NagaAgent
An anime-styled all-in-one AI assistant with streaming tool calls, knowledge-graph memory, Live2D avatars, voice interaction, and OpenClaw integration.
- Source repo
- RTGS2017/NagaAgent
- Stars
- ★ 1.5k
- Last updated
- 2mo ago
- License
- AGPL-3.0
- Primary language
- Python
- FA score
- 45/100 · Major gaps
At a glance
- How it runs
- Works with
- Platform-specific
- Cost
- Free tier plus a paid hosted plan
- Setup effort
- High · needs real infrastructure
- You'll need
- Typical use
- Individual users seeking a desktop companion AI with anime avatar for friendly chat and tool use like weather queries and app launching.
- Not a fit if
- Casual users wanting a one-command install; five services and API keys must be configured
- Teams needing fully offline, local-only data; cloud memory and voice use vendor services
- Closed-source commercial projects unwilling to accept AGPL-3.0 or a paid licence
- Source review
- 45/100 · Major gaps 1 safety controls not found
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.
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.
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 does this agent compare with similar options?
Key facts side by side with the most closely related agents.
| Agent | Source review | Form / cost | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|---|
| NagaAgent This agent | 45 · Major gaps | Desktop appFreemium | ★ 1.5k | 2mo ago | Python | — |
| WUPHF — A Collaborative Office for AI Employees with a Shared Brain | 66 · Some gaps | CLIFree + model costs | ★ 1.5k | 3d ago | Go | Codex · Claude Code |
| Maestro-Flow | 52 · Major gaps | CLIFree + model costs | ★ 566 | 3d ago | TypeScript | Codex · Claude Code · Claude API |
| RepoBrain – Repo Intelligence Engine | 49 · Major gaps | Agent plugin / skillFree + model costs | ★ 1.4k | 3d ago | Python | Codex · Claude Code |
How does FollowAgents rate this agent?
Why each dimension lost points
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.
- Not found in source: rollback or recovery pathBack up first, or work on a git branch or snapshot, so its changes can be undone.
- 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.
Recent releases
Latest entries from the project's GitHub Releases. Notes are quoted from upstream as written, not rewritten or tested by FollowAgents.
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v5.1.5 NagaAgent 5.1.5
Release notes excerpt
Full Changelog: https://github.com/RTGS2017/NagaAgent/compare/v5.1.4...v5.1.5View on GitHub ↗ -
v5.1.4 NagaAgent 5.1.4
Release notes excerpt
Full Changelog: https://github.com/RTGS2017/NagaAgent/compare/v5.1.3...v5.1.4View on GitHub ↗ -
v5.1.3 NagaAgent 5.1.3
Release notes excerpt
Full Changelog: https://github.com/RTGS2017/NagaAgent/compare/v5.1.2...v5.1.3View on GitHub ↗