# ModelMux Implementation Plan
## Phased Roadmap with Actionable Steps
> **Strategy**: Focus on Vertex AI + Responses API excellence, add vLLM support for company needs, keep architecture open for future contributors.
---
## Phase 1: Foundation & Quick Wins (2-3 weeks)
**Goal**: Improve core quality, add vLLM support (for your company), ship Docker
### Week 1: Core Quality & Testing
#### Step 1.1: Add Comprehensive Tests (3-4 days)
- [ ] Create `tests/integration/` directory
- [ ] Add integration test for Vertex AI chat completions
- [ ] Non-streaming request/response
- [ ] Streaming request/response
- [ ] Tool calling request/response
- [ ] Add integration test for health endpoint
- [ ] Add unit tests for converters
- [ ] `OpenAI → Anthropic` message conversion
- [ ] `Anthropic → OpenAI` message conversion
- [ ] Tool/function conversion both ways
- [ ] Add error case tests
- [ ] Invalid API keys
- [ ] Malformed requests
- [ ] Rate limiting scenarios
- [ ] Create test fixtures directory `tests/fixtures/`
- [ ] Sample OpenAI requests
- [ ] Sample Anthropic responses
- [ ] Sample tool definitions
- [ ] Document how to run tests in README
```bash
cargo test
cargo test --features integration-tests
```
#### Step 1.2: Improve Error Handling (2-3 days)
- [ ] Audit all error types in `src/error.rs`
- [ ] Add specific error variants for:
- [ ] `ConfigurationError` (missing fields, invalid values)
- [ ] `AuthenticationError` (GCP auth issues)
- [ ] `ProviderError` (upstream API errors)
- [ ] `ConversionError` (format translation issues)
- [ ] Improve error messages with actionable suggestions
```rust
ConfigurationError::MissingProject =>
"Missing VERTEX_PROJECT. Run: modelmux config init"
```
- [ ] Add error context preservation
```rust
.context("Failed to convert OpenAI request to Anthropic format")?
```
- [ ] Add request ID tracking for debugging
- [ ] Log errors with structured data (JSON format)
#### Step 1.3: Documentation Pass (1-2 days)
- [ ] Create `docs/API_COMPATIBILITY.md`
- [ ] List supported OpenAI endpoints
- [ ] List supported features (streaming, tools, etc.)
- [ ] List known limitations
- [ ] List model mappings
- [ ] Create `docs/TROUBLESHOOTING.md`
- [ ] Common errors and solutions
- [ ] Debug mode instructions
- [ ] Log interpretation guide
- [ ] Update README with examples
- [ ] cURL examples for common use cases
- [ ] SDK examples (Python, TypeScript)
- [ ] Environment variable reference
### Week 2: vLLM Support (Company Priority)
#### Step 2.1: Implement OpenAI-Compatible Provider (3-4 days)
- [ ] Create `src/provider/openai_compatible.rs`
```rust
pub struct OpenAiCompatibleProvider {
client: reqwest::Client,
base_url: String,
api_key: String,
model: String,
}
```
- [ ] Implement `LlmProvider` trait
- [ ] `chat_completions()` method
- [ ] `chat_completions_stream()` method
- [ ] Error handling with proper conversion
- [ ] Add authentication
- [ ] Bearer token in Authorization header
- [ ] Support API key in custom header (some vLLM setups)
- [ ] Add request forwarding
- [ ] Pass OpenAI format directly (no conversion needed!)
- [ ] Handle streaming responses
- [ ] Preserve headers and error codes
- [ ] Add timeout configuration
- [ ] Add retry logic (reuse existing code)
#### Step 2.2: Add Provider Configuration (1-2 days)
- [ ] Extend `config.toml` schema
```toml
[provider]
type = "vertex" # or "openai_compatible"
