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

Automated code documentation is essential for modern software development, providing contextual grounding for human developers and coding agents navigating large codebases. The gist: MemDocAgent, a long-horizon agentic framework that generates documentation within a single, integrated context spanning the entire repository by combining Dependency-Aware Traversal Guiding and Memory-Guided Agentic Interaction, achieves superior performance over existing systems by eliminating repeated retrieval and reducing inconsistency.

The Problem Addressed

Existing repository-level approaches decompose a repository into components, causing redundant retrieval and conflicting descriptions across documents while lacking hierarchical structure. This leads to an average overlap of 50% in source-file retrieval and a cross-document inconsistency rate of 13% across existing systems. Furthermore, current methods treat documentation as short-horizon tasks, failing to accumulate and reconcile repository-wide knowledge across many interdependent steps. This results in documentation that is either too local to explain system design or too coarse for concrete code understanding.

MemDocAgent Framework

MemDocAgent frames repository-level documentation as a cumulative long-horizon process, where a single agent processes every documentation unit within one continuous trajectory and reuses information accumulated along the way. This framework is supported by two core components:

1. Dependency-Aware Traversal Guiding: This module predetermines a traversal order that respects dependency relations and the granularity hierarchy, ensuring that each unit is documented after its dependencies and child units, thereby guaranteeing context-grounded generation and repository coverage.

2. Memory-Guided Agentic Interaction: The agent interacts with RepoMemory, a shared memory that accumulates prior work traces throughout the trajectory. This memory stores retrieved components, intermediate reasoning, and generated documentation to improve efficiency and cross-document consistency.

Agent Interaction Loop

The agent operates through an iterative loop where every action is grounded in interaction with RepoMemory. The four primary actions are:

READ:

adaptively decides whether additional context is needed for each sub-task. This involves structured requests, including internal requests to check memory by component IDs or external requests for natural-language queries about external algorithms or libraries.

WRITE:

When sufficient context is collected, the agent generates a draft document guided by a granularity-specific format defined in its system prompt.

VERIFY:

This action involves self-evaluation and cross-document conflict verification. The agent performs a self-evaluation on factual consistency, completeness, and helpfulness (averaging three scores), and then applies an NLI-based inconsistency detection tool to compare the draft against documents already committed to RepoMemory, checking for contradictions based on dependency relations.

FINISH:

Once the documentation passes verification, this action commits the document to the corresponding granularity store and refreshes local context for the next sub-task.

Evaluation and Results

MemDocAgent was evaluated across four dimensions: completeness, truthfulness, helpfulness, and information sufficiency. Empirically, MemDocAgent achieved the best performance across all four criteria, demonstrating that its documentation captures both the breadth and depth required to support real software development workflows. Its improvements are consistent across all documentation granularities (component, module, and repository levels), maintaining high quality even as the repository size increases.

Efficiency Gains

The framework demonstrates significant efficiency improvements over baselines. For example, MemDocAgent reduces read time by 41% compared to DocAgent by eliminating redundant source-file retrieval. This efficiency is attributed to RepoMemory, which preserves the documentation information accumulated along the long-horizon trajectory in an efficiently retrievable form, replacing repeated raw context reads with focused memory lookups. The framework also shows a lower average generation time per document than DocAgent and achieves a lower API cost per document on GPT-5-mini.

Conclusion

MemDocAgent successfully produces hierarchical documentation that is more useful for developers and coding agents in real software development workflows. The combination of dependency-aware traversal guiding and memory-guided interaction ensures that the resulting documentation is consistent, complete, and sufficient to reproduce the original source code. It proves that a long-horizon agentic approach is effective for solving complex repository-level documentation tasks.

Limitation

The primary limitation identified is the computational resource requirement due to the growing input context and generated outputs over a long trajectory. Additionally, because the agent depends on model reasoning ability, there is a minimal risk of entering a repeated loop without completing a sub-task, although this occurs in only 0.08% of components in experiments. Future work should focus on more efficient trajectory control and adaptive stopping criteria to mitigate these risks.

