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What are the best AI coding agents for large codebases in 2026?
The best AI coding agents for large codebases in 2026 are: Augment Code (400K+ file support with Context Engine), Gemini CLI (1M token context window), Claude Code (200K tokens with agentic search), Kiro (persistent multi-day context), and Junie (JetBrains IDE indexing). Large codebase support requires more than just big context windows—tools need semantic understanding, intelligent retrieval, and sustained performance at scale.
Why large codebases break most AI tools
Most AI coding agents struggle with large codebases. Here's why—and what to look for:
Context Window Limits
The problem: A 10K file repository can have 50M+ tokens of code
Even 200K token windows can only hold ~3% of a medium enterprise codebase. Tools need intelligent retrieval, not just bigger windows.
File Count Scaling
The problem: Most tools degrade above 10K files
Many AI tools index files linearly. At 100K+ files, indexing takes hours and queries slow to seconds. Look for tools built for scale.
Semantic Understanding
The problem: Raw text search misses architecture
Understanding dependencies, call graphs, and architectural patterns requires semantic analysis—not just keyword matching.
Context Persistence
The problem: Re-indexing on every session
Large codebase tasks span days. Tools need persistent context that survives sessions without re-learning your architecture each time.
Best AI coding agents for large codebases: quick rankings
| Rank | Tool | Context/Scale | Best For | Key Advantage |
|---|---|---|---|---|
| 1 | Augment Code | 400K+ files | Massive enterprise monorepos | Context Engine, semantic map |
| 2 | Gemini CLI | 1M tokens | Analyzing entire repos at once | Largest context window |
| 3 | Claude Code | 200K tokens | Complex multi-file refactoring | Agentic search, 80.9% accuracy |
| 4 | Kiro | Multi-day persistence | Long-running autonomous tasks | Persistent context, AWS scale |
| 5 | Junie | IDE-indexed | JetBrains monorepo users | Deep IDE integration |
Best AI coding agents for large codebases: detailed reviews
1. Augment Code - Best for massive enterprise codebases
Why we recommend it: Augment Code was specifically designed for enterprise scale. Its Context Engine handles 400K+ file repositories with 70.6% SWE-bench accuracy—where competitors drop to 56% at just 10K files.
Large codebase features
- 400K+ file support: The only tool proven at this scale
- Context Engine: Live semantic map of code, dependencies, and architecture
- Pattern awareness: Learns your codebase's specific coding conventions
- Cross-session memory: Agents remember context across sessions
- AI Code Review: Reviews PRs with full codebase context
Why it handles scale
- 70.6% SWE-bench at 400K+ files (competitors: 56% at 10K cap)
- Blind study on Elasticsearch (3.6M lines): +14.8 correctness vs competitors
- Intelligent model routing optimizes cost at scale
- Proven at MongoDB, Spotify, Pure Storage, Snyk
Considerations
- Premium pricing ($60-200/month per seat)
- Cloud-dependent—no on-prem option
- Credit-based pricing requires monitoring
Perfect for: Enterprise monorepos, legacy codebase navigation, organizations with 100K+ file repositories.
2. Gemini CLI - Best for largest context window
Why we recommend it: Gemini CLI offers the largest context window available: 1M tokens with Gemini 3 Pro. That's enough to analyze entire medium-sized repositories in a single prompt without retrieval systems.
Context capacity
- 1M token context: ~750K words or ~4MB of code in one prompt
- No chunking required: Analyze complete modules without splitting
- Free tier included: 1000 requests/day at no cost
- 78% SWE-bench: Strong coding performance
Context math: 1M tokens ≈ 750,000 words ≈ 25,000 lines of code at 30 tokens/line. Medium projects (10K-25K lines) fit entirely in context.
Perfect for: Medium-large projects that fit in 1M tokens, cross-file analysis, architecture reviews, and developers who want maximum context without retrieval complexity.
