Why pay for the same reasoning twice? Most AI systems call an LLM every time — even when the work has already been understood and validated. KORA takes a different approach: New work learns. Known work runs.

KORA Doctor tells you where to cut it. Built at Krako. Open source on top of AUDR. AI agents are making far more model calls than they need to. KORA Doctor finds where that waste is happening. Built at Krako. Open source on top of AUDR. Inference should be earned, not assumed. github.com/Krako-Labs/kora-d… Measure → diagnose → eliminate unnecessary inference.

A 12-step agent workflow. **0 provider calls.** KORA's offline Agent Workflow Optimization example routes: → 4 deterministic → 2 cache hits → 3 tool-needed → 3 provider-needed → 0 actual provider calls It's small, reproducible, and easy to extend. Developers: run it, inspect the routing logic, and contribute. 🔗 github.com/Krako-Labs/KORA/t… #OpenSource #AI #KORA

12-step agent workflow. Only 3 steps actually needed the model. KORA routes classification, validation, transforms, and policy checks to deterministic handlers — cache reuse and tool-needed steps handled separately. Fully offline, reproducible: python3 examples/agent_workflow_optimization/run.py Open source, contributors welcome 🔗 github.com/Krako-Labs/KORA

Ran 7 RAG queries through KORA. Zero hit the model. 2 answered deterministically, 1 from cache, 2 flagged retrieval-needed, 2 provider-needed — but 0 provider calls actually made. Offline, no API key, fully reproducible: python3 examples/rag_routing/run.py Open source, contributors welcome 🔗 github.com/Krako-Labs/KORA

Every AI request has a tradeoff. Should it optimize for: 💰 Cost ⚡ Speed 🎯 Correctness Not every task needs an LLM. Use deterministic logic where possible. Escalate only when reasoning adds value. How do you balance the tradeoff? #AI #LLM #SystemDesign #OpenSource #KORA

Every merged PR makes open source stronger. You don't need to build a huge feature to contribute. Start with: • Docs • Bug fixes • Reviews • Examples Your first contribution could help hundreds of developers. Join KORA: github.com/Krako-Labs/KORA #OpenSource #GitHub #BuildInPublic #KORA

The fastest way to understand an open source project? Read the README. KORA's README covers: → Architecture → Deterministic routing → Request flow → Quick start Good docs are part of the product. 📖 github.com/Krako-Labs/KORA #OpenSource #Documentation #GitHub #KORA

Not every request needs an LLM. Use deterministic logic for: • Validation • Business rules • Routing • Cache • Permissions • APIs Call the model only when reasoning is required. That's the philosophy behind KORA. What would you never send to an LLM? #AI #LLM #OpenSource #SoftwareArchitecture #KORA

Small bug. Big improvement. We refined KORA's retry behavior to make execution more reliable. ✅ Retry recoverable failures ✅ Avoid duplicate execution ✅ Preserve deterministic routing ✅ Reduce unnecessary compute Read the PR: github.com/Krako-Labs/KORA #OpenSource #AI #BuildInPublic #DeveloperTools #KORA

AI latency isn't just model inference. Every request spends time in: → Validation → Routing → Retrieval → Tools → LLM → Response Measure every stage before optimizing. See the benchmarks and reproduce them yourself. github.com/Krako-Labs/KORA #AI #Performance #OpenSource #LLM #KORA

Most AI apps call an LLM first. What if that was the last step instead? Validate → Route → Cache → Tools → Retrieval → LLM (only when needed) Smarter routing means lower costs, faster responses, and more predictable AI systems. That's the philosophy behind KORA. ⭐ Explore the project: github.com/Krako-Labs/KORA #AI #OpenSource #LLM #DeveloperTools #BuildInPublic #KORA

Weekly KORA OSS Roundup 🚀 This week: • New commits • Docs improved • Architecture updates • Bug fixes • More deterministic routing Building AI infrastructure in the open. ⭐ Star the repo: github.com/Krako-Labs/KORA #OpenSource #GitHub #AI #BuildInPublic #KORA

Every AI request shouldn't go straight to an LLM. KORA's Dispatcher decides the best execution path first: → Logic → Cache → Tools → Retrieval → LLM (only if needed) Deterministic routing = lower cost + better reliability. Read the architecture: github.com/Krako-Labs/KORA #AI #OpenSource #LLM #KORA

AI failures shouldn't be mystery logs. KORA publishes every routing failure as structured JSON with validation results, execution metadata, and failure reasons. Debug faster. Build reliable AI. GitHub: github.com/Krako-Labs/KORA #AI #LLM #OpenSource #KORA

☀️ Sunday is for builders. Join the Krako community to learn, build, and contribute with developers from around the world. 💬 Discord discord.gg/G6yea8XV ✈️ Telegram t.me/+ISn5ykcIBj0zODE0 💼 LinkedIn linkedin.com/company/krakola… One community. One mission. Build the future of decentralized AI.