Roadmap

Documentation Index

Fetch the complete documentation index at: https://mintlify.com/HKUDS/nanobot/llms.txt

Use this file to discover all available pages before exploring further.

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Vision

nanobot aims to be the simplest, most hackable AI agent framework while maintaining full functionality. We’re not trying to be the biggest or most feature-rich — we’re optimizing for clarity, simplicity, and research-readiness.

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Design Goals

1. Keep it tiny: Target ~5,000 core agent lines (currently ~4,000)
2. Stay readable: Every line should be understandable
3. Make it hackable: Easy to modify and extend
4. Remain practical: Real features, not toy examples

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Current Status (v0.1.4.post3)

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✅ What’s Working

• 10 chat channels: Telegram, Discord, WhatsApp, Feishu, Email, Slack, QQ, DingTalk, Matrix, Mochat
• 15+ LLM providers: OpenRouter, Anthropic, OpenAI, DeepSeek, Gemini, Groq, and more
• 8 built-in tools: Shell, filesystem, web, spawn, cron, message, MCP
• MCP support: Model Context Protocol integration
• Multi-modal: Images, voice transcription (Groq Whisper)
• Memory system: Persistent MEMORY.md
• Subagents: Background task spawning
• Scheduled tasks: Cron-based scheduling + heartbeat
• Session isolation: Per-user/thread conversations
• Prompt caching: Anthropic/OpenRouter support
• OAuth providers: OpenAI Codex, GitHub Copilot
• Thinking mode: Experimental reasoning support

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🔧 Current Limitations

• Manual testing only: No automated test suite
• Basic memory: Simple markdown, no vector search
• Limited multimodal: Images receive-only (most channels)
• No streaming: Responses sent after completion
• Simple context: No advanced retrieval

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Roadmap

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Phase 1: Enhanced Multi-Modal (Q2 2026)

Goal: See, hear, and create media. Features:

• Vision: Image understanding (GPT-4V, Claude 3)
• Image generation: DALL-E, Stable Diffusion integration
• Video support: Receive and analyze videos
• Voice output: Text-to-speech responses
• Audio analysis: Analyze audio files beyond transcription

Channels impacted:

• Telegram (send images)
• Discord (send images)
• WhatsApp (send images)
• All channels (receive images for vision)

Tools:

• generate_image(prompt: str) -> image_path
• analyze_image(image_path: str) -> description
• speak(text: str) -> audio_path

Estimated complexity: Medium (LiteLLM supports vision, need tool wrappers)

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Phase 2: Long-Term Memory (Q3 2026)

Goal: Never forget important context. Features:

• Vector search: Semantic memory retrieval
• Automatic summarization: Compress old conversations
• Entity tracking: Remember people, places, facts
• Memory importance scoring: Prioritize key information
• Multi-document memory: Organize by topic/project

Implementation ideas:

• Use lightweight vector DB (ChromaDB, DuckDB)
• Auto-summarize conversations > N messages
• Extract entities with LLM calls
• Store in ~/.nanobot/memory/ with indexes

Tools:

• remember(fact: str, importance: int)
• recall(query: str) -> relevant_facts
• forget(fact_id: str)

Estimated complexity: High (vector DB integration, summarization logic)

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Phase 3: Better Reasoning (Q4 2026)

Goal: Multi-step planning and self-reflection. Features:

• Chain-of-thought: Explicit reasoning steps
• Task decomposition: Break complex tasks into subtasks
• Self-critique: Evaluate and revise outputs
• Plan visualization: Show reasoning tree to user
• Alternative exploration: Consider multiple approaches

Implementation ideas:

• Add reason() tool for internal thinking
• Multi-pass agent loop (plan → execute → reflect)
• Reasoning prompt templates
• Visualization in web UI (future)

Tools:

• plan(goal: str) -> steps[]
• critique(output: str) -> improvements[]
• reflect() -> insights

Estimated complexity: High (new agent loop patterns)

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Phase 4: More Integrations (Ongoing)

Goal: Work with more platforms and tools. Channels:

• Twitter/X: Post tweets, reply to mentions
• LinkedIn: Messaging integration
• SMS: Twilio integration
• Mastodon: Fediverse support
• iMessage: Apple Messages (via bridge)
• Signal: Private messaging
• Zulip: Team chat
• Mattermost: Open-source Slack alternative

Providers:

• Together AI: Fast inference
• Fireworks: Model zoo
• Replicate: Run any model
• Cohere: Command models
• AI21: Jurassic models
• Mistral: Mistral AI (if not via OpenRouter)

Tools:

• Calendar: Google Calendar, Outlook integration
• Email send: Proactive email sending
• File sync: Dropbox, Google Drive
• Database: SQL query execution
• Code execution: Jupyter kernels
• Browser: Playwright/Selenium automation

Estimated complexity: Medium per integration

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Phase 5: Self-Improvement (2027)

Goal: Learn from feedback and mistakes. Features:

