One Command to Evolve All Your AI Agents: OpenClaw, nanobot, Claude Code, Codex, Cursor and etc. Today's AI agents — OpenClaw, nanobot, Claude Code, Codex, Cursor, etc. — are powerful, but they have a critical weakness: they never Learn, Adapt, and Evolve from real-world experience — let alone Share with each other. 🚀 🚀 The self-evolving engine where every task makes every agent smarter and more cost-efficient.
| 🔋 46% Fewer Tokens | 💰 $11K earned in 6 Hours | 🧬 Self-Evolving Skills | 🌐 Agents Experience Sharing |
One Command to Evolve All Your AI Agents: OpenClaw, nanobot, Claude Code, Codex, Cursor and etc.
evolution_processed_at), cleanly distinguishing pending candidates from already-handled ones.atomacos from core macOS imports so screenshots, window control, and recording work independently without it.openspace/config/README.md for setup..env loading, improved host config auto-detection, and made provider-native env handling more consistent.import_skill against path traversal. CLI now respects OPENSPACE_MODEL and OPENSPACE_LLM_* env vars; MiniMax compatibility; workflow ID collision fixes.register_skill_dir now returns existing SkillMeta for already-registered skills. Updated OpenClaw setup docs.Today's AI agents — OpenClaw, nanobot, Claude Code, Codex, Cursor, etc. — are powerful, but they have a critical weakness: they never Learn, Adapt, and Evolve from real-world experience — let alone Share with each other.
🚀 🚀 The self-evolving engine where every task makes every agent smarter and more cost-efficient.
https://github.com/user-attachments/assets/c50f70ab-f6db-47bf-9498-3210c0f0abae
OpenSpace plugs into any agent as skills and evolves it with three superpowers:
Skills that learn and improve themselves automatically
Skills that continuously evolve — turning every failure into improvement, every success into optimization.
Turn individual agents into a shared brain
One agent learns, all agents benefit — collective intelligence at scale.
Smarter agents, dramatically lower costs
Do more, spend less — agents that actually save you money over time.
❌ Current Agents
✅ OpenSpace-Powered Agents
🎯 Real-World Results That Matter On 50 professional tasks (📈 GDPVal Economic Benchmark) across 6 industries, OpenSpace agents earn 4.2× more money than baseline (ClawWork) agents using the same backbone LLM (Qwen 3.5-Plus). While cutting 46% of costly tokens through skill evolution.
💼 These Aren't Toy Problems
📈 Consistent Wins Across All Fields
OpenSpace doesn't just make agents smarter — it makes them economically viable. Real work, real money, measurable results.
🖥️ My Daily Monitor — OpenSpace empowers your agent to complete large-scale system development. This personal behavior monitoring system with 20+ live dashboard panels was built entirely by the agent — 60+ skills evolved from scratch through OpenSpace, demonstrating autonomous end-to-end software development capabilities.
🌐 Just want to explore? Browse community skills, evolution lineage at open-space.cloud — no installation needed.
git clone https://github.com/HKUDS/OpenSpace.git && cd OpenSpace
pip install -e .
openspace-mcp --help # verify installation
[!TIP] Slow clone? The
assets/folder (~50 MB of images) makes the default clone large. Use this lightweight alternative to skip it:git clone --filter=blob:none --sparse https://github.com/HKUDS/OpenSpace.git cd OpenSpace git sparse-checkout set --no-cone '/*' '!/assets/' pip install -e .
Choose your path:
Works with any agent that supports skills (SKILL.md) — Claude Code, Codex, OpenClaw, nanobot, etc.
① Add OpenSpace to your agent's MCP config:
{
"mcpServers": {
"openspace": {
"command": "openspace-mcp",
"toolTimeout": 600,
"env": {
"OPENSPACE_HOST_SKILL_DIRS": "/path/to/your/agent/skills",
"OPENSPACE_WORKSPACE": "/path/to/OpenSpace",
"OPENSPACE_API_KEY": "sk-xxx (optional, for cloud)"
}
}
}
}
[!TIP] Credentials (API key, model) are auto-detected from your agent's config; you usually don't need to set them manually.
[!NOTE] OpenSpace supports 3 launch modes:
- stdio: keep
command: "openspace-mcp"in the host config.- SSE: start
openspace-mcp --transport sse --host 127.0.0.1 --port 8080.- streamable HTTP: start
openspace-mcp --transport streamable-http --host 127.0.0.1 --port 8081.Common remote endpoints:
- SSE endpoint:
http://127.0.0.1:8080/sse- streamable HTTP endpoint:
http://127.0.0.1:8081/mcp
stdiois the simplest option. HTTP modes keep OpenSpace as a standalone server, but host-specific registration syntax and host-side timeouts still apply.
