NagaAgent
An anime-styled all-in-one AI assistant with streaming tool calls, knowledge-graph memory, Live2D avatars, voice interaction, and OpenClaw integration.
Evidence shows: README mentions placeholder API key rejection, preserving real keys on save, and Skill path traversal rejection, but lacks a full permission model or user confirmation mechanism. Deductions: no explicit user confirmation flow, limited data flow transparency, insufficient sensitive data handling details, un-audited dependency security, external effects (e.g., OpenClaw auto-execution) without user confirmation, no rollback mechanism, source attribution only author info.
Evidence shows: README and test files demonstrate config path priority, tool execution, route tests, showing good self-consistency. Deductions: dependency availability unverified (e.g., Neo4j, OpenClaw), failure messages partial coverage.
Evidence shows: README describes multiple use cases (chat, memory, game guides), supports Windows/macOS/Linux, flexible config. Deductions: capability boundaries unclear (e.g., OpenClaw auto-execution scope), trigger precision (e.g., tool call parsing) described but not fully verified, environment fit details (e.g., packaging) not deep.
Evidence shows: README well-structured, install steps detailed, naming stable (version unified), examples and FAQ (troubleshooting), license clear (AGPL-3.0 dual), changelog detailed. Deductions: known limitations not explicitly listed, maintenance responsibility only author info, no contributor or maintenance policy.
Evidence shows: output usability (e.g., structured tool results) described, marginal value (e.g., multi-agent collaboration) evident. Deductions: cost-benefit not quantified, no resource or performance data.
Evidence shows: README feature descriptions partially correspond to code structure (e.g., directory), tests provide some verification. Deductions: claim traceability insufficient (e.g., feature claims not fully mapped to code), cross-source corroboration limited (only within repo), fact-inference separation unclear.
- The repository includes auto-execution tools (e.g., OpenClaw) and web exploration features that may have external effects; configure permissions and user confirmation carefully.
- Dependencies are numerous and not security-audited; check for vulnerabilities before use.
- License is AGPL-3.0 dual-licensed; commercial use requires attention to closed-source license terms.
What does this agent do, and when should you use it?
NagaAgent is a full-stack agent framework for personal assistants, built with an Electron + Vue 3 frontend and multiple Python microservice backends. It features streaming tool calls (parsed from inline tool code blocks, not dependent on OpenAI Function Calling), GRAG knowledge-graph memory (Neo4j or local), Live2D virtual avatar interaction, TTS/ASR voice interaction, and OpenClaw computer control. The software comprises five independent services: API Server (port 8000), Agent Server (8001), MCP Server (8003), Memory Server (8004), and Voice Service (5048), orchestrated by main.py. The main panel offers eight modules: Chat, MindSea (knowledge graph visualization), Skill Workshop, Naga Network (forum), Cardinal Market, Terminal Settings, Music Studio, and Floating Orb. It uses a dual license: AGPL-3.0 for open source and a proprietary license for commercial use, supporting Windows/macOS/Linux.
NagaAgent performs the following concrete operations: 1) Streams dialogue via SSE, parses JSON tool calls from inline ``tool`` code blocks, and routes them to MCP Manager or OpenClaw, supporting up to 5 tool loops. 2) Extracts quintuples (subject, predicate, object, etc.) from conversation and stores them in Neo4j or local JSON, injecting retrieved context via RAG. 3) Renders Live2D models with a 4-channel orthogonal animation system (state, action, emotion, tracking) and SSAA anti-aliasing. 4) Provides TTS (Edge-TTS) and ASR (FunASR) voice interaction, including real-time full-duplex voice chat (Qwen Omni). 5) Controls the computer via OpenClaw Gateway, with task scheduling and memory compression. 6) Includes 9 built-in MCP agents (weather, launcher, game guide, search, crawler, browser automation, vision, MQTT, document). 7) Offers community forum and credit/affinity system.
- Individual users seeking a desktop companion AI with anime avatar for friendly chat and tool use like weather queries and app launching.
- Gamers using the game guide agent to automatically recognize game screenshots and receive suggestions, optionally automating gameplay via MAA plugins.
- Developers and power users integrating OpenClaw to execute local computer tasks through natural language, such as file operations and browser automation.
- Users needing knowledge management, leveraging Neo4j graph memory to record facts and automatically retrieve them in future conversations.
- Content creators using voice interaction for hands-free dialogue or TTS to generate audio content.
- Community members engaging in forum activities, earning credits and affinity through daily check-ins to unlock model quotas or music albums.
What are this agent's strengths and limitations?
- Streaming tool calls are provider-agnostic, working with any OpenAI-compatible API and also supporting Anthropic format, reducing vendor lock-in.
- Rich built-in components including Live2D avatar, voice interaction, and game guide, offering a unique anime-style experience.
- Knowledge-graph memory (GRAG) supports local Neo4j or cloud NagaMemory, enhancing context understanding.
- Includes MCP server and skill workshop with community skill installation, enabling extensibility.
- Desktop-focused, relying on Electron and multiple Python microservices, making deployment and configuration complex.
- Core services like NagaBusiness gateway and NagaMemory require login and may incur credits; functionality is limited when not logged in.
- Dual licensing (AGPL-3.0 + commercial) may impose additional requirements for commercial use.
- Real-time voice chat depends on DashScope (Qwen) API, introducing specific vendor dependency.
How do you install or deploy this agent?
Requirements: Python 3.11 (>=3.11,<3.12), optional uv, optional Neo4j. Steps: 1) git clone https://github.com/Xxiii8322766509/NagaAgent.git; 2) cd NagaAgent; 3) Install frontend: cd frontend && npm install && cd..; 4) Install backend (option A: uv sync; option B: python -m venv .venv && source .venv/bin/activate (Windows: .\venv\Scripts\activate) && pip install -r requirements.txt). 5) Copy config.json.example to config.json and fill in LLM API details (api_key, base_url, model).
How do you use this agent?
After configuration, run cd frontend && npm run dev to launch the app (one-click startup supported). Log in to auto-configure API keys, or choose 'Don't log in, use open-source version' and set local keys manually. From the main panel, access Chat, MindSea, Skill Workshop, etc. Configure model connection, memory, and audio-visual settings in Terminal Settings. To use OpenClaw, ensure the Agent Server is running and enabled in settings.