# For OpenAI-compatible providers (vLLM, etc.)
[provider.openai_compatible]
base_url = "http://localhost:8000/v1"
api_key = "${VLLM_API_KEY}" # or "none" for local
model = "meta-llama/Llama-3-8B-Instruct"
timeout_seconds = 120
```
- [ ] Update `src/config.rs` to parse provider config
- [ ] Add provider factory pattern
```rust
pub enum ProviderType {
Vertex,
OpenAiCompatible,
}
pub fn create_provider(config: &Config) -> Result<Box<dyn LlmProvider>> {
match config.provider.type {
ProviderType::Vertex => Ok(Box::new(VertexProvider::new(config)?)),
ProviderType::OpenAiCompatible => Ok(Box::new(OpenAiCompatibleProvider::new(config)?)),
}
}
```
- [ ] Add validation for provider-specific config
- [ ] Update `modelmux config init` to ask about provider type
#### Step 2.3: Test vLLM Integration (1-2 days)
- [ ] Create test script for local vLLM
```bash
python -m vllm.entrypoints.openai.api_server \
--model meta-llama/Llama-3-8B-Instruct \
--port 8000
modelmux config init --preset vllm
curl http://localhost:3000/v1/chat/completions ...
```
- [ ] Document vLLM setup in `docs/VLLM_SETUP.md`
- [ ] Add example config for gpt-oss-120b
- [ ] Test failover scenario (Vertex → vLLM)
- [ ] Document known vLLM quirks/limitations
### Week 3: Docker & Deployment
#### Step 3.1: Docker Image (2-3 days)
- [ ] Create multi-stage `Dockerfile`
```dockerfile
# Builder stage
FROM rust:1.84-slim as builder
WORKDIR /app
COPY . .
RUN cargo build --release
# Runtime stage
FROM debian:bookworm-slim
RUN apt-get update && apt-get install -y ca-certificates && rm -rf /var/lib/apt/lists/*
COPY --from=builder /app/target/release/modelmux /usr/local/bin/
EXPOSE 3000
CMD ["modelmux"]
```
- [ ] Test multi-arch builds (amd64, arm64)
```bash
docker buildx build --platform linux/amd64,linux/arm64 -t modelmux:latest .
```
- [ ] Create `.dockerignore`
- [ ] Optimize image size (use Alpine? scratch?)
- [ ] Add health check
```dockerfile
HEALTHCHECK --interval=30s --timeout=3s \
CMD curl -f http://localhost:3000/health || exit 1
```
#### Step 3.2: Docker Compose Examples (1 day)
- [ ] Create `docker-compose.yml` for Vertex AI
```yaml
services:
modelmux:
image: modelmux:latest
ports:
- "3000:3000"
volumes:
- ./config:/config
- ./service-account.json:/secrets/sa.json
environment:
- MODELMUX_CONFIG_FILE=/config/config.toml
```
- [ ] Create `docker-compose.vllm.yml` for vLLM + ModelMux
```yaml
services:
vllm:
image: vllm/vllm-openai:latest
ports:
- "8000:8000"
command: --model meta-llama/Llama-3-8B-Instruct
modelmux:
image: modelmux:latest
ports:
- "3000:3000"
environment:
- PROVIDER_TYPE=openai_compatible
- OPENAI_BASE_URL=http://vllm:8000/v1
```
- [ ] Document Docker deployment in README
- [ ] Add Kubernetes example (optional)
#### Step 3.3: Binary Releases (1 day)
- [ ] Set up GitHub Actions for releases
- [ ] Build for multiple targets
- [ ] `x86_64-unknown-linux-gnu`
- [ ] `aarch64-unknown-linux-gnu`
- [ ] `x86_64-apple-darwin`
- [ ] `aarch64-apple-darwin`
- [ ] Create release workflow
```yaml
# .github/workflows/release.yml
on:
push:
tags:
- 'v*'
jobs:
build:
# ... build for each platform
# ... create GitHub release
# ... upload artifacts
```
- [ ] Update Homebrew formula automatically
---
## Phase 2: Responses API Foundation (3-4 weeks)
**Goal**: Research, design, and implement core Responses API support
### Week 4: Research & Design
#### Step 4.1: Responses API Deep Dive (2-3 days)
- [ ] Study OpenAI Responses API documentation
- [ ] Request format (`input`, `instructions`, `modalities`)
- [ ] Response format (`output`, `usage`, `metadata`)
- [ ] State management (`previous_response_id`, `store`)
- [ ] Built-in tools (`web_search`, `file_search`, etc.)