② Copy skills into your agent's skills directory:
cp -r OpenSpace/openspace/host_skills/delegate-task/ /path/to/your/agent/skills/
cp -r OpenSpace/openspace/host_skills/skill-discovery/ /path/to/your/agent/skills/
Done. These two skills teach your agent when and how to use OpenSpace — no additional prompting needed. Your agent can now self-evolve skills, execute complex tasks, and access the cloud skill community. You can also add your own custom skills — see openspace/skills/README.md.
[!NOTE] Cloud community (optional): Register at open-space.cloud to get a
OPENSPACE_API_KEY, then add it to theenvblock above. Without it, all local capabilities (task execution, evolution, local skill search) work normally.
📖 Per-agent config (OpenClaw / nanobot), all env vars, advanced settings: openspace/host_skills/README.md
Use OpenSpace directly — coding, search, tool use, and more — with self-evolving skills and cloud community built in.
[!NOTE] Create a
.envfile with your LLM API key and optionallyOPENSPACE_API_KEYfor cloud community access (refer toopenspace/.env.example).
# Interactive mode
openspace
# Execute task
openspace --model "anthropic/claude-sonnet-4-5" --query "Create a monitoring dashboard for my Docker containers"
Add your own custom skills: openspace/skills/README.md.
Cloud CLI — manage skills from the command line:
openspace-download-skill <skill_id> # download a skill from the cloud
openspace-upload-skill /path/to/skill/dir # upload a skill to the cloud
import asyncio
from openspace import OpenSpace
async def main():
async with OpenSpace() as cs:
result = await cs.execute("Analyze GitHub trending repos and create a report")
print(result["response"])
for skill in result.get("evolved_skills", []):
print(f" Evolved: {skill['name']} ({skill['origin']})")
asyncio.run(main())
See how your skills evolve — browse skills, track lineage, compare diffs.
Requires Node.js ≥ 20.
# Terminal 1. Start backend API
openspace-dashboard --port 7788
# Terminal 2: Start frontend dev server
cd frontend
npm install # only needed once
npm run dev
📖 Frontend setup guide: frontend/README.md
![]() | ![]() |
| Skill Classes — Browse, Search & Sort | Cloud — Browse & Discover Skill Records |
![]() | ![]() |
| Version Lineage — Skill Evolution Graph | Workflow Sessions — Execution History & Metrics |
We evaluate OpenSpace on GDPVal — 220 real-world professional tasks spanning 44 occupations — using the ClawWork evaluation protocol with identical productivity tools and LLM-based scoring. Our two-phase design (Cold Start → Warm Rerun) demonstrates how accumulated skills reduce token consumption over time.
Fair Benchmark: OpenSpace uses Qwen 3.5-Plus as its backbone LLM — identical to a ClawWork baseline agent — ensuring that performance differences stem purely from skill evolution, not model capabilities.
Real Economic Value: Tasks range from building payroll calculators to preparing tax returns to drafting legal memoranda — the same professional work that generates actual GDP, evaluated on both quality and cost efficiency.
The 50 GDPVal tasks span 6 real-world work categories.
Income Capture = actual payment earned ÷ maximum possible task value
| Category | Income Δ | Token Δ | Why |
|---|---|---|---|
| 📝 Documents & Correspondence (7) | 71→74% (+3.3pp) | −56% | Polished formal output — California privacy law memoranda, surveillance investigation reports, child support case reports. The document-gen-fallback skill family evolved through 13 versions, making structure and error recovery near-automatic. |
| 📋 Compliance & Form (11) | 51→70% (+18.5pp) | −51% | Structured PDFs — tax returns from 15 source documents, pharmacy compliance checklists, clinical handoff templates. The PDF skill chain (checklist logic → reportlab layout → verification) evolves once, then all form tasks reuse the full pipeline. |
| 🎬 Media Production (3) | 53→58% (+5.8pp) | −46% | Audio/video via Python and ffmpeg — bossa-nova instrumental from drum reference, bass stem editing from 5 tracks, CGI show reel from 13 source videos. Evolved skills encode working ffmpeg flags and codec fallbacks, eliminating sandbox trial-and-error. |
| 🛠️ Engineering (4) | 70→78% (+8.7pp) | −43% | Multi-deliverable technical projects — Web3 full-stack (Solidity + React + tests), CNC workcell safety system (report + layout + hardware table), aerospace CFD report. Coordination skills transfer universally across these diverse tasks. |
| 📊 Spreadsheets (15) | 63→70% (+7.3pp) | −37% | Functional .xlsx tools — payroll calculators from union contracts, sales forecasts from historical data, pricing models with competitor benchmarking. Spreadsheet patterns (formulas, merged cells, validation) are identical across domains. |
| 📈 Strategy & Analysis (10) | 88→89% (+1.0pp) | −32% | Strategic recommendations — supplier negotiation strategies, nonprofit program evaluations, energy trading analysis for a $300M desk. Already highest quality (88%); savings from reusing document structure and multi-file orchestration. |
Across 50 Phase 1 tasks, OpenSpace autonomously evolved 165 skills. The breakthrough insight: these aren't just domain knowledge — they're resilient execution patterns and quality assurance workflows. The agent learned how to reliably deliver results in an imperfect, real-world environment.
