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Update ROADMAP.md: mark Phase 1 as complete 

https://claude.ai/code/session_012cMotoivyjuMwbrnDo6YRg
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Claude 2026-04-01 01:18:32 +00:00
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ROADMAP.md
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@ -4,24 +4,24 @@ Transform `open-multi-agent` into `@vcg/agent-sdk`: a turnkey agent framework fo
---
## Phase 1: Foundation — vLLM Adapter + Package Rebranding
## Phase 1: Foundation — vLLM Adapter + Package Rebranding ✅ COMPLETE
**Goal:** Agents can target our vLLM servers out of the box.
### 1A. Package Rename
### 1A. Package Rename ✅
- Rename from `open-multi-agent` to `@vcg/agent-sdk`
- Rename `OpenMultiAgent` class to `VCGAgent` (or `AgentSDK`)
- Update all exports, doc comments, and README
- ✅ Renamed from `open-multi-agent` to `@vcg/agent-sdk`
- ✅ Renamed `OpenMultiAgent` class to `VCGAgentSDK`
- ✅ Added deprecated `OpenMultiAgent` re-export alias for backward compat
- ✅ Updated all exports, doc comments, JSDoc, and example files
### 1B. vLLM Adapter
### 1B. vLLM Adapter ✅
vLLM exposes an OpenAI-compatible API, so the adapter extends the existing OpenAI adapter pattern with custom base URL and model config.
- **New** `src/llm/vllm.ts` — `VLLMAdapter` class
- **New** `src/llm/openai-compat.ts` — extract shared OpenAI-format helpers (message conversion, tool formatting, streaming) so both `OpenAIAdapter` and `VLLMAdapter` reuse them
- **Modify** `src/llm/adapter.ts` — add `'vllm'` to `createAdapter()` factory
- **Modify** `src/types.ts` — add `VLLMConfig` type, `'vllm'` to provider unions
- ✅ **New** `src/llm/openai-compat.ts` — extracted shared OpenAI-format helpers (message conversion, tool formatting, response parsing, streaming) so both `OpenAIAdapter` and `VLLMAdapter` reuse them
- ✅ **New** `src/llm/vllm.ts` — `VLLMAdapter` class with `chat()`, `stream()`, and `healthCheck()`
- ✅ **Modified** `src/llm/openai.ts` — refactored to import from `openai-compat.ts`
- ✅ **Modified** `src/llm/adapter.ts` — added `'vllm'` to `createAdapter()` factory; accepts `VLLMConfig` object
- ✅ **Modified** `src/types.ts` — added `VLLMConfig` type, `'vllm'` to all provider unions
```typescript
interface VLLMConfig {
@ -33,11 +33,12 @@ interface VLLMConfig {
}
```
### 1C. Centralized Configuration
### 1C. Centralized Configuration ✅
- **New** `src/config/defaults.ts` — default vLLM server URL, model, common settings
- **New** `src/config/index.ts` — `loadConfig()` with priority: constructor args > env vars > config file
- Env vars: `VCG_VLLM_URL`, `VCG_VLLM_MODEL`, `VCG_VLLM_API_KEY`, `VCG_DEFAULT_PROVIDER`, `VCG_LOG_LEVEL`, `VCG_LOCALE`
- ✅ **New** `src/config/defaults.ts` — `DEFAULT_CONFIG` and `loadConfig(overrides?)` with priority: constructor args > env vars > defaults
- ✅ **New** `src/config/index.ts` — re-exports
- ✅ **New** `VCGConfig` type in `src/types.ts`
- ✅ Env vars: `VCG_VLLM_URL`, `VCG_VLLM_MODEL`, `VCG_VLLM_API_KEY`, `VCG_DEFAULT_PROVIDER`, `VCG_MAX_CONCURRENCY`, `VCG_LOG_LEVEL`
---
@ -294,9 +295,9 @@ const agent = createChatAgent({ locale: 'ja-JP' })
## Build Order
```
Phase 1 (vLLM + rebrand) <- Start here, immediate value
Phase 1 (vLLM + rebrand) ✅ COMPLETE
|
Phase 2 (presets + DX) <- Devs can start using it
Phase 2 (presets + DX) <- NEXT: Devs can start using it
|
Phase 3 (tool packs) \
>-- Can be parallelized
@ -316,7 +317,7 @@ Phase 7 (production hardening) <- Final polish
| vLLM adapter approach | Extend OpenAI adapter via shared `openai-compat.ts` | vLLM is OpenAI-compatible; avoids code duplication |
| Request queue placement | Transparent wrapper around `LLMAdapter` | Agents are unaware of queuing; zero code changes for consumers |
| Queue implementation | Priority queue + semaphore + token bucket | Handles concurrency, rate limits, and fairness in one layer |
| Config management | Env vars > config file > constructor (merge) | Flexible for different deployment contexts |
| Config management | Env vars > constructor args > defaults (merge) | Flexible for different deployment contexts |
| Cron library | Lightweight internal parser (or `cron-parser` dep) | Avoids heavy dependencies |
| i18n approach | JSON locale files + template system | Simple, no heavy framework needed |
| Tool middleware | Function composition (decorator pattern) | Familiar, zero-dependency, composable |