Remember Your Trace: Memory-Guided Long-Horizon Agentic Framework for Consistent and Hierarchical Repository-Level Code Documentation

2026-07-13 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Expert, extended

Summary

MemDocAgent is a novel long-horizon agentic framework designed for automated, consistent, and hierarchical repository-level code documentation. It addresses critical limitations in existing systems, such as redundant information retrieval and cross-document inconsistencies, which average 50% and 13% respectively. The framework integrates two key components: Dependency-Aware Traversal Guiding, which establishes a hierarchical processing order, and Memory-Guided Agentic Interaction, where an agent uses a shared RepoMemory for Read, Write, and Verify operations to accumulate and reuse prior work. Evaluated against four open-source and two closed-source baselines, MemDocAgent achieved superior performance across completeness, truthfulness, helpfulness, and a new "information sufficiency" metric. It improved completeness by 15.9%, truthfulness by 9.7%, and helpfulness by 9.9% over strong baselines using Qwen3-Coder, and reduced retrieval time by 41% compared to DocAgent. Code and data are available at https://github.com/bsy99615/MemDocAgent.

Key takeaway

For AI Engineers building automated code documentation systems, you should consider adopting a long-horizon agentic approach with shared memory. This framework significantly improves documentation consistency and reduces redundant processing, as demonstrated by MemDocAgent's 41% retrieval time reduction and superior quality metrics. Implement dependency-aware traversal and cross-document verification to ensure hierarchical coherence and factual accuracy across your codebase.

Key insights

Long-horizon agentic frameworks with shared memory improve code documentation consistency and efficiency.

Principles

• Documenting in dependency order ensures context.
• Shared memory reduces redundant retrieval.
• Cross-document verification prevents inconsistencies.

Method

MemDocAgent uses Dependency-Aware Traversal Guiding for hierarchical order and Memory-Guided Agentic Interaction with RepoMemory (Read, Write, Verify actions) to generate and validate documentation across component, module, and repository levels.

In practice

• Implement a shared memory for agentic tasks.
• Order documentation generation by code dependencies.
• Use NLI models for cross-document consistency checks.

Topics

• Automated Code Documentation
• Long-Horizon Agents
• Large Language Models
• Repository-Level Documentation
• Memory-Guided Agents
• Code Generation Benchmarks

Code references

• bsy99615/MemDocAgent

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Engineer

Related on AIssential

• The Ceiling of the Loop — Append-only persistence without judgment creates context debt, not true memory, leading to system degradation.
• Drawing on Memory: Dual-Trace Encoding Improves Cross-Session Recall in LLM Agents — Dual-trace memory encoding enhances LLM agent recall by adding contextual "scene traces" to factual memories.
• AGORA: An Archive-Grounded Benchmark for Agentic Workplace Document Reasoning — Archive-grounded reasoning in large, messy document collections remains a significant challenge for LLM agents, even with advanced benchmarks.
• .agent folder is making claude code 10x better... — Optimizing LLM context through structured documentation and sub-agents dramatically improves coding agent performance.
• Can LLM Agents Sustain Long-Horizon Organizational Dynamics? — Structured simulation memory is key for LLM agents to sustain coherent, long-horizon organizational dynamics and produce grounded artifacts.
• Agent memory resolved? — A git-like memory framework significantly enhances coding agent performance and context persistence across sessions.

See Counsel's argued verdicts on the open AI decisions leaders are weighing →

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.