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feat(prometheus): Implement self-evolving meta-agent for Blaxel Hackathon
🔥 PROMETHEUS: The Agent That Builds Itself Combines 6 cutting-edge AI research breakthroughs (Nov 2025): ## Core Components (~5,800 lines of Python) ### 1. Memory System (MIRIX-inspired) - 6-type memory: Core, Episodic, Semantic, Procedural, Resource, Vault - Based on arXiv:2507.07957 (+47% adaptation to new situations) ### 2. World Model (SimuRA-inspired) - Simulates actions before execution - Tree of Thoughts planning - Based on arXiv:2507.23773 (+124% task completion) ### 3. Tool Factory (AutoTools-inspired) - Generates tools automatically on-demand - Tests in sandbox before registration - Based on arXiv:2405.16533 ### 4. Reflection Engine (Reflexion-inspired) - Self-critique after every action - Generate → Critique → Improve loop - Based on arXiv:2303.11366 ### 5. Co-Evolution Loop (Agent0-inspired) - Curriculum Agent generates progressive tasks - Executor Agent learns through practice - Based on arXiv:2511.16043 (+18% math, +24% reasoning) ### 6. Orchestrator - Coordinates all subsystems - Multi-agent orchestration pattern - Based on Anthropic research ## Files Added - prometheus/ - Complete agent implementation (17 Python files) - prometheus/README.md - Detailed documentation (627 lines) - prometheus_entry.py - Blaxel entry point - blaxel.toml - Blaxel deployment configuration - modal_app.py - Modal hybrid deployment - deploy.sh - Multi-platform deployment script ## Hackathon Targets - 🏆 Blaxel Choice Award ($2,500) - 🏆 Modal Innovation Award ($2,500) - 🏆 Google Gemini ($30,000 API Credits) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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blaxel.toml

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# Blaxel Configuration
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# Juan-Dev-Code - MCP Hackathon Day 30
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#
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# Deploy: bl deploy
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# Run: bl run prometheus "your task"
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name = "prometheus"
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type = "agent"
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version = "1.0.0"
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[metadata]
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title = "PROMETHEUS: Self-Evolving Meta-Agent"
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description = """
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🔥 The Agent That Builds Itself
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Combines 6 cutting-edge AI research breakthroughs (Nov 2025):
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• Self-Evolution (Agent0, arXiv:2511.16043)
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- Improves through practice without external data
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- +18% math reasoning, +24% general reasoning
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• World Model (SimuRA, arXiv:2507.23773)
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- Simulates actions before execution
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- +124% task completion rate
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• 6-Type Memory (MIRIX, arXiv:2507.07957)
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- Persistent episodic, semantic, procedural memory
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- +47% adaptation to new situations
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• Tool Factory (AutoTools, arXiv:2405.16533)
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- Creates new tools automatically on-demand
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- Self-testing and improvement
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• Reflection Engine (Reflexion, arXiv:2303.11366)
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- Self-critique and continuous improvement
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- Learn from every interaction
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• Multi-Agent Orchestration
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- Coordinates all subsystems seamlessly
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- Based on Anthropic's research patterns
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Use Cases:
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- Complex code generation and review
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- Multi-step reasoning and analysis
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- Autonomous task execution with learning
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- Research and information synthesis
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"""
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author = "JuanCS Dev"
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tags = ["ai-agent", "self-evolving", "code-generation", "reasoning", "hackathon", "gemini", "nov-2025"]
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[entrypoint]
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prod = "python prometheus_entry.py"
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dev = "python -m prometheus.main --interactive"
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[env]
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# Set via environment variable or Blaxel secrets
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# GOOGLE_API_KEY = ""
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[resources]
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memory = "2Gi"
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cpu = "1"
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timeout = 600

blaxel.yaml

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# Blaxel Multi-Agent Configuration
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# Juan-Dev-Code - MCP Hackathon Day 30
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#
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# Deploy: bl deploy --name juan-code-reviewer
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# Run: bl run juan-code-reviewer "Review https://github.com/owner/repo/pull/123"
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#
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# Blaxel Choice Award: $2,500 Cash
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name: juan-code-reviewer
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version: 2.0.0
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description: |
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Multi-Agent Code Review System powered by Gemini 2.0
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ARCHITECTURE (Supervisor Pattern):
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┌───────────────────────────────────────┐
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│ SUPERVISOR AGENT │
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│ (Orchestrates & Synthesizes) │
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└───────────────┬───────────────────────┘
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┌─────────────┼─────────────┬─────────────┐
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▼ ▼ ▼ ▼
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Research Security Performance Style
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Agent Agent Agent Agent
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Features:
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- 4 specialized AI agents working in parallel
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- OWASP Top 10 security scanning
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- Performance bottleneck detection
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- Code quality scoring (0-100)
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- GitHub PR review support
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- Sandbox execution for safe code analysis
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# Runtime configuration
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runtime:
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python: "3.11"
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# Type definition
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type: agent
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# Environment variables (secrets managed by Blaxel)
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environment:
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- GOOGLE_API_KEY
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- GEMINI_API_KEY
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# Python dependencies
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dependencies:
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- google-generativeai>=0.8.0
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- httpx>=0.27.0
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- pydantic>=2.0.0
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# Agent entrypoint
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entrypoint: blaxel_agent:agent
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# Resource limits (higher for multi-agent processing)
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resources:
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memory: 4096 # 4GB RAM for parallel agents
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timeout: 600 # 10 minutes max for complex reviews
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cpu: 2.0
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# Tool definitions for Blaxel UI
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tools:
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- name: review_code
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description: Review code snippet using multi-agent system
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parameters:
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code:
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type: string
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description: The code to review
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required: true
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language:
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type: string
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description: Programming language (python, javascript, etc.)
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required: false
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default: "python"
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- name: review_file
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description: Review a file from the filesystem
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parameters:
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path:
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type: string
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description: Path to the file to review
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required: true
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- name: review_pr
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description: Review a GitHub Pull Request
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parameters:
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pr_url:
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type: string
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description: GitHub PR URL (e.g., https://github.com/owner/repo/pull/123)
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required: true
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# Metadata for Blaxel marketplace
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metadata:
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author: JuanCS Dev
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repository: https://github.com/juancs/juan-dev-code
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license: MIT
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tags:
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- code-review
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- multi-agent
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- security
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- performance
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- gemini
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- hackathon
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category: Development Tools
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innovation:
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- "Multi-agent architecture with 4 specialized AI agents"
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- "Parallel execution for fast code analysis"
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- "Comprehensive security scanning (OWASP Top 10)"
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- "Performance bottleneck detection with Big-O analysis"
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- "GitHub PR integration with sandbox execution"
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# Health check configuration
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health:
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endpoint: /health
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interval: 60
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timeout: 10
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# Scaling configuration (optimized for $250 budget)
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scaling:
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min_instances: 0
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max_instances: 3
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scale_down_delay: 300 # 5 minutes
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# Triggers for HTTP endpoints
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triggers:
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- type: http
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path: /review
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auth: api-key

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