🗺️ ExperienceMaker Future Roadmap

P0 - Ready-to-Use Experience Libraries

We aim to build curated experience libraries for complex scenarios, providing battle-tested best practices and lessons learned rather than simple documentation aggregation.

Just as financial analysts develop analytical frameworks, senior engineers establish coding standards, and education experts create teaching methodologies, AI agents can build professional experience repositories. Start your AI projects standing on the shoulders of giants.

Core Features:

Pre-built experience libraries for key domains (finance, programming, education, research, etc.)
Experience marketplace: community-driven experience sharing and exchange

P0 - Support for Rich Experience Formats

Expert knowledge extends beyond text to include debugged code, fine-tuned toolchains, and validated workflows. We aim to integrate diverse experience carriers:

Executable Code: Functions, code files, and scripts
Tool Integration: APIs, MCP configurations, and tool setups
Pipeline Templates: Agent execution pipelines and multi-step tool combinations

P1 - Experience Validation & Optimization

AI-powered analysis of experience usage patterns and effectiveness, with automatic quality optimization and cross-task validation feedback loops.

P2 - Universal Trajectory Experience Extraction

Raw Data Processing

Automatic extraction of valuable experiences from agent execution logs
Multimodal support: images, videos, and other formats

Vision

Transform valuable experience data from daily work into usable insights:

Communication techniques from emails
Optimization insights from code commits
Decision-making processes from meeting recordings

Enable AI to naturally become stronger through everyday work, rather than wasting real-world experience data due to format limitations.

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