- [ ] Structured outputs (`text.format`)
- [ ] Compare with Chat Completions API
- [ ] Create mapping table (see below)
- [ ] Identify translation challenges
- [ ] Document unsupported features
- [ ] Study vLLM's Responses API implementation
- [ ] What they support
- [ ] What they don't support
- [ ] Create `docs/RESPONSES_API_DESIGN.md`
#### Step 4.2: Design Translation Layer (2-3 days)
- [ ] Design data structures
```rust
pub struct ResponsesRequest {
pub input: InputContent,
pub instructions: Option<String>,
pub previous_response_id: Option<String>,
pub modalities: Vec<String>,
pub tools: Vec<Tool>,
pub store: Option<bool>,
}
pub struct ResponsesResponse {
pub id: String,
pub output: Vec<OutputItem>,
pub usage: Usage,
pub metadata: Metadata,
}
```
- [ ] Design conversion strategy
```
Responses Request → Chat Completions Request:
- input (string/items) → messages (with system role for instructions)
- previous_response_id → inject previous messages into context
- modalities → infer from content types
- tools → convert format
Chat Completions Response → Responses Response:
- choices[0].message → output items
- Handle tool calls → output items with type="function_call"
- Generate response ID
- Metadata transformation
```
- [ ] Identify edge cases
- [ ] Multimodal input handling
- [ ] State persistence (where to store `previous_response_id` data?)
- [ ] Tool result correlation
- [ ] Decide on state storage approach
- [ ] Option 1: In-memory cache (simple, loses state on restart)
- [ ] Option 2: Redis (persistent, scales)
- [ ] Option 3: File-based (simple persistence)
- [ ] **Recommendation**: Start with in-memory, add Redis later
#### Step 4.3: API Compatibility Matrix (1 day)
- [ ] Create comprehensive comparison document
- [ ] Feature-by-feature breakdown
- [ ] What works with Vertex
- [ ] What works with vLLM
- [ ] What requires translation
- [ ] What's not possible
- [ ] Document in `docs/RESPONSES_API_SUPPORT.md`
### Week 5-6: Core Implementation
#### Step 5.1: Data Types & Parsing (3-4 days)
- [ ] Create `src/types/responses_api.rs`
- [ ] Implement all Responses API types
```rust
pub struct ResponsesRequest { }
pub struct ResponsesResponse { }
pub struct InputContent { }
pub struct OutputItem { }
pub struct Metadata { }
```
- [ ] Add Serde serialization/deserialization
- [ ] Add validation
```rust
impl ResponsesRequest {
pub fn validate(&self) -> Result<()> {
}
}
```
- [ ] Add tests for parsing
#### Step 5.2: Request Converter (3-4 days)
- [ ] Create `src/converter/responses_to_chat.rs`
```rust
pub struct ResponsesToChatConverter;
impl ResponsesToChatConverter {
pub fn convert_request(
req: ResponsesRequest,
context: Option<ConversionContext>,
) -> Result<ChatCompletionRequest> {
}
}
```
- [ ] Implement input → messages conversion
- [ ] String input → single user message
- [ ] Array input → multiple messages
- [ ] Instructions → system message
- [ ] Implement tool conversion
- [ ] Responses tool format → Chat Completions function format
- [ ] Handle built-in tools (mark as unsupported for now)
- [ ] Implement state injection
- [ ] Load previous conversation from `previous_response_id`
- [ ] Inject into messages array
- [ ] Add comprehensive tests
#### Step 5.3: Response Converter (3-4 days)
- [ ] Create `src/converter/chat_to_responses.rs`
```rust
pub struct ChatToResponsesConverter;
impl ChatToResponsesConverter {
pub fn convert_response(
res: ChatCompletionResponse,
metadata: ResponseMetadata,
) -> Result<ResponsesResponse> {
}
}
```
- [ ] Implement message → output conversion