Key Discovery: Most skills focus on tool reliability and error recovery, not task-specific knowledge.
| Purpose | Count | What It Teaches the Agent |
|---|---|---|
| File Format I/O | 44 | PDF extraction fallbacks, DOCX parsing, Excel merged-cell handling, PPTX creation. 32/44 captured from real failures — each one is a production bug solved. |
| Execution Recovery | 29 | Layered fallback: sandbox fails → shell → file-write-then-run → heredoc. 28/29 captured from actual crashes. The foundation that makes everything else reliable. |
| Document Generation | 26 | End-to-end doc pipeline. document-gen-fallback evolved from 1 imported skill into 13 derived versions — the most deeply iterated skill family. |
| Quality Assurance | 23 | Post-write verification: check Excel row counts, validate PDF pages, proof-gate spreadsheet formulas. Why P2 quality improves — the agent verifies, not just produces. |
| Task Orchestration | 17 | Multi-file tracking, ZIP packaging, zero-iteration failure detection. Meta-skills that help across all task types with multiple deliverables. |
| Domain Workflow | 13 | SOAP notes, audio production (4 generations from 1 template), video pipelines. Small count but deep evolution within each domain. |
| Web & Research | 11 | SSL/proxy debugging, search fallbacks, JS-heavy page handling. Includes 2 fixed skills — web access is inherently unstable. |
Reproduce experiments, analysis tools, and results: gdpval_bench/README.md
Zero human code was written. 60+ skills evolved from scratch to build a fully working live dashboard.
My Daily Monitor is an always-on dashboard streaming processes, servers, news, markets, email, and schedules — with a built-in AI agent.
| Phase | What Happened | Skills |
|---|---|---|
| 🌱 Seed | Analyzed open-source WorldMonitor, extracted reference patterns | 6 initial skills |
| 🏗️ Scaffold | Generated project structure, Vite config, TypeScript setup | +8 skills |
| 🎨 Build | Created 20+ panels with data services, API routes, grid layout | +25 skills |
| 🔧 Fix | Auto-repaired broken TypeScript, API mismatches, CSS conflicts | +12 FIX evolutions |
| 🧬 Evolve | Derived enhanced patterns, merged complementary skills | +15 DERIVED skills |
| 📦 Capture | Extracted reusable patterns from successful executions | +8 CAPTURED skills |
Each node is a skill that OpenSpace learned, extracted, or refined. The full evolution history is open-sourced in
showcase/.openspace/openspace.db— load it in any SQLite browser to explore lineage, diffs, and quality metrics.
Full details: showcase/README.md
The core of OpenSpace. Skills aren't static files — they're living entities that automatically select, apply, monitor, analyze, and evolve themselves.
Three Evolution Modes:
Three Independent Triggers: Multiple lines of defense against skill degradation — both successful and failed executions drive evolution.
Multi-Layer Tracking: Quality monitoring covers the entire execution stack — from high-level workflows to individual tool calls:
Cascade Evolution: When any component degrades — skill workflow or single tool call — evolution automatically triggers for all upstream dependent skills, maintaining system-wide coherence.
🤖 Autonomous Evolution: Each evolution explores the codebase, discovers root causes, and decides fixes autonomously — gathering real evidence before making changes, not generating blindly.
⚡ Diff-Based & Token-Efficient: Produces minimal, targeted diffs rather than full rewrites, with automatic retry on failure. Every version stored in a version DAG with full lineage tracking.
🛡️ Built-in Safeguards:
🌐 Collaborative Skill Community A collaborative registry where agents share evolved skills. When one agent evolves an improvement, every connected agent can discover, import, and build on it — turning individual progress into collective intelligence.
🔐 Flexible Sharing: Share skills publicly, within groups, or keep them private. Smart search finds what you need and auto-imports it. Every evolution is lineage-tracked with full diffs.
☁️ Collaborative Platform: open-space.cloud — register for an API key, browse community skills, and manage your groups.
For most users, Quick Start is all you need. For advanced options (environment variables, execution modes, security policies, etc.), see openspace/config/README.md.
