code",
    "messages": [
      {"role": "user", "content": "Write a hello world program"}
    ],
    "stream": false
  }'

Example 3: Streaming Request (curl)

curl -X POST http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gaia-code",
    "messages": [
      {"role": "user", "content": "Write a hello world program"}
    ],
    "stream": true
  }'

Example 4: Python Client

import requests

# List models
response = requests.get("http://localhost:8080/v1/models")
models = response.json()["data"]
for model in models:
    print(f"{model['id']}: {model.get('description', 'No description')}")

# Non-streaming chat
response = requests.post(
    "http://localhost:8080/v1/chat/completions",
    json={
        "model": "gaia-code",
        "messages": [
            {"role": "user", "content": "Write hello world in Python"}
        ],
        "stream": False
    }
)
result = response.json()
print(result["choices"][0]["message"]["content"])

# Streaming chat
response = requests.post(
    "http://localhost:8080/v1/chat/completions",
    json={
        "model": "gaia-code",
        "messages": [
            {"role": "user", "content": "Count to 5"}
        ],
        "stream": True
    },
    stream=True
)

for line in response.iter_lines():
    if line:
        line = line.decode('utf-8')
        if line.startswith("data: "):
            data = line[6:]
            if data != "[DONE]":
                import json
                chunk = json.loads(data)
                content = chunk["choices"][0]["delta"].get("content", "")
                if content:
                    print(content, end="", flush=True)
print()

Example 5: OpenAI Python SDK

from openai import OpenAI

# Point to GAIA API server
client = OpenAI(
    base_url="http://localhost:8080/v1",
    api_key="none"  # Not required for GAIA
)

# Non-streaming
response = client.chat.completions.create(
    model="gaia-code",
    messages=[
        {"role": "user", "content": "Write hello world"}
    ]
)
print(response.choices[0].message.content)

# Streaming
stream = client.chat.completions.create(
    model="gaia-code",
    messages=[
        {"role": "user", "content": "Count to 5"}
    ],
    stream=True
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)
print()

CLI Interface

Commands

# app.py

def start_server(
    host: str = "localhost",
    port: int = 8080,
    background: bool = False,
    debug: bool = False,
    show_prompts: bool = False,
    streaming: bool = False,
    step_through: bool = False,
):
    """
    Start the API server.

    Args:
        host: Host to bind to (default: localhost)
        port: Port to bind to (default: 8080)
        background: Run in background if True
        debug: Enable debug logging
        show_prompts: Display prompts sent to LLM
        streaming: Enable real-time streaming of LLM responses
        step_through: Enable step-through debugging mode
    """
    pass

def stop_server(port: int = 8080):
    """
    Stop the API server by finding and killing processes on the port.

    Args:
        port: Port number to stop server on (default: 8080)
    """
    pass

def check_status():
    """Check if API server is running."""
    pass

API Server Technical Specification

Core Framework

SDKs

Infrastructure

Code Infrastructure

Tool Mixins

Packaging

Agents & Apps

Overview

Requirements

Functional Requirements

Core Endpoints

Agent Registry

Request Processing

Streaming Support

Non-Functional Requirements

API Specification

File Locations

Schemas (Pydantic Models)

API Endpoints

Agent Registry

Implementation Details

Workspace Root Extraction

Request Processing Flow

Debug Middleware

Testing Requirements

Unit Tests

Usage Examples

Example 1: Start API Server

Example 2: Non-Streaming Request (curl)

Example 3: Streaming Request (curl)

Example 4: Python Client

Example 5: OpenAI Python SDK

CLI Interface

Commands

Core Framework

SDKs

Infrastructure

Code Infrastructure

Tool Mixins

Packaging

Agents & Apps

​Overview

​Requirements

​Functional Requirements

​Core Endpoints

​Agent Registry

​Request Processing

​Streaming Support

​Non-Functional Requirements

​API Specification

​File Locations

​Schemas (Pydantic Models)

​API Endpoints

​Agent Registry

​Implementation Details

​Workspace Root Extraction

​Request Processing Flow

​Debug Middleware

​Testing Requirements

​Unit Tests

​Usage Examples

​Example 1: Start API Server

​Example 2: Non-Streaming Request (curl)

​Example 3: Streaming Request (curl)

​Example 4: Python Client

​Example 5: OpenAI Python SDK

​CLI Interface

​Commands

Overview

Requirements

Functional Requirements

Core Endpoints

Agent Registry

Request Processing

Streaming Support

Non-Functional Requirements

API Specification

File Locations

Schemas (Pydantic Models)

API Endpoints

Agent Registry

Implementation Details

Workspace Root Extraction

Request Processing Flow

Debug Middleware

Testing Requirements

Unit Tests

Usage Examples

Example 1: Start API Server

Example 2: Non-Streaming Request (curl)

Example 3: Streaming Request (curl)

Example 4: Python Client

Example 5: OpenAI Python SDK

CLI Interface

Commands