A comprehensive analysis of code retrieval approaches for AI coding agents, comparing semantic/RAG-based search against traditional methods (grep, keyword search) and agentic tool use. Focus: empirical data and benchmarks over opinions.
| Approach | Accuracy | Speed | Cost | Scalability | Best For |
|---|---|---|---|---|---|
| Semantic RAG | High (+12-40%) | Medium | Medium | High | Large/complex codebases |
| Grep/Keyword | Moderate | Fast | Low | Medium | Well-organized code, exact matches |
| Agentic Tool Use | Highest | Slow | High | Low | Complex multi-file reasoning |
| Hybrid (RAG + Grep) | Highest | Medium | Medium | High | Production systems |
Source: Cursor Blog - Improving Agent with Semantic Search
| Metric | Result | Notes |
|---|---|---|
| Average accuracy improvement | +12.5% | Range: 6.5% to 23.5% depending on model |
| Code retention increase | +0.3% overall | +2.6% on large codebases (1000+ files) |
| Dissatisfied follow-ups reduction | -2.2% | When semantic search available |
Key finding: Semantic search benefits scale with codebase size. The 2.6% improvement on large codebases vs 0.3% overall shows diminishing returns for small projects.
Source: CodeRAG-Bench Paper, Project Page
Improvement with retrieved context vs no retrieval:
| Task Type | Improvement | Notes |
|---|---|---|
| MBPP (basic programming) | +15.6-17.8% | Even outperformed oracle setups |
| ODEX (open-domain) | +20.3-40.1% | Unfamiliar library tasks |
| DS-1000 (data science) | +20.3-40.1% | Complex API usage |
| SWE-Bench | +27.4% (GPT-4o) | Repository-level tasks |
| ODEX-hard subset | +6.9% (GPT-4o) | Hardest retrieval scenarios |
Critical gap: 9-point gap remains between RAG performance and oracle (gold documents), indicating retrieval quality limits downstream generation.
Source: SWE-bench, Scale AI Leaderboard
| Period | Best Performance | Method |
|---|---|---|
| 2024 (early) | 1.96% | BM25 retrieval + Claude 2 |
| 2024 (with oracle) | 4.8% | Gold files provided |
| 2024 (SWE-agent) | 12.47% | Agentic approach (GPT-4 Turbo) |
| 2024 (Devin) | 13.86% | Autonomous agent |
| 2025 (Verified) | ~75% | Modern agents + verified subset |
| 2025 (Full) | 29% | OpenHands (#1 open source) |
| 2025 (Pro) | 23.26% | GPT-5 |
Key insight: Agent-based approaches (12.47%+) dramatically outperformed pure RAG baselines (1.96-3.8%) on SWE-bench, a 3-6x improvement.
Source: CoIR Paper
| Model | Mean Score | Variance | Notes |
|---|---|---|---|
| Voyage-Code-002 | 56.26 | High | Best overall, weaker generalization |
| BGE-M3 | Moderate | Lowest | Best robustness |
| E5-Mistral | High | - | 1840ms latency, 2.3GB index |
| E5-Base | Moderate | - | 7.4ms latency, 0.3GB index |
Trade-off: 250x latency difference between fast (E5-Base) and accurate (E5-Mistral) models.
Source: Augment Code Comparison
| Metric | Copilot | Cody |
|---|---|---|
| Usable code rate | 68% | 82% |
| Context scope | Single file | Full repository |
| Failure mode | Missing cross-file dependencies | Slower response |
Root cause of gap: Copilot couldn't locate imports and internal utilities not visible in immediate context.
Source: Kilo.ai Blog
| Metric | Semantic Search | Keyword Search |
|---|---|---|
| Average result rank | 3.5 | 6.0 |
| Top-5 hit rate | 76% | Lower |
| MRR improvement (Samsung) | +41% | Baseline |
Source: Jolt AI Blog
Tested: Cursor, Windsurf, GitHub Copilot, Claude Code, OpenAI Codex, Augment, Jolt Codebases: Django, Grafana, Kubernetes (6 closed PRs each)
| Tool Type | Speed | Thoroughness | Accuracy |
|---|---|---|---|
| Pure vector (Copilot) | <1 min | Low | Low (wrong files) |
| Agentic (Codex, Claude Code) | 3-5 min | High | High |
| Hybrid | Medium | High | High |
Trade-off: 3-5x slower for agentic but significantly more accurate file discovery.
Source: Jason Liu - Why Grep Beat Embeddings
"We explored adding various embedding-based retrieval tools, but found that for SWE-bench tasks this was not the bottleneck – grep and find were sufficient."
Why grep worked for SWE-bench:
- Repositories smaller than real-world codebases
- 90% of problems solvable by good engineer in <1 hour
- Agent persistence compensated for simpler tools
- Code has distinctive keywords (function names, error messages)
But: "In practice, we find that embedding-based tools are critical to deliver a great product experience."