- [ ] Text content → text output item
- [ ] Tool calls → function_call output items
- [ ] Handle multiple choice scenarios
- [ ] Generate response IDs
```rust
fn generate_response_id() -> String {
format!("resp_{}", uuid::Uuid::new_v4())
}
```
- [ ] Add metadata transformation
- [ ] Add comprehensive tests
### Week 7: API Endpoint & State Management
#### Step 7.1: State Storage (2-3 days)
- [ ] Create `src/state/mod.rs`
```rust
#[async_trait]
pub trait StateStore: Send + Sync {
async fn save_conversation(
&self,
response_id: &str,
data: ConversationState,
) -> Result<()>;
async fn load_conversation(
&self,
response_id: &str,
) -> Result<Option<ConversationState>>;
async fn delete_conversation(&self, response_id: &str) -> Result<()>;
}
```
- [ ] Implement in-memory store
```rust
pub struct InMemoryStateStore {
store: Arc<RwLock<HashMap<String, ConversationState>>>,
}
```
- [ ] Add TTL for stored conversations
- [ ] Add size limits (prevent memory leak)
- [ ] Add cleanup task (delete expired conversations)
#### Step 7.2: Responses Endpoint (2-3 days)
- [ ] Add route in `src/server.rs`
```rust
async fn handle_responses_request(
State(state): State<AppState>,
Json(req): Json<ResponsesRequest>,
) -> Result<Json<ResponsesResponse>> {
req.validate()?;
let chat_req = converter.convert_request(req, context)?;
let chat_res = provider.chat_completions(chat_req).await?;
let responses_res = converter.convert_response(chat_res, metadata)?;
if req.store == Some(true) {
state_store.save_conversation(&responses_res.id, state).await?;
}
Ok(Json(responses_res))
}
```
- [ ] Add streaming variant
```rust
async fn handle_responses_request_stream(
State(state): State<AppState>,
Json(req): Json<ResponsesRequest>,
) -> Sse<impl Stream<Item = Result<Event>>> {
}
```
- [ ] Wire up to router
```rust
Router::new()
.route("/v1/responses", post(handle_responses_request))
```
#### Step 7.3: Integration Testing (2 days)
- [ ] Create `tests/integration/responses_api.rs`
- [ ] Test basic request/response
- [ ] Test with previous_response_id
- [ ] Test with tools
- [ ] Test streaming
- [ ] Test error cases
- [ ] Document usage examples
---
## Phase 3: Advanced Features & Polish (3-4 weeks)
**Goal**: Production-ready observability, optimizations, and user experience
### Week 8: Observability
#### Step 8.1: Prometheus Metrics (2-3 days)
- [ ] Add `prometheus` crate dependency
- [ ] Create metrics registry in `src/metrics.rs`
```rust
use prometheus::{
register_counter_vec, register_histogram_vec, CounterVec, HistogramVec,
};
lazy_static! {
pub static ref HTTP_REQUESTS_TOTAL: CounterVec = register_counter_vec!(
"modelmux_http_requests_total",
"Total HTTP requests",
&["method", "endpoint", "status"]
).unwrap();
pub static ref REQUEST_DURATION: HistogramVec = register_histogram_vec!(
"modelmux_request_duration_seconds",
"Request duration in seconds",
&["endpoint", "provider"]
).unwrap();
pub static ref PROVIDER_REQUESTS: CounterVec = register_counter_vec!(
"modelmux_provider_requests_total",
"Total provider requests",
&["provider", "model", "status"]
).unwrap();
pub static ref TOKEN_USAGE: CounterVec = register_counter_vec!(
"modelmux_tokens_total",
"Total tokens processed",
&["provider", "model", "type"] ).unwrap();
}
```
- [ ] Add metrics middleware
```rust
pub async fn metrics_middleware(
req: Request,
next: Next,
) -> Response {
let start = Instant::now();
let method = req.method().to_string();
let path = req.uri().path().to_string();
let response = next.run(req).await;
let duration = start.elapsed().as_secs_f64();