References

[1] Dayu Yang et al.

Based on the scientific paper MemDocAgent: Long-Horizon Agentic Framework for Consistent and Hierarchical Repository-Level Code Documentation, here are specific improvements that can be made to AI systems, along with what those improved systems will be capable of doing:

)1. Transition from Isolated Component Processing to Integrated Contextual Reasoning (The Core Architectural Shift):

The current limitation in existing systems is treating documentation as a short-horizon, component-local task, leading to redundant retrieval and contradictory descriptions. By implementing MemDocAgent's core mechanism—combining Dependency-Aware Traversal Guiding with Memory-Guided Agentic Interaction—the AI system moves from independent summarization to a single, continuous trajectory over the entire repository.

)2. Hierarchical Documentation Generation (Multi-Level Synthesis):

The improved AI system will be capable of producing documentation that spans three distinct granularities:

• From fine-grained component behaviors (function/method level).

• To module responsibilities and internal design patterns.

• Up to repository-wide architectural patterns and overall system purpose.

This allows the AI to generate documentation that is simultaneously too local for system design or too coarse for concrete code understanding, effectively providing a multi-level view of the codebase.

)3. Guaranteed Consistency via Memory and Verification Loops:

The system will incorporate a shared memory (RepoMemory) and an explicit verification step (VERIFY) within the agent's loop.

• The agent will reuse prior retrievals and outputs across iterations, eliminating redundant source-file retrieval (as shown by the 41% reduction in read time).

• The VERIFY action uses NLI-based cross-document conflict detection against already committed documents to detect and correct contradictions in real-time.

This results in documentation with significantly reduced cross-document inconsistency rates (reduced by 75.5% compared to existing systems) and higher truthfulness, ensuring that architectural descriptions remain coherent throughout the entire documentation process.

)4. Enhanced Information Sufficiency for Code Reconstruction:

The system will be explicitly optimized for information sufficiency, defined as whether the documentation alone can reproduce the original source code. This is achieved by:

• Integrating a quantitative metric (Information Sufficiency Score) that measures code regeneration performance (Pass@k and CodeBLEU) using downstream models fed only the generated documentation.

• Progressively enriching context during generation by feeding C+M+R context (component + module + repository docs) to the final code-generation model.

This means the AI system will produce documentation that is not just descriptive, but functionally sufficient, enabling developers or subsequent agents to reliably reconstruct the original implementation from scratch.

)5. Optimized Traversal and Efficiency:

The AI system will utilize a Dependency-Aware Traversal Guiding mechanism (Algorithm 1 & 2) to predetermine the exact order of documentation tasks based on dependency and granularity hierarchy. This ensures that every unit is documented only after its dependencies are complete, guaranteeing structural completeness from the start.

Furthermore, by efficiently managing context through memory lookups instead of repeated raw retrieval, the system achieves substantial efficiency gains (e.g., 41% reduction in read time for component-level tasks).

)6. Adaptive and Self-Correcting Iteration:

The agent will be equipped with a sophisticated reasoning loop (Thought-Action-Observation) that includes adaptive refinement based on verification feedback. If the VERIFY action fails, the agent autonomously decides whether to perform a targeted READ for more context or a WRITE revision, effectively self-correcting documentation flaws until the strict quality threshold (0.90) is met across consistency, completeness, and helpfulness scores.

This improved AI system will be capable of:

1. Generating comprehensive, multi-level documentation (Component -> Module -> Repository).

2. Maintaining perfect factual consistency across all generated documents via real-time cross-document verification.

3. Ensuring that the output is functionally sufficient for code regeneration using quantitative metrics (Pass@k/CodeBLEU).

4. Operating efficiently by reusing verified knowledge from long-horizon memory, drastically reducing redundant computational costs compared to existing baselines.