Source: Nuss and Bolts
Using GPT-3.5-mini to expand grep keywords improved performance nearly 10x by generating semantically-derived terms not present in original queries.
Source: Milvus Blog
| Approach | Token Usage | Notes |
|---|---|---|
| Grep-only | High (bloat) | Returns all matching lines without semantic filtering |
| Semantic + AST chunking | -40% tokens | Same recall, preserves function boundaries |
| Use Case | Recommended Approach |
|---|---|
| Known function/variable name | Grep |
| Exact error message | Grep |
| "How does authentication work?" | Semantic |
| Finding usage patterns | Semantic |
| Cross-file dependencies | Semantic/Agentic |
| Small, well-organized codebase | Grep sufficient |
| Large enterprise codebase | Semantic essential |
| Multimodal (diagrams, images) | Semantic only |
Source: LightOn Blog
Test scenario: 1,000 pages knowledge base (~600K tokens), 1,000 daily requests
| Approach | Cost Multiplier |
|---|---|
| Long context | 8-82x more expensive |
| RAG | Baseline |
RAG also provides better latency due to processing only relevant chunks.
Source: Chroma Research - Context Rot
- LLM performance degrades as context length increases
- Irrelevant information introduces noise weakening attention to relevant portions
- GPT-4/5: Dramatic performance degradation around 400K characters (~133K tokens)
- Response times jump 50x near context limits
Implication: Larger context windows don't guarantee better performance; retrieval remains essential.
Source: Voyage AI Blog
Voyage-3: 7.55% better than OpenAI v3 large while 2.2x cheaper
| Optimization | Benefit |
|---|---|
| Distillation | 50-80% cost reduction, <3% recall drop |
| Quantization | 40% latency reduction, <2% recall drop |
| Approach | Quality | Speed | Cost | Reliability |
|---|---|---|---|---|
| Traditional RAG | Decent | Excellent | Low | Low (no correction) |
| Agent + Grep | Excellent | Poor | High | High |
| Agent + Embeddings | Excellent | Poor | High | High |
Source: RAGFlow Blog
Source: Anthropic Engineering
Agents processing results in execution environments: 98.7% token reduction vs returning raw data to model context.
For repository-level code generation, single-document retrieval insufficient. Multi-hop approaches:
- Retrieve initial candidates
- Extract entities/evidence
- Retrieve additional documents based on discovered relationships
Source: ACL Anthology
Combines:
- Custom embedding model (trained on agent traces)
- Grep for exact matches
- Turbopuffer vector database
Result: Best outcomes from combination, not either alone.
| Method | Use Case |
|---|---|
| Min-max normalization | Comparable score distributions |
| Reciprocal Rank Fusion (RRF) | Incompatible score distributions |
| Cross-encoder reranking | Final precision boost |
Formula: RRF_score = 1/(rank + k) where k typically = 60
Source: Microsoft Azure Docs
- Start with grep for well-structured, smaller codebases
- Add semantic search when:
- Codebase exceeds 1,000 files
- Developers ask conceptual questions
- Cross-file reasoning needed
- Use hybrid for production systems
- Expose both as agent tools for maximum flexibility
| Claim | Statistic | Source |
|---|---|---|
| Semantic search accuracy gain | +12.5% average | Cursor A/B tests |
| Large codebase benefit | +2.6% code retention | Cursor (1000+ files) |
| Cody vs Copilot accuracy | 82% vs 68% | Augment comparison |
| RAG vs long context cost | 8-82x cheaper | LightOn |
| Semantic vs keyword rank | 3.5 vs 6.0 | DeepCS study |
| Top-5 hit rate (semantic) | 76% | DeepCS |
| MRR improvement | +41% | Samsung deployment |
| Agent token reduction | 98.7% | Anthropic MCP |
| SWE-bench agent vs RAG | 12.47% vs 1.96% | SWE-agent vs baseline |
| Retrieval gain (open-domain) | +20-40% | CodeRAG-Bench |
| Context rot threshold | ~133K tokens | GPT-4/5 testing |
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Neither grep nor semantic search is universally better - context determines optimal approach
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Semantic search advantages increase with:
- Codebase size (inflection point ~1,000 files)
- Code complexity and architectural depth
- Natural language queries about intent
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Grep advantages:
- Speed and simplicity
- Exact matches (function names, errors)
- No index maintenance overhead
- Sufficient for SWE-bench-style tasks
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Production best practice: Hybrid systems combining both, exposed as agent tools
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The retrieval gap: 9-point gap between RAG and oracle on SWE-bench indicates retrieval quality remains a bottleneck for hardest problems
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Agents change the equation: Persistence and multi-step reasoning can compensate for simpler retrieval tools, but at cost of speed and tokens