Mini-SWE-Agent Interface Overview

• Mini-SWE-Agent Interface is a lightweight, modular agent-computer interface designed for repository-level code exploration and question answering.
• It employs a ReAct-style iterative reasoning loop that integrates semantic search, file reading, and context pruning to enable multi-file analysis.
• Empirical results on the SWE-QA benchmark indicate significant improvements in multi-hop dependency analysis and code comprehension over conventional methods.

A Mini-SWE-Agent Interface refers to a lightweight, modular agent-computer interface (ACI) that enables a LLM (LM)-based agent to autonomously navigate, query, and reason over entire software repositories for realistic engineering tasks. This class of interface synthesizes principles from recent “agentic” AI research—emphasizing ReAct-style iterative reasoning, explicit tool use, action/observation cycles, and targeted context management—to facilitate complex code understanding, multi-file reasoning, and long-range dependency analysis at repository scale.

1. Definition, Motivation, and Scope

The Mini-SWE-Agent Interface is designed to expose just enough agentic capability to address repository-level or cross-file software engineering queries without excessive workflow overhead. It abstracts away unnecessary system-level complexity, instead focusing on actions and context most relevant for question answering, architecture investigation, and design rationale exploration.

Motivation for such an interface stems from the limitations of prior benchmarks (e.g., CoSQA, CodeQA), which only assess narrow code snippet reasoning and fail to model the holistic, multi-hop, multi-file information needs seen in real-world software development (Peng et al., 18 Sep 2025). The Mini-SWE-Agent Interface is thus engineered to:

• Provide high-level, LM-friendly actions for efficient exploration and semantic retrieval across entire repositories.
• Support compositional, stepwise reasoning that iteratively augments agent context.
• Enable robust grounding of answers in file structure, code content, and architectural dependencies.
• Prevent context overflow and hallucination via adaptive context pruning and relevance filtering.

2. Agent Architecture and Workflow

The canonical Mini-SWE-Agent Interface follows a ReAct-style (Yao et al., 2022) agentic framework, decomposed as:

1. Initialization: The agent parses the NL query and issues a broad semantic search over the codebase for initial context (retrieval-augmented generation, RAG).
2. Iterative Plan-Act-Observe Loop: At each iteration, the agent:
   • Reasons over the accumulated context to formulate a plan or hypothesis.
   • Chooses from a modular action space (e.g., semantic search, file read, structure extraction).
   • Executes the action, observes the output, and updates its working memory.
   • Prunes or augments the context as needed, optimizing for coverage and relevance.
   • Checks for evidence sufficiency or step limit to determine when to finalize.
3. Finalization: Upon accumulating sufficient multi-faceted evidence, the agent synthesizes a comprehensive, reference-grounded answer.

Sample Agent Loop (algorithmic form):

context = [] context.append(broad_search(query, repo)) for i in range(N): thought = reason(context, query) action = select_action(thought) output = execute(action) context.append(output) if sufficient_evidence(context, query) or i == N: break answer = synthesize(context, query) return answer

[see Algorithm 1 in (Peng et al., 18 Sep 2025)]

3. Action Space and Tooling

The Mini-SWE-Agent Interface exposes a core set of actions, chosen for their balance of informativeness, granularity, and LM interpretability:

Action	Functionality	Motivation
ReadFile	Read arbitrary file content (cat, grep)	Directs agent to fine-grained code/data
GetRepoStructure	Retrieve project directory structure (tree)	Summarizes architecture, informs search
SearchContent	Semantic code/content search via embeddings	Enables cross-file, conceptual retrieval
Finish	Synthesize and return answer	Concludes plan-act loop

This modular action space ensures that the agent can mix structure-driven exploration (e.g., seeing high-level module layouts) with fine semantic probes (e.g., function/method usage, plugin registry tracing), as required by real-world engineering queries.

4. Context Construction and Retrieval Strategies

To withstand repository scale and the diversity of engineering queries, Mini-SWE-Agent Interfaces deploy flexible context augmentation and retrieval techniques:

• Function-Chunked RAG: Index code at function/method granularity for fine-tuned, minimal semantic retrieval.
• Sliding-Window RAG: Divide files into overlapping chunks to provide both local and global code context.
• Iterative, Selective Augmentation: Rather than static context selection, agents iteratively hypothesize, retrieve, and prune, maximizing multi-hop reasoning efficiency and minimizing irrelevant or hallucinated context.

Notably, the agent is driven by evidence sufficiency: it accumulates only the specific context pieces needed to answer a given query, rather than flooding the context window.

5. Supported Question Types and Capabilities

The interface is tuned for answering:

• Intention Understanding: E.g., design rationale, architectural contracts, the role and logic of major modules.
• Cross-file Reasoning: Aggregation and tracing of functionality or usage spanning multiple files.
• Multi-hop Dependency Analysis: Tracing of propagation flows (data, control, or structural) through several layers/files.
• Procedural and Locational Queries: “How does X work?”, “Where is Y implemented and how does it interact with Z?”

These demands cannot be addressed by snippet-level or retrieval-only approaches; iterative, tool-using plan-act-observe is essential.

6. Performance and Empirical Insights

On the SWE-QA benchmark (576 repository-level Q&A; 12 open-source Python repos) (Peng et al., 18 Sep 2025), the Mini-SWE-Agent (prototype: SWE-QA-Agent) outperforms both direct prompting and conventional RAG baselines:

• Claude 3.7 Sonnet + SWE-QA-Agent: 47.82/50 (vs 38.18 direct, 41.44 chunked RAG).
• Completeness and Reasoning Dimensions: Substantial gains over baselines.
• Human Evaluation: Experts rate agentic approach higher for correctness and depth.

Strengths are pronounced on “what”/“why” conceptual and architectural queries; procedural/multi-hop/“how”/“where” questions remain challenging, particularly when relevant information is deeply dispersed or encoded in non-standard fashion. Open-source models lag behind proprietary ones, but the iterative agentic interface narrows this gap.

7. Implementation Considerations and Best Practices

For effective Mini-SWE-Agent Interfaces:

• Implement a ReAct-style action loop, exposing only LM-friendly, well-documented actions.
• Provide semantic search (embedding-based), repo structure extraction, file reading tools as primitives.
• Gate loop iterations and context augmentation with quantitative sufficiency and iteration limits, to avoid endless cycles and context overflow.
• Accumulate and prune context adaptively, preserving salient (“citation-worthy”) evidence for the final answer.
• Use the LLM itself for final answer synthesis, referencing specific supporting context and reasoning steps.
• Evaluate using multi-dimensional rubrics: correctness, completeness, clarity, reasoning, and evidence anchoring.

Typical computational requirements are dominated by semantic search indexing and LLM inference steps. Resource scaling may be tuned via chunk size, max iteration count, embedding/model selection, and context window management.

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In summary, the Mini-SWE-Agent Interface operationalizes a modern, modular, and evidence-driven agent-computer abstraction well-suited for realistic repository-level code QA, enabling robust, multi-file reasoning and answer synthesis. It advances beyond retrieval or one-shot QA paradigms by supporting iterative, action-oriented exploration—validating the design on complex SE benchmarks and offering a replicable, extensible blueprint for future engineering agent systems (Peng et al., 18 Sep 2025).