Self-supervised method for learning when to retrieve contextual information from a code repository speeds up code completion times by 70% while increasing accuracy.
RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation
The task of repository-level code completion is to continue writing the unfinished code based on a broader context of the repository. While for automated code completion tools, it is difficult to utilize the useful information scattered in different files. We propose RepoCoder, a simple, generic, and effective framework to address the challenge. It streamlines the repository-level code completion process by incorporating a similarity-based retriever and a pre-trained code language model in an iterative retrieval-generation pipeline. RepoCoder makes effective utilization of repository-level information for code completion and has the ability to generate code at various levels of granularity. Moreover, we propose a new benchmark RepoBench, which consists of the latest and high-quality real-world repositories covering line, API invocation, and function body completion scenarios. Experimental results indicate that RepoCoder significantly improves the In-File completion baseline by over 10% in all settings and consistently outperforms the vanilla retrieval-augmented code completion approach. Furthermore, we validate the effectiveness of RepoCoder through comprehensive analysis, providing valuable insights for future research. Our source code and benchmark will be publicly available after the paper review.
Retrieval-Augmented Code Generation: A Survey with Focus on Repository-Level Approaches
Recent advancements in large language models (LLMs) have substantially improved automated code generation. While function-level and file-level generation have achieved promising results, real-world software development typically requires reasoning across entire repositories. This gives rise to the challenging task of Repository-Level Code Generation (RLCG), where models must capture long-range dependencies, ensure global semantic consistency, and generate coherent code spanning multiple files or modules. To address these challenges, Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm that integrates external retrieval mechanisms with LLMs, enhancing context-awareness and scalability. In this survey, we provide a comprehensive review of research on Retrieval-Augmented Code Generation (RACG), with an emphasis on repository-level approaches. We categorize existing work along several dimensions, including generation strategies, retrieval modalities, model architectures, training paradigms, and evaluation protocols. Furthermore, we summarize widely used datasets and benchmarks, analyze current limitations, and outline key challenges and opportunities for future research. Our goal is to establish a unified analytical framework for understanding this rapidly evolving field and to inspire continued progress in AI-powered software engineering.
Evaluating AGENTS.md: Are Repository-Level Context Files Helpful for Coding Agents?
A widespread practice in software development is to tailor coding agents to repositories using context files, such as AGENTS.md, by either manually or automatically generating them. Although this practice is strongly encouraged by agent developers, there is currently no rigorous investigation into whether such context files are actually effective for real-world tasks. In this work, we study this question and evaluate coding agents' task completion performance in two complementary settings: established SWE-bench tasks from popular repositories, with LLM-generated context files following agent-developer recommendations, and a novel collection of issues from repositories containing developer-committed context files. Across multiple coding agents and LLMs, we find that context files tend to reduce task success rates compared to providing no repository context, while also increasing inference cost by over 20%. Behaviorally, both LLM-generated and developer-provided context files encourage broader exploration (e.g., more thorough testing and file traversal), and coding agents tend to respect their instructions. Ultimately, we conclude that unnecessary requirements from context files make tasks harder, and human-written context files should describe only minimal requirements.
Building an Open-Source Alternative to Cursor with Code Context - Milvus Blog
Code Context—an open-source, MCP-compatible plugin that brings powerful semantic code search to any AI coding agent, Claude Code and Gemini CLI, IDEs like VSCode, and even environments like Chrome.
Semble: Fast and Accurate Code Search for Agents
Fast and Accurate Code Search for Agents. Uses ~98% fewer tokens than grep+read
AI Code Reviews | CodeRabbit | Try for Free.
AI-first pull request reviewer with context-aware feedback, line-by-line code suggestions, and real-time chat.
The Coding Agent Data Deal
On user data control, coding agents as retrievers, and the value of your coding transcripts
The Coding Agent Data Deal
On user data control, coding agents as retrievers, and the value of your coding transcripts
OpenWiki: Open Source Repo Documentation for Coding Agents
OpenWiki generates and maintains codebase documentation so coding agents can find the repo context they need without loading everything into one instruction file.
best coding - Reddit Search!
Lost in the Middle: How Language Models Use Long Contexts
While recent language models have the ability to take long contexts as input, relatively little is known about how well they use longer context. We analyze the performance of language models on two tasks that require identifying relevant information in their input contexts: multi-document question answering and key-value retrieval. We find that performance can degrade significantly when changing the position of relevant information, indicating that current language models do not robustly make use of information in long input contexts. In particular, we observe that performance is often highest when relevant information occurs at the beginning or end of the input context, and significantly degrades when models must access relevant information in the middle of long contexts, even for explicitly long-context models. Our analysis provides a better understanding of how language models use their input context and provides new evaluation protocols for future long-context language models.
The Complete Guide to Claude Code V4 — The Decipherist
The community asked, Anthropic delivered: 85% context reduction via MCP Tool Search, custom agents, session teleportation, and the January 2026 revolution.
Overview - Claude Code Docs
Claude Code is an agentic coding tool that reads your codebase, edits files, runs commands, and integrates with your development tools. Available in your terminal, IDE, desktop app, and browser.
A beginner's guide to learning to code with GitHub Codespaces
When you’re new to coding, it’s easy to get stuck completing endless tutorials. You can apply what you’ve learned (and learn even more) through GitHub Codespaces. The best part is you don’t need a powerful computer to get started.
Cody - Sourcegraph docs
Documentation for Sourcegraph, the code intelligence platform.