Start here
This Space is for engineers evaluating Slipstream in a real agent stack.
- Inspect the protocol surface in
UCR Explorer. - Validate concrete messages in
Conformance Lab. - Drop the adapter into LangGraph from
LangGraph Starter. - Use the dataset/model tab last, after you have measured fallback rate and routing quality.
You do not need to train a model to adopt Slipstream. Start with the runtime,
route on Force:Object, and train only if your workload needs better
quantization than the built-in path provides.
For the release narrative, benchmarks, and positioning, use the website: https://slipstream.making-minds.ai
| Resource | Link |
|---|---|
| Website | https://slipstream.making-minds.ai |
| GitHub | https://github.com/anthony-maio/slipcore |
| PyPI | https://pypi.org/project/slipcore/ |
| Paper | https://doi.org/10.5281/zenodo.18063451 |
| Dataset | https://huggingface.co/datasets/anthonym21/slipstream-tqt |
| Reference model | https://huggingface.co/anthonym21/slipstream-glm-z1-9b |