contexta-ai 2.0.0

AI-native codebase context engine. Scans projects and serves intelligent context packs to Claude Code, Cursor, Windsurf, and any MCP-compatible tool.

Contexta

AI-native codebase context engine. Serves intelligent project context to Claude Code, Cursor, Windsurf, and any MCP-compatible tool — or generates curated context packs for manual use.

     

Install via pip, download the portable executable, or run from source.

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What is Contexta

Contexta analyzes codebases and serves intelligent, curated context to AI tools. Instead of dumping files blindly, it understands project structure, detects frameworks, ranks files by importance, and delivers exactly what an AI needs.

Three ways to use it:

Mode	Command	What it does
MCP Server	contexta serve	Runs as a tool server — Claude Code, Cursor, Windsurf call it automatically
CLI	contexta ./project --pack onboarding	Generates a context pack Markdown file
GUI	contexta	Desktop app with visual controls

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MCP Server (recommended)

The MCP server is the primary way to use Contexta. AI coding assistants call it as a tool to understand your project — no copy-paste needed.

Setup

Add to your MCP configuration (Claude Code, Cursor, Windsurf, etc.):

{
  "mcpServers": {
    "contexta": {
      "command": "contexta",
      "args": ["serve"]
    }
  }
}

Or if running from source:

{
  "mcpServers": {
    "contexta": {
      "command": "python",
      "args": ["/path/to/contexta_mcp.py"]
    }
  }
}

Available tools

Tool	What it does
scan_project	Quick fingerprint — type, language, frameworks, deps, entry points
get_architecture	Module relationships, folder structure, risks