Poorcontextbreaksyouragents
Ratel is the open-source context-engineering SDK to make agents leaner, more accurate, and easier to debug
Trusted by leaders at
Keep your agents focused and effective
Accuracy on local models with small context windows
Net tokens by loading only the context that matters
Token cost on frontier models with stable accuracy
Why Ratel
Agents fail when their context rots and no model can fix it
Token bills triple. Reliability drops. Ratel builds managed infra so you stay focus on the core, we let it run smoothly.
Supports any stack, any model
Seamlessly integrates in your stack, running on both cloud and local models
Reduces token bills
Only the right context at each turn is loaded, maxing token efficiency
Increases agent reliability
Every new data point improve agents instead of bloating context
Use Cases
The context-engineering SDK to make agents leaner, more accurate, and easier to debug
qwen3.5 (local) · 100-tool catalog
single-tool selection accuracy
Right context, not all context
Context windows fill with stale tools, drifting memory, dead history. Ratel injects what's needed, when it's needed
Shared context, not silos
One agent learns. The next starts from zero. Ratel unifies the fleet's context: memory, skills, tools, history
Understand behaviour beyond traces
Traces show every step. Ratel tells you why, because it's the layer that routed every choice your agent made
Try now Ratel
At Ratel we also ship OSS products that helped us when building agents daily. We thought it might help you to!
- pnpm add @ratel-ai/sdk
- ~70% higher accuracy on Qwen 3.5
- 62% more accuracy on Opus 4.7 with 83% less tokens
Got agents in production?Let's talk.
