Next Chats

中文版 README（简体中文）

Chat window (conversation / thinking / tools / topic rail)

Admin console (LLM providers / MCP / approvals / audit / usage)

A B/S multimodal AI chat platform based on the DeepSeek Harness principles: LLM + MCP (Model Context Protocol) + Skill plugin orchestration, with a Cordis-driven, Three.js-powered 3D frontend.

Reference paper: A Programming Paradigm for Spatiotemporal Composability — everything is a plugin, driven by Cordis.

Multilingual UI: English (default) and 简体中文 — switch from the top bar (🌐 EN / 中文); your choice is remembered in localStorage.

Architecture & Flows

Screenshots

Chat window (conversation / thinking / tools / topic rail)

Admin console (LLM providers / MCP / approvals / audit / usage)

Data Model (ER)

erDiagram
    APP_USER {
        Guid id PK
        string username UK
        string display_name
        string email
        string password_hash
        string password_salt
        string status
        datetime created_at
        datetime last_login_at
    }
    APP_ROLE {
        Guid id PK
        string name
        string code UK
        string description
        bool is_system
        datetime created_at
    }
    CHAT_SESSION {
        Guid id PK
        Guid user_id FK
        string title
        string status
        Guid llm_provider_id
        string context_json
        datetime created_at
        datetime updated_at
        datetime last_message_at
    }
    CHAT_MESSAGE {
        Guid id PK
        Guid session_id FK
        Guid user_id FK
        string role
        text content
        text reasoning
        string tool_calls_json
        string status
        string model
        int prompt_tokens
        int completion_tokens
        string trace_id
        string client_message_id UK
        datetime created_at
    }
    LLM_PROVIDER {
        Guid id PK
        string name
        string kind
        string base_url
        string api_key_encrypted
        string model
        int context_window
        int priority
        bool enabled
        bool is_vision
        bool is_healthy
        datetime created_at
    }
    MCP_SERVER {
        Guid id PK
        string name
        string transport
        string endpoint
        string headers_json
        string stdio_command
        bool enabled
        bool is_vision
        int timeout_seconds
        string description
        datetime created_at
    }
    MCP_CATALOG_ITEM {
        Guid id PK
        Guid mcp_server_id FK
        string kind
        string name
        string description
        string schema_json
        bool enabled
    }
    PROMPT {
        Guid id PK
        string name
        string description
        text content
        bool enabled
        int version
        datetime created_at
    }
    SKILL {
        Guid id PK
        string name
        string meta_tool_name
        text instruction
        bool enabled
        string model_override
        int max_nested_steps
    }
    TOOL_APPROVAL {
        Guid id PK
        string trace_id
        Guid user_id FK
        Guid session_id FK
        string mcp_server_name
        string tool_name
        string arguments_json
        string status
        datetime created_at
        datetime decided_at
        datetime expires_at
    }
    AUDIT_LOG {
        Guid id PK
        string trace_id
        Guid user_id FK
        string category
        string action
        string target
        string detail_json
        string ip
        bool is_suspicious
        datetime created_at
    }
    USER_SETTING {
        Guid id PK
        Guid user_id FK
        string key
        string value_json
    }
    TOKEN_USAGE_RECORD {
        Guid id PK
        string trace_id UK
        Guid user_id FK
        Guid session_id FK
        string provider_name
        string model
        int total_tokens
        decimal cost
        int ttft_ms
        datetime created_at
    }

    APP_USER ||--o{ CHAT_SESSION : "owns"
    APP_USER ||--o{ CHAT_MESSAGE : "sends"
    APP_USER }o--o{ APP_ROLE : "member_of (user_roles)"
    APP_USER ||--o{ USER_SETTING : "has"
    APP_ROLE }o--o{ MCP_SERVER : "can_use (role_mcp_servers)"
    APP_ROLE }o--o{ PROMPT : "can_use (role_prompts)"
    APP_ROLE }o--o{ SKILL : "can_use (role_skills)"
    MCP_SERVER ||--o{ MCP_CATALOG_ITEM : "catalog"
    CHAT_SESSION ||--o{ CHAT_MESSAGE : "contains"
    CHAT_SESSION }o--o| LLM_PROVIDER : "uses"
    TOOL_APPROVAL }o--|| APP_USER : "requested_by"
    TOOL_APPROVAL }o--|| CHAT_SESSION : "in"
    AUDIT_LOG }o--o| APP_USER : "actor"
    TOKEN_USAGE_RECORD }o--o| APP_USER : "accrued_by"

