civyk-repoix 0.4.0

Civyk Repo Index - Codebase indexing service for AI coding agents

Civyk Repo Index

Semantic code intelligence for AI coding agents — Give your AI assistant deep understanding of your codebase through the Model Context Protocol (MCP).

If you find this useful, please consider supporting the project!

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Local-First, Private, Secure

Your code never leaves your machine. Civyk Repo Index is a fully local MCP server:

• 100% offline — No cloud services, no API calls, no telemetry
• Your data stays yours — All indexes and caches stored locally in SQLite
• Works air-gapped — Perfect for proprietary codebases and enterprise environments
• Free binaries — Compiled binaries available via PyPI at no cost

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Why Civyk Repo Index?

AI coding assistants have limited context windows. They can't read entire codebases. Civyk Repo Index provides token-budgeted semantic code intelligence:

• Symbol-aware search — Find functions, classes, and types instantly
• Smart context packs — Auto-select relevant code within token budgets
• Relationship tracking — Understand calls, imports, and inheritance
• Real-time indexing — Always up-to-date with your code changes
• Multi-language — Python, TypeScript, JavaScript, Java, Go, C#, Rust, Ruby, PHP
• Branch-aware — Separate indexes per git branch
• AI Context Cache — Persist code understanding across sessions, save 80-90% tokens

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Quick Start

Installation

pip install civyk-repoix

Setup for Your AI Agent

cd /path/to/your/project

# Interactive setup (recommended)
civyk-repoix setup

# Or configure specific agent
civyk-repoix setup --ai claude        # Claude Code
civyk-repoix setup --ai cursor-agent  # Cursor
civyk-repoix setup --ai windsurf      # Windsurf
civyk-repoix setup --ai copilot       # GitHub Copilot
civyk-repoix setup --ai opencode      # OpenCode
civyk-repoix setup --ai kilocode      # Kilo Code
civyk-repoix setup --ai antigravity   # Antigravity

# Configure all supported agents at once
civyk-repoix setup --all

Verify

civyk-repoix query index-status

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MCP Tools

36 tools for code intelligence.

Category	Tools
Core	index_status, build_context_pack, search_symbols, get_symbol, get_references, get_components, get_api_endpoints, get_dependencies, force_reindex
Navigation	get_file_symbols, get_definition, get_callers
Discovery	list_files, get_file_imports, search_code
Git	get_recent_changes, get_hotspots, get_branch_diff
Analysis	get_dead_code, find_circular_dependencies, analyze_impact, get_tests_for, get_code_for_test, get_duplicate_code, get_tool_performance_stats
Advanced	get_type_hierarchy, get_related_files, find_similar
AI Cache	store_understanding, recall_understanding, get_understanding_stats, invalidate_understanding
Context	build_delta_context_pack, map_trace_to_symbols, get_recommended_tests, build_doc_pack

Tip: Use recall_understanding before reading files — cached analysis saves 80-90% of tokens.

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AI Context Cache — Your AI Remembers

The killer feature: Your AI assistant remembers what it learned about your code.

Traditional AI coding assistants forget everything when you start a new chat or session. With Civyk Repo Index's AI Context Cache, understanding persists:

Monday:    AI reads auth.py → analyzes → stores understanding
Tuesday:   New chat → AI recalls cached understanding → no file read needed!
Wednesday: You modify auth.py → cache auto-invalidates → AI re-analyzes

Why This Matters

Without Cache	With Cache
AI re-reads files every session	AI recalls previous analysis instantly
Wastes tokens on repeated reads	80-90% token savings
Slow context building	Sub-millisecond recall
Understanding lost on chat restart	Persists across sessions and chats

How It Works

1. First encounter: AI reads a file, analyzes it, calls store_understanding
2. Future sessions: AI calls recall_understanding first — gets cached analysis
3. File changes: Cache auto-invalidates via content hash — AI re-analyzes
4. Per-repository: Each repo has its own persistent cache

Cache Tools

Tool	Purpose
recall_understanding	Call FIRST before reading any file — retrieves cached analysis
store_understanding	Persist AI's analyzed understanding after reading files
get_understanding_stats	Session start: List cached targets, filter by path/scope, sort, check freshness
invalidate_understanding	Manually clear cached entries when needed

store_understanding supports structured fields (purpose, key_points, gotchas) plus a free-form analysis field for complex business logic, state machines, and workflows.

Pro tip: The AI Context Cache is stored locally in SQLite alongside your code index. Your analysis never leaves your machine.

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Language Support

Tier	Languages
Full	Python, TypeScript, JavaScript
Standard	Java, Go, C#, Rust, Ruby, PHP
SQL	T-SQL, PL/SQL, Standard SQL
Docs	Markdown

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Architecture

Daemon-based architecture for multi-repository support.

graph LR
    IDE[IDE] --> Shim[stdio Shim] --> Daemon[Daemon Manager] --> Workers[Repository Workers] --> DB[(SQLite)]

Key Components:

• Daemon Manager — Coordinates worker lifecycle
• Repository Worker — One per repo, handles indexing and queries
• Indexer — Tree-sitter parsing, symbol extraction
• Context Builder — Token-budgeted context generation

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CLI Mode

Use without MCP protocol:

civyk-repoix query search-symbols --query "%User%" --kind class
civyk-repoix query build-context-pack --task "implement auth" --token-budget 1000
civyk-repoix query --schema  # Get JSON schema of all tools

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Configuration

Location: ~/.config/civyk-repoix/config.yaml

index:
  max_file_size_mb: 10
  debounce_ms: 500

daemon:
  max_workers: 10
  idle_worker_timeout_s: 3600

context:
  default_token_budget: 800
  max_token_budget: 4000

Environment Variables:

Variable	Default	Description
CIVYK_LOG_LEVEL	INFO	Log level
REPOIX_PARSE_WORKERS	CPU count	Parallel parsing workers
REPOIX_CACHE_TTL	60	Query cache TTL (seconds)

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Performance

Benchmarked on Windows 11 Pro, Python 3.13, AMD Ryzen processor with a codebase of 152 files and 6,443 symbols.

Tool Performance

Tool	Avg Latency	Throughput	Category
index_status	0.5ms	3,700+ req/s	Fast
get_symbol	0.6ms	3,600+ req/s	Fast
get_definition	0.6ms	3,400+ req/s	Fast
list_files	0.7ms	3,100+ req/s	Fast
get_file_symbols	0.8ms	2,800+ req/s	Fast
search_symbols	1.8ms	600+ req/s	Medium
get_callers	2.2ms	500+ req/s	Medium
get_references	2.5ms	450+ req/s	Medium
search_code	4ms	280+ req/s	Medium
get_components	2ms	550+ req/s	Medium
build_context_pack	16ms	60+ req/s	Compute
analyze_impact	35ms	30+ req/s	Compute
get_dead_code	45ms	25+ req/s	Compute
find_similar	90ms	12+ req/s	Compute

Index Performance

Operation	Performance
Full index (152 files)	~3 seconds
Delta index	< 500ms
Symbol search	< 2ms
Context pack build	< 20ms

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Support

Help keep this project alive and growing!

If Civyk Repo Index has helped your development workflow, consider supporting its continued development. Your contribution helps with:

• Ongoing maintenance and bug fixes
• New feature development
• Infrastructure costs

50% of all donations go directly to children's charities helping those in need. The remaining funds support project maintenance and feature upgrades.

Every contribution, no matter the size, makes a difference.

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License

Proprietary — see LICENSE

Free to use: Compiled binaries are available via PyPI at no cost for personal and commercial use.