Microsoft · 2026-06-15 · notable
FastContext — Microsoft's Explore subagent cuts coding-agent tokens by 60%
Microsoft and SJTU release FastContext: a small repository-exploration subagent that does parallel code search and hands focused context to a larger coding model. MIT-licensed code and 4B SFT and RL checkpoints are out.
Small 4B subagent that searches the repo for a bigger coding model, lifting SWE-bench resolution by up to 5.5% while cutting main-agent tokens by 60%.
What is it?
FastContext is a research release from Microsoft Research and Shanghai Jiao Tong University. It splits coding-agent work into two roles: a cheap exploration model that finds the right files and snippets, and a stronger problem-solving model that does the actual reasoning over that focused context.
How does it work?
The explorer issues many code searches in parallel and returns short file-and-line evidence instead of dumping full files. The team trained explorers from 4B to 30B parameters with supervised fine-tuning followed by reinforcement learning, optimizing them to surface the right code with the fewest tokens. The main coding model then runs over only the explorer's distilled findings.
Why does it matter?
Coding agents hit a wall on long repos because context fills with irrelevant files. A specialised explorer raises SWE-bench Verified resolution by up to 5.5% while shaving 60% off the main model's token bill — useful for any agent that pays per-token. FastContext slots in as a callable tool for existing coding agents.
Who is it for?
coding-agent builders, SWE-bench researchers, anyone running multi-step LLM agents over large codebases
Try it
https://github.com/microsoft/fastcontextKey numbers
- resolution gain pct: 5.5
- main token savings pct: 60
- param range: 4B-30B
- stars: 361