System Architecture

flowchart LR
    subgraph Web["Web (Vue 3 + Cordis)"]
        UI[Chat UI / 3D Theme] --> K[Kernel Plugins]
        K -->|SSE / REST| HTTP[Vite proxy → /api]
    end

    subgraph Api["NextChats.Api (ASP.NET Core)"]
        Ctl[Controllers / RBAC / Audit] --> Mod[Error Localization Middleware]
        Ctl --> Orch{{ChatOrchestrator}}
        Ctl --> Adm[Admin: Providers / MCP / Prompts / Skills / RBAC / Approvals]
    end

    Orch --> Loop{{AgentLoopEngine}}
    Loop -->|ReAct think→act→observe| Router[LLM Router]
    Router --> OpenAI[OpenAI-Compatible Client]
    Router --> Mock[Mock Client: localized output]
    Orch --> Driver[MCP Driver]
    Driver --> MCP1[(MCP Server: Demo 5300)]
    Driver --> MCP2[(MCP Server: more…)]
    Orch --> Skills[Skill Execution Engine]
    Orch --> Policy[Policy Engine / Approval Coordinator]

    Api --> DB[(SQLite: config / chat / audit)]
    UI --> V3[Three.js scenes]

    style Orch fill:#e3f2fd
    style Loop fill:#fff3e0

Chat (ReAct) Flow — SSE streaming

sequenceDiagram
    participant U as User (Browser)
    participant K as Cordis Kernel
    participant C as ChatController
    participant O as ChatOrchestrator
    participant L as AgentLoopEngine
    participant LLM as LLM Client
    participant M as MCP Driver / Skills

    U->>K: type message (+ optional images)
    K->>C: POST /api/chat/stream (SSE)
    C->>O: ChatStreamRequest (lang, images)
    O-->>C: SESSION_NOT_FOUND? / INPUT_FLAGGED? (error event)
    O->>L: AgentRunRequest (tools, context window)
    loop ReAct round
        L->>LLM: stream prompt + tools
        LLM-->>L: reasoning / text / tool calls chunks
        L-->>U: thinking_start · thinking_delta · text_delta (SSE)
        alt tool call
            L->>Policy: evaluate (allow/deny/approval)
            Policy-->>U: approval_updated (pending) → user approves
            L->>M: execute tool (approval / args)
            M-->>L: tool result
            L-->>U: tool_start · tool_result (SSE)
        end
    end
    L-->>U: done (usage: tokens / ttft / cost)
    O->>DB: persist assistant message + usage + audit (trace_id)
    O-->>C: SSE end event
    C-->>K: event stream → UI renders

Image / Vision Flow (image_source standard base64)

sequenceDiagram
    participant U as User (Browser)
    participant B as ChatInputBar
    participant C as ChatController
    participant O as ChatOrchestrator
    participant V as Vision MCP Tool (image_source)
    participant LLM as LLM (Mock / real)
    participant P as Provider · MCP Vision Flags

    U->>C: GET /api/chat/vision-config
    C->>P: provider.IsVision ∨ bound MCP.IsVision
    P-->>U: supported: true → show upload button + Ctrl+V

    U->>B: paste (Ctrl+V) or upload N images
    B->>B: validate type/size → dataURL → base64
    B->>C: POST /api/chat/stream { message, images[] }
    C->>O: ChatStreamRequest.Images (standard base64)
    loop each image (multi-image recognized one by one)
        O->>V: call vision tool { image_source: base64 }
        V-->>O: recognition text (per image)
    end
    O->>LLM: system + history + [vision texts] + user(message + <image_source> blocks)
    LLM-->>U: streamed answer (SSE) using the recognized text

Tech Stack

Layer	Technology
Backend	.NET 10 · ASP.NET Core · EF Core (SQLite now → MySQL 8 ready)
MCP	ModelContextProtocol SDK 2.2.0 (latest stable) · Streamable HTTP transport · STDIO extensible
Frontend	Vue 3 · TypeScript · Vite · Element Plus · Three.js · Cordis ^3.18.1 (plugin kernel)
Security	JWT · PBKDF2-SHA256(210k) password hashing · AES-256-GCM secret encryption · sanitized audit logs
i18n	vue-i18n 10 (en / zh-CN, English default) · `X-Lang` error localization on the API

Repository Layout

next-chats/
├── src/
│   ├── NextChats.Core/           # Domain model, orchestration, engines, drivers (no ASP.NET dependency)
│   ├── NextChats.Infrastructure/ # EF Core data, caching, security services (swappable)
│   └── NextChats.Api/            # Web API (SSE streaming, admin, RBAC, audit, metrics, i18n)
├── samples/McpDemoServer/        # Demo MCP Server (Streamable HTTP)
└── web/                          # Vue 3 + Cordis frontend
    ├── src/i18n/                 # en / zh dictionaries (per-domain locale files)
    └── scripts/smoke*.mjs        # End-to-end smoke scripts (Node fetch streaming)