let status = response.status().as_u16().to_string();
HTTP_REQUESTS_TOTAL
.with_label_values(&[&method, &path, &status])
.inc();
REQUEST_DURATION
.with_label_values(&[&path, "vertex"])
.observe(duration);
response
}
```
- [ ] Add `/metrics` endpoint
```rust
async fn metrics_handler() -> Response {
use prometheus::Encoder;
let encoder = prometheus::TextEncoder::new();
let metrics = prometheus::gather();
let mut buffer = Vec::new();
encoder.encode(&metrics, &mut buffer).unwrap();
Response::builder()
.header("Content-Type", encoder.format_type())
.body(buffer.into())
.unwrap()
}
```
- [ ] Instrument all key paths
- [ ] HTTP requests
- [ ] Provider calls
- [ ] Token usage
- [ ] Errors by type
- [ ] Cache hits/misses (when caching added)
#### Step 8.2: Structured Logging (1-2 days)
- [ ] Switch to structured logging with `tracing`
- [ ] Add request ID to all logs
```rust
#[instrument(skip_all, fields(request_id = %uuid::Uuid::new_v4()))]
async fn handle_chat_completion(req: Request) -> Response {
tracing::info!("Processing chat completion request");
}
```
- [ ] Log key events
- [ ] Request received (with sanitized payload)
- [ ] Provider call started
- [ ] Provider call completed (with latency)
- [ ] Response sent
- [ ] Errors (with full context)
- [ ] Add log levels appropriately
- [ ] DEBUG: Detailed request/response data
- [ ] INFO: Key operations
- [ ] WARN: Retries, fallbacks
- [ ] ERROR: Failures
- [ ] Support JSON output format
```toml
[server]
log_format = "json" # or "pretty"
```
#### Step 8.3: Health Checks Enhanced (1 day)
- [ ] Extend `/health` endpoint
```json
{
"status": "healthy",
"version": "0.7.0",
"uptime_seconds": 3600,
"provider": {
"type": "vertex",
"healthy": true,
"last_check": "2025-02-15T10:00:00Z"
},
"metrics": {
"total_requests": 1000,
"success_rate": 0.98,
"avg_latency_ms": 245
}
}
```
- [ ] Add liveness probe `/health/live`
- [ ] Add readiness probe `/health/ready`
- [ ] Check provider connectivity
- [ ] Check state store connectivity
- [ ] Add configurable health check interval
### Week 9: Performance & Caching
#### Step 9.1: Request Caching (3-4 days)
- [ ] Add `redis` crate (or `moka` for in-memory)
- [ ] Create `src/cache/mod.rs`
```rust
#[async_trait]
pub trait Cache: Send + Sync {
async fn get(&self, key: &str) -> Result<Option<Vec<u8>>>;
async fn set(&self, key: &str, value: Vec<u8>, ttl: Duration) -> Result<()>;
async fn delete(&self, key: &str) -> Result<()>;
}
```
- [ ] Implement Redis cache
```rust
pub struct RedisCache {
client: redis::Client,
}
```
- [ ] Implement in-memory cache (for development)
```rust
pub struct InMemoryCache {
cache: moka::future::Cache<String, Vec<u8>>,
}
```
- [ ] Add cache key generation
```rust
fn generate_cache_key(req: &ChatCompletionRequest) -> String {
use std::collections::hash_map::DefaultHasher;
use std::hash::{Hash, Hasher};
let mut hasher = DefaultHasher::new();
req.messages.hash(&mut hasher);
req.model.hash(&mut hasher);
format!("chat:{:x}", hasher.finish())
}
```
- [ ] Add cache middleware
```rust
async fn cache_middleware(
State(cache): State<Arc<dyn Cache>>,
req: Request,
next: Next,
) -> Response {
if let Some(cached) = cache.get(&key).await.ok().flatten() {
CACHE_HITS.inc();
return Response::from_bytes(cached);
}
let response = next.run(req).await;
cache.set(&key, response.bytes(), TTL).await.ok();
CACHE_MISSES.inc();
response
}
```
- [ ] Add cache configuration
```toml
[cache]
enabled = true
type = "redis" # or "memory"
redis_url = "redis://localhost:6379"
ttl_seconds = 3600