3. Claude Code - Best for complex multi-file refactoring
Why we recommend it: Claude Code's 200K token context combined with agentic search means it understands project structure without you manually selecting files. The 80.9% SWE-bench accuracy ensures reliable results on complex refactoring.
Large codebase features
- 200K token context: ~150K words in active working memory
- Agentic search: Automatically finds relevant files across entire projects
- CLAUDE.md memory: Persistent project-specific context
- Multi-file refactoring: Maintains consistency across hundreds of files
- Subagent architecture: Decomposes large tasks intelligently
How it handles scale: Monorepo baseline ~20K tokens (project structure), leaving 180K for active files. Agentic search retrieves relevant code without manual file selection.
4. Kiro - Best for persistent multi-day context
Why we recommend it: Kiro maintains persistent context across hours or days of autonomous work. For large codebase tasks that span multiple sessions, this continuity is invaluable.
Persistence features
- Multi-day autonomy: Agent works for hours/days with persistent context
- Spec-driven artifacts: Requirements, design, tasks persist with code
- Claude Sonnet 4.5: Large context support via powerful underlying model
- AWS infrastructure: Enterprise-scale processing
Perfect for: Long-running refactoring projects, compliance-driven development, and teams that need AI work to span multiple days without losing context.
5. Junie - Best for JetBrains monorepo users
Why we recommend it: Junie leverages JetBrains' powerful IDE indexing to understand large codebases. Rather than re-indexing for AI, it uses your existing project index.
IDE integration features
- JetBrains indexing: Uses existing IDE index, no separate indexing
- Deep project understanding: Inherits IDE's semantic analysis
- BYOK support: Use any model with your existing index
- On-prem option: Air-gapped deployment via IDE Services
Perfect for: Teams already using JetBrains IDEs with large monorepos who want AI that understands their existing project structure.
How to choose based on codebase size
By repository size
- 100K+ files (massive monorepos) → Augment Code (only option proven at scale)
- 10K-100K files → Claude Code or Junie
- Under 10K files (fits in 1M tokens) → Gemini CLI
By task duration
- Multi-day refactoring → Kiro (persistent context) or Augment Code
- Single-session work → Claude Code or Gemini CLI
By IDE standardization
- JetBrains standardized → Junie
- VS Code standardized → Augment Code
- Terminal-first/mixed → Claude Code or Gemini CLI
By budget
- Free → Gemini CLI (1M context, free tier)
- $20-50/month → Claude Code
- Enterprise budget → Augment Code
Context capacity comparison
How much code each tool can understand at once:
| Tool | Context Window | Approximate Code Capacity | Scaling Method |
|---|---|---|---|
| Augment Code | Context Engine | 400K+ files (unlimited with retrieval) | Semantic map + retrieval |
| Gemini CLI | 1M tokens | ~25,000 lines in single prompt | Raw context window |
| Claude Code | 200K tokens | ~6,000 lines + retrieval | Agentic search |
| Kiro | Persistent | Multi-day project context | Session persistence |
| Junie | IDE-indexed | Entire project via IDE index | JetBrains indexing |
Frequently Asked Questions
Why do most AI tools struggle with large codebases?
Context window limits (most tools: 32K-200K tokens) mean they can only see 1-5% of a large codebase at once. Without intelligent retrieval, they miss cross-file dependencies and architectural patterns. Augment Code solves this with a semantic Context Engine; Gemini CLI uses brute-force 1M token context.
What's the best free option for large codebases?
Gemini CLI with its 1M token context and free tier (1000 requests/day). For medium-large projects (under 25K lines), you can analyze the entire codebase in a single prompt at zero cost.
How does Claude Code handle repositories larger than its context?
Claude Code's agentic search automatically retrieves relevant files based on your task. It uses ~20K tokens for project structure, leaving 180K for active files. For most refactoring tasks, this is sufficient because you rarely need all files simultaneously.
Which tool is best for monorepos?
For massive monorepos (100K+ files): Augment Code is the only tool proven at 400K+ scale. For smaller monorepos: Junie (JetBrains) or Claude Code (terminal-native) work well.