Quick Start

# 1. Start the demo MCP Server (port 5300)
dotnet run --project samples/McpDemoServer -c Debug

# 2. Start the API (port 5210; DB is auto-created and seeded on first boot)
dotnet run --project src/NextChats.Api -c Debug

# 3. Start the frontend (Vite proxies /api → 5210)
cd web && npm install && npm run dev
# Open http://localhost:5173  (seeded admin: admin / admin123)

Core Design

Orchestration (Core)

Effective tool intersection: role binding ∩ user selection ∩ MCP global enabled per user, with data isolation.
Active Skill matching: Skills are exposed to the model as meta tools (skill_<slug>); instructions are lazy-loaded to avoid token explosion.
Prompt building: a template engine ({{var}} / #if / #each / #section) renders system prompts.
ReAct loop (AgentLoopEngine): producer–consumer Channel architecture decouples LLM streaming from the main loop; safe interruption.

Engines (Core)

LLM Router: multi-vendor priority + round-robin + failover (mark-unhealthy circuit breaking); OpenAI-compatible & Mock clients.
MCP driver: strictly follows the latest MCP spec (see MCP 2026-07 revision); connection pooling; call retry with backoff. MCP errors go into the loop — tool errors are fed back to the model as tool results for retry/explanation; sessions never break because of an MCP error.
Policy engine: Allow → Deny → RequireApproval with dangerous-operation heuristics (delete_all etc.); approvals support pending/approved/rejected/expired.
Context manager: token estimate → compress (LLM summary) → truncate; never exceeds the model length limit and never interrupts the session.

Server-side Unified Configuration (Admin)

LLM providers (enable flag / model / endpoint / encrypted api-key / temperature / context-window)
MCP servers (Name / Endpoint / Headers JSON encrypted; "Fetch" auto-discovers tools/prompts/resources with schemas; per-item disable)
Multiple Prompts & Skills (pluggable, lazy-load)
RBAC: users / roles; role ↔ MCP / Prompt / Skill bindings

Observability & Cost

trace_id (trc_...) flows through Orchestration / LLM / MCP / Audit; token accounting in/out; metrics: TTFT, tokens, cost, tool latency, approval count (/api/admin/metrics/usage).
Idempotent writes: unique (UserId, ClientMessageId) key; key chat logs persist across restarts.

Logging, three channels

To the user: friendly message + error code (no stack / endpoint / header)
To the model: tool errors are re-fed as tool results (retryable, explainable)
To the logs: full context (sanitized)

Internationalization

Frontend: vue-i18n with per-domain locale files under web/src/i18n/locales/{en,zh}/; English is the default locale.
Backend: every user-visible message lives in one dictionary (src/NextChats.Core/Localization/Texts.cs, en/zh) — no hard-coded UI text in code. The API localizes errors via the X-Lang request header (or Accept-Language), so non-browser clients get the same language, e.g. curl -H "X-Lang: en"; seeded content (roles, prompts, skills, demo provider) is English.
README: this English file and README.zh.md are linked at the top.

Smoke Verification (web/scripts/)

node scripts/smoke.mjs          # login → create session → SSE streaming chat → persistence → metrics
node scripts/smoke-tools.mjs    # ReAct tool call (tool:echo) → dangerous approval flow (danger:delete_all)

MCP References

What's new in MCP (2026-07-28)
ModelContextProtocol C# SDK 2.2.0: McpClient.CreateAsync + HttpClientTransport (Streamable HTTP) / StdioClientTransport

Name		Name	Last commit message	Last commit date
docs		docs
reviews		reviews
samples/McpDemoServer		samples/McpDemoServer
src		src
web		web
.gitignore		.gitignore
Directory.Build.props		Directory.Build.props
LICENSE		LICENSE
NextChats.slnx		NextChats.slnx
README.md		README.md
README.zh.md		README.zh.md

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Next Chats

Architecture & Flows

Screenshots

Data Model (ER)

System Architecture

Chat (ReAct) Flow — SSE streaming

Image / Vision Flow (image_source standard base64)

Tech Stack

Repository Layout

Quick Start

Core Design

Orchestration (Core)

Engines (Core)

Server-side Unified Configuration (Admin)

Observability & Cost

Logging, three channels

Internationalization

Smoke Verification (web/scripts/)

MCP References

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Next Chats

Architecture & Flows

Screenshots

Data Model (ER)

System Architecture

Chat (ReAct) Flow — SSE streaming

Image / Vision Flow (image_source standard base64)

Tech Stack

Repository Layout

Quick Start

Core Design

Orchestration (Core)

Engines (Core)

Server-side Unified Configuration (Admin)

Observability & Cost

Logging, three channels

Internationalization

Smoke Verification (web/scripts/)

MCP References

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