Struggling with flaky AI agents? This framework retries smartly for massive gains – and it’s dev-friendly.
AI agents ghosting on complex tasks? EnCompass from MIT fixes that by backtracking LLM calls with structured search, squeezing out the best outputs without rewriting your agent.[2]
The system decouples search strategy from agent logic, letting you swap beam search or whatever boosts perf. On code tasks, a two-level beam search delivered 15-40% accuracy jumps at just 16x LLM calls.[2]
Perfect for devs building agents in CI/CD, data analysis, or RAG – turn inconsistent LLMs into reliable workhorses. Experiment effortlessly to optimize your stack.
Unlike rigid agent frameworks, EnCompass is plug-and-play, outperforming baselines across repos. As agents explode in dev tools, this abstraction is gold; Carnegie Mellon profs call it foundational for search-driven dev.[2]
Get started: Open-source soon? Prototype with their strategies in LangChain/CrewAI. Test on your workflows – what’s your best search algo? Future: human-AI collab on hardware design.
Source: MIT CSAIL