max_size_mb = 100
```
- [ ] Add cache metrics (hits, misses, evictions)
#### Step 9.2: Connection Pooling (1-2 days)
- [ ] Review `reqwest::Client` configuration
- [ ] Optimize connection pool settings
```rust
let client = reqwest::Client::builder()
.pool_max_idle_per_host(10)
.pool_idle_timeout(Duration::from_secs(90))
.timeout(Duration::from_secs(120))
.build()?;
```
- [ ] Add connection pool metrics
- [ ] Test under load (use `wrk` or `hey`)
#### Step 9.3: Performance Testing (1-2 days)
- [ ] Create load test scripts
```bash
wrk -t 4 -c 100 -d 30s -s basic_chat.lua http://localhost:3000
```
- [ ] Test scenarios
- [ ] Simple chat completions
- [ ] Streaming responses
- [ ] Tool calling
- [ ] With caching enabled/disabled
- [ ] Document performance benchmarks
- [ ] Identify bottlenecks
- [ ] Optimize hot paths
### Week 10: Configuration & UX
#### Step 10.1: Configuration Presets (2-3 days)
- [ ] Create preset templates
- [ ] `presets/vertex.toml`
- [ ] `presets/vllm.toml`
- [ ] `presets/azure.toml`
- [ ] Enhance `modelmux config init`
```rust
let provider = Select::new()
.with_prompt("Which provider?")
.items(&["Vertex AI", "vLLM (local)", "Azure OpenAI"])
.interact()?;
let preset = load_preset(provider)?;
let config = customize_preset(preset)?;
write_config(&config)?;
```
- [ ] Add preset validation
- [ ] Document presets in README
#### Step 10.2: Better Error Messages (1-2 days)
- [ ] Audit all error messages
- [ ] Add actionable suggestions
```
Error: Authentication failed
Possible causes:
1. Service account file not found
→ Run: modelmux config init
2. Invalid credentials
→ Check: /path/to/service-account.json
3. Insufficient permissions
→ Required: roles/aiplatform.user
For more help: modelmux troubleshoot auth
```
- [ ] Add `modelmux troubleshoot` command
- [ ] Add common error solutions to docs
#### Step 10.3: Environment Auto-Detection (1 day)
- [ ] Detect GCP environment
```rust
fn detect_gcp_environment() -> Option<GcpEnv> {
}
```
- [ ] Auto-discover Vertex AI settings
```rust
fn discover_vertex_config() -> Option<VertexConfig> {
}
```
- [ ] Make `modelmux config init` smarter with defaults
### Week 11: Documentation & Examples
#### Step 11.1: Comprehensive Documentation (3-4 days)
- [ ] Create `docs/` directory structure
```
docs/
├── getting-started/
│ ├── installation.md
│ ├── quickstart.md
│ └── configuration.md
├── guides/
│ ├── vertex-ai-setup.md
│ ├── vllm-setup.md
│ ├── docker-deployment.md
│ └── kubernetes-deployment.md
├── api/
│ ├── chat-completions.md
│ ├── responses-api.md
│ └── compatibility.md
├── observability/
│ ├── metrics.md
│ ├── logging.md
│ └── tracing.md
└── troubleshooting/
├── common-errors.md
└── debugging.md
```
- [ ] Write each guide with examples
- [ ] Add diagrams (architecture, flow)
- [ ] Add screenshots where helpful
#### Step 11.2: Code Examples (2-3 days)
- [ ] Create `examples/` directory
```
examples/
├── python/
│ ├── basic_chat.py
│ ├── streaming.py
│ ├── tool_calling.py
│ └── responses_api.py
├── typescript/
│ ├── basic_chat.ts
│ ├── streaming.ts
│ └── tool_calling.ts
├── rust/
│ └── client_example.rs
└── curl/
└── examples.sh
```
- [ ] Test all examples
- [ ] Document in README
#### Step 11.3: Video/Blog Content (1-2 days)
- [ ] Create demo video
- [ ] Installation
- [ ] Configuration
- [ ] First request
- [ ] Docker deployment
- [ ] Write blog post
- [ ] "Building an OpenAI-Compatible Proxy in Rust"
- [ ] "Migrating from OpenAI to Vertex AI"
- [ ] "Supporting Multiple LLM Providers"
- [ ] Share on Reddit, HN, etc.
