I have a confession. For months, I was obsessed with prompt engineering.
I'd spend hours tweaking, rephrasing, and chaining prompts, convinced that the perfect input would unlock my AI agent's potential. But it always felt like I was building a sandcastle. One unexpected wave, a new piece of information, a forgotten detail, and the whole thing would collapse. My agent had amnesia, and my clever prompts were just a band-aid.
If you've been building with LLMs, you've probably felt this too. That nagging feeling that despite their power, our agents are still brittle, forgetful, and disconnected from the real world.
The "Aha!" Moment: It's Not the Prompt, It's the Context
Andrej Karpathy nailed it: “The future isn’t prompt engineering. It’s context engineering.” Then it hit me. We were all staring at the wrong problem. The magic wasn't in the question; it was in the information we provided before the question.
This is the core idea of Context Engineering, a term you're hearing more and more from people like Andrej Karpathy and Tobi Lütke. It’s the delicate art and science of building a rich, dynamic "world" for your AI to operate in, not just giving it better instructions.
Building the Infrastructure We Wished Existed: Context Space
This realization led us to build Context Space. We needed more than just another library; we needed infrastructure.
Context Space is a production-grade, open-source engine that handles all the gnarly parts of context management. It's the missing data plane for AI agents.
Connect to Anything, Securely: It integrates with 14+ services like GitHub, Slack, Notion, and HubSpot using proper OAuth 2.0 flows. No more pasting API keys into a YAML file and praying it doesn't leak.
Built for Production: It’s not a script; it’s a service. Written in Go and designed for enterprise deployment with Docker, Kubernetes, and HashiCorp Vault for security.
A Foundation for the Future: We started with robust integrations, but our roadmap is all about intelligence: native agent protocol (MCP) support, semantic context retrieval, and smart compression to make the most of every token.
Join the Context Engineering Movement
This is just the beginning. Our vision is to build the definitive infrastructure for a new generation of context-aware AI. But we can't do it alone.
If this idea resonates with you, if you're tired of brittle, amnesiac agents and want to build something that truly works, I invite you to join us.
Check out the project on GitHub, give it a star to show your support, and help us build the future of AI. Let's stop building toys and start building tools.
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