SkillGraph: An Open-Source Agentic Framework for Efficient AI Workflows

SkillGraph is an experimental, open-source agentic framework designed to improve cost efficiency and control in AI agent development. Built by Tejas Parthasarathi Sudarshan, it reimagines agent architecture by introducing "skills"—sophisticated, self-contained units that handle specific tasks with their own logic, workflows, and state management. These skills can orchestrate multiple tools, act as subagents, and support complex multi-turn interactions. SkillGraph features a subject-object memory architecture for robust state tracking, smart caching using Anthropic prompt caching and Redis, and built-in reliability through LLM fallback chains. While not yet production-ready, it offers a functional and cost-effective alternative to traditional tool-calling frameworks, making it ideal for developers and researchers exploring advanced AI agent design.

Key Features:

• Skill-Based Architecture: Sophisticated task units with independent logic and workflows.
• Multi-Turn Workflows: Native support for complex, multi-step interactions.
• Subject-Object Memory: Advanced state tracking for consistent agent behavior.
• Smart Caching: Optimized with Anthropic prompt caching and Redis for conversation history.
• Built-In Reliability: LLM fallback chains ensure robust performance.
• Cost Efficiency: Designed to be cheaper to run than traditional frameworks.
• Open Source: Released under the Apache 2.0 license, enabling transparency and customization.

Pricing: SkillGraph is free to use and open-source, with no paid tiers or subscription models.

Conclusion: SkillGraph is a promising, developer-focused framework that brings greater control, efficiency, and modularity to AI agent development, offering a powerful foundation for experimentation and innovation in agentic systems.