• User feedback loop: Rate responses, agent learns
• Error tracking: Log and analyze failures
• Automatic retries: Fix mistakes without user intervention
• Preference learning: Adapt to user style
• Skill discovery: Auto-install useful skills

Implementation ideas:

• Feedback storage in ~/.nanobot/feedback/
• Error pattern detection
• Preference profiles in config
• Skill marketplace integration (ClawHub)

Tools:

• rate_response(rating: int, feedback: str)
• analyze_errors() -> patterns[]
• adjust_preferences(key: str, value: any)

Estimated complexity: Very High (ML components, feedback loops)

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Community Priorities

Based on GitHub discussions and Discord feedback:

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High Demand

1. Web UI: Browser-based interface (like OpenWebUI)
2. Streaming responses: Real-time output
3. Function calling improvements: Parallel tool execution
4. Better error messages: More helpful diagnostics
5. RAG support: Document Q&A

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Medium Demand

1. Plugin system: Third-party tool installation
2. Multi-agent coordination: Agents working together
3. Custom prompts: User-defined system prompts
4. Voice UI: Speak to agent directly
5. Mobile app: iOS/Android companion

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Low Demand (but interesting)

1. Agent marketplace: Share and download agents
2. Blockchain integration: Web3 tools
3. IoT control: Smart home integration
4. AR/VR: Spatial computing

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Non-Goals

What we’re not building (to keep nanobot simple): ❌ Enterprise features: SSO, multi-tenancy, admin panels
❌ Complex UIs: Rich web dashboards (keep it CLI-first)
❌ Heavy dependencies: Avoid large frameworks (Django, etc.)
❌ Monolithic architecture: Stay modular and hackable
❌ Kitchen sink: Don’t add every possible feature If you need these, consider building on top of nanobot or using a different framework.

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How to Contribute

Want to help with the roadmap?

1. Pick an item from the roadmap above
2. Open a GitHub Discussion to discuss your approach
3. Create a PR with your implementation
4. Get feedback from maintainers
5. Iterate until it’s ready to merge

See the Contributing Guide for details.

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Versioning Strategy

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Current: v0.1.x (Alpha)

• Rapid iteration
• Breaking changes allowed
• Focus on core features

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Future: v0.2.x (Beta)

• Stable API
• Deprecation warnings before breaking changes
• Focus on polish and reliability

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Long-term: v1.0.0 (Stable)

• Production-ready
• Semantic versioning
• Long-term support

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Release Cadence

• Patch releases (v0.1.4.post1): As needed (bug fixes)
• Minor releases (v0.1.5): Every 1-2 weeks (new features)
• Major releases (v0.2.0): When API changes significantly

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Feature Requests

Have an idea? Here’s how to suggest it:

1. Check existing issues/discussions: Might already be planned
2. Open a GitHub Discussion: Describe the feature and use case
3. Gauge community interest: See if others want it too
4. Estimate complexity: How many lines of code?
5. Propose implementation: How would it fit into nanobot?

Good feature requests:

• Align with nanobot’s goals (simple, hackable)
• Have clear use cases
• Don’t add excessive complexity
• Can be implemented in less than 500 lines

Bad feature requests:

• “Add everything from framework X”
• Niche features used by less than 1% of users
• Require heavy dependencies
• Violate the “keep it simple” principle

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Research Areas

For academic/research use:

1. Memory architectures: Better long-term memory designs
2. Multi-agent systems: Agent communication protocols
3. Tool learning: Automatic tool discovery and composition
4. Context optimization: Smarter prompt compression
5. Reasoning methods: Novel planning and reflection techniques

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Metrics

How we measure success:

• Lines of code: Keep core agent under 5,000 lines
• Startup time: CLI mode under 1 second
• Dependencies: Minimize third-party packages
• Documentation: Every feature documented
• Community: Active Discord, GitHub discussions
• Real usage: People actually use it daily

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Timeline

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Long-Term Vision (2028+)

• Autonomous agents: Proactively help without prompting
• Agent collaboration: Multiple agents working together
• Continuous learning: Improve over time from usage
• Universal interface: Control anything via natural language
• Personal AI OS: nanobot as your digital assistant layer

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Get Involved

• Discord: Join the community
• GitHub: HKUDS/nanobot
• Discussions: Share ideas
• Issues: Report bugs

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Frequently Asked Questions

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Will nanobot always be free?

Yes. MIT licensed, forever.

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Will nanobot stay lightweight?

That’s the core principle. If something makes it too heavy, we won’t add it.

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Can I use nanobot for commercial projects?

Yes, MIT license allows commercial use.

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Will there be a hosted version?

No plans currently. Self-hosting keeps it simple.

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How can I sponsor development?

Contributions and feedback are the best support. Star the repo on GitHub!

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What if my favorite feature isn’t on the roadmap?

Open a discussion! If there’s community interest and it aligns with nanobot’s goals, it might get added.