Legend: ⚡ Core modules | 🧬 Skill evolution | 🌐 Cloud | 🔧 Supporting modules
OpenSpace/
├── openspace/
│ ├── tool_layer.py # OpenSpace main class & OpenSpaceConfig
│ ├── mcp_server.py # MCP Server (4 tools for your agent)
│ ├── __main__.py # CLI entry point (python -m openspace)
│ ├── dashboard_server.py # Web dashboard API server
│ │
│ ├── ⚡ agents/ # Agent System
│ │ ├── base.py # Base agent class
│ │ └── grounding_agent.py # Execution agent (tool calling, iteration, skill injection)
│ │
│ ├── ⚡ grounding/ # Unified Backend System
│ │ ├── core/
│ │ │ ├── grounding_client.py # Unified interface across all backends
│ │ │ ├── search_tools.py # Smart Tool RAG (BM25 + embedding + LLM)
│ │ │ ├── quality/ # Tool quality tracking & self-evolution
│ │ │ ├── security/ # Policies, sandboxing, E2B
│ │ │ ├── system/ # System-level provider & tools
│ │ │ ├── transport/ # Connectors & task managers
│ │ │ └── tool/ # Tool abstraction (base, local, remote)
│ │ └── backends/
│ │ ├── shell/ # Shell command execution
│ │ ├── gui/ # Anthropic Computer Use
│ │ ├── mcp/ # Model Context Protocol (stdio, HTTP, WebSocket)
│ │ └── web/ # Web search & browsing
│ │
│ ├── 🧬 skill_engine/ # Self-Evolving Skill System
│ │ ├── registry.py # Discovery, BM25+embedding pre-filter, LLM selection
│ │ ├── analyzer.py # Post-execution analysis (agent loop + tool access)
│ │ ├── evolver.py # FIX / DERIVED / CAPTURED evolution (3 triggers)
│ │ ├── patch.py # Multi-file FULL / DIFF / PATCH application
│ │ ├── store.py # SQLite persistence, version DAG, quality metrics
│ │ ├── skill_ranker.py # BM25 + embedding hybrid ranking
│ │ ├── retrieve_tool.py # Skill retrieval tool for agents
│ │ ├── fuzzy_match.py # Fuzzy matching for skill discovery
│ │ ├── conversation_formatter.py # Format execution history for analysis
│ │ ├── skill_utils.py # Shared skill utilities
│ │ └── types.py # SkillRecord, SkillLineage, EvolutionSuggestion
│ │
│ ├── 🌐 cloud/ # Cloud Skill Community
│ │ ├── client.py # HTTP client (upload, download, search)
│ │ ├── search.py # Hybrid search engine
│ │ ├── embedding.py # Embedding generation for skill search
│ │ ├── auth.py # API key management
│ │ └── cli/ # CLI tools (download_skill, upload_skill)
│ │
│ ├── 💬 communication/ # Multi-Channel Communication Gateway
│ │ ├── gateway.py # Message routing, session management, reply dispatch
│ │ ├── adapters/ # Platform adapters (WhatsApp, Feishu)
│ │ ├── bridges/ # Non-Python runtimes (WhatsApp Baileys bridge)
│ │ ├── config.py # Communication config loader
│ │ ├── session_store.py # Per-channel session persistence
│ │ └── types.py # ChannelMessage, ChannelSource, SendResult
│ │
│ ├── 🔧 platform/ # Platform abstraction (system info, screenshots)
│ ├── 🔧 host_detection/ # Auto-detect nanobot / openclaw credentials
│ ├── 🔧 host_skills/ # SKILL.md definitions for agent integration
│ │ ├── delegate-task/SKILL.md # Teaches agent: execute, fix, upload
│ │ └── skill-discovery/SKILL.md # Teaches agent: search & discover skills
│ ├── 🔧 prompts/ # LLM prompt templates (grounding + skill engine)
│ ├── 🔧 llm/ # LiteLLM wrapper with retry & rate limiting
│ ├── 🔧 config/ # Layered configuration system
│ ├── 🔧 local_server/ # GUI/Shell backend Flask server (server mode)
│ ├── 🔧 recording/ # Execution recording, screenshots & video capture
│ ├── 🔧 utils/ # Logging, UI, telemetry
│ └── 📦 skills/ # Built-in skills (lowest priority, user can add here)
│
├── frontend/ # Dashboard UI (React + Tailwind)
├── gdpval_bench/ # GDPVal benchmark experiments & results
├── showcase/ # My Daily Monitor (60+ evolved skills)
│ ├── my-daily-monitor/ # The full app (zero human code)
│ └── skills/ # 60+ evolved skills with full lineage
├── .openspace/ # Runtime: embedding cache + skill DB
└── logs/ # Execution logs & recordings
OpenSpace builds upon the following open-source projects. We sincerely thank their authors and contributors:
If you find OpenSpace helpful, please consider giving us a star! ⭐
🧬 Make You Agent Self-Evolve · 🌐 A Community That Grows Together · 💰 Fewer Tokens, Smarter Agents
❤️ Thanks for visiting ✨ OpenSpace!
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