---
## Phase 4: Community & Extensibility (Ongoing)
**Goal**: Make it easy for contributors to add features
### Step 12.1: Contributor Guidelines (1-2 days)
- [ ] Create `CONTRIBUTING.md`
- [ ] How to set up dev environment
- [ ] How to run tests
- [ ] Code style guide
- [ ] How to add a new provider
- [ ] How to add a new endpoint
- [ ] Create `docs/ARCHITECTURE.md`
- [ ] High-level design
- [ ] Key abstractions
- [ ] Extension points
- [ ] Create issue templates
- [ ] Bug report
- [ ] Feature request
- [ ] Provider support request
### Step 12.2: Provider Plugin System (Optional, 1-2 weeks)
- [ ] Design plugin architecture
```rust
[provider]
type = "plugin"
plugin_path = "./target/release/libmy_provider.so"
```
- [ ] Implement dynamic loading
- [ ] Document plugin development
- [ ] Create example plugin
### Step 12.3: Community Features (Ongoing)
- [ ] Set up Discord/Slack community
- [ ] Create roadmap voting system
- [ ] Regular releases (semantic versioning)
- [ ] Changelog maintenance
---
## Priority Quick Reference
### Week 1
✅ Tests, error handling, docs
### Week 2
🚀 vLLM support (company priority)
### Week 3
🐳 Docker, deployment
### Week 4
📖 Responses API research & design
### Week 5-7
🔧 Responses API implementation
### Week 8
📊 Observability (metrics, logging)
### Week 9
⚡ Performance (caching, optimization)
### Week 10
🎨 UX (presets, error messages)
### Week 11
📚 Documentation & examples
---
## Success Metrics
After Phase 1 (3 weeks):
- [ ] vLLM works for company use case
- [ ] Docker image available
- [ ] Integration tests pass
After Phase 2 (6 weeks):
- [ ] Responses API works with Vertex AI
- [ ] State management functional
- [ ] Basic examples working
After Phase 3 (10 weeks):
- [ ] Production-ready observability
- [ ] Performance optimized
- [ ] Comprehensive documentation
---
## Notes for Contributors
If others want to contribute, these are **high-value, self-contained tasks**:
1. **AWS Bedrock provider** (similar to vLLM, but AWS auth)
2. **Azure OpenAI provider** (easiest - just URL + auth)
3. **Embeddings endpoint** (`/v1/embeddings`)
4. **Built-in tools** (web_search, file_search)
5. **Web UI** (config management, monitoring)
6. **Terraform/CloudFormation** (deployment templates)
7. **Helm chart** (Kubernetes deployment)
Each can be developed independently without blocking core work.
---
## Timeline Summary
| Phase 1: Foundation | 3 weeks | vLLM working, Docker ready, tests passing |
| Phase 2: Responses API | 4 weeks | Responses API functional with Vertex |
| Phase 3: Polish | 4 weeks | Production-ready, well-documented |
| **Total** | **~11 weeks** | **Professional, extensible proxy** |
---
## Next Actions (Start Tomorrow)
1. Create `tests/integration/` directory — **[TASK-001](tasks/TASK-001-integration-tests.md)**
2. Write first integration test — **TASK-001**
3. Implement `OpenAiCompatibleProvider` — **[TASK-004](tasks/TASK-004-openai-compatible-provider.md)**
4. Update config schema for provider selection — **[TASK-005](tasks/TASK-005-provider-configuration.md)**
**Task index**: See [tasks/README.md](tasks/README.md) for all detailed task files. Each task follows [AGENT.md](AGENT.md) and [tools/task_template.md](tools/task_template.md).
Let's build! 🚀