DeepCode: Open Agentic Coding

Recent advances in Large Language Models (LLMs) have enabled the shift from coding assistants to autonomous software engineers. However, high-fidelity document-to-codebase synthesis—such as reproducing scientific papers—remains challenging due to the fundamental conflict between information overload and the finite context constraints of LLMs. In this work, we introduce DeepCode, a fully autonomous framework that addresses this challenge through principled information-flow management. DeepCode maximizes task-relevant signals under strict context budgets via four orchestrated operations: source compression via blueprint distillation, structured indexing using stateful memory, conditional knowledge injection via retrieval-augmented generation, and closed-loop error correction. Extensive evaluations on PaperBench demonstrate that DeepCode achieves state-of-the-art performance among the evaluated automated agents, outperforms leading commercial agents on a 5-paper subset, and exceeds the reported PhD-level human Best@3 baseline on the PaperBench 3-paper subset under the Replication Score metric. Our source code is available at: https://anonymous.4open.science/r/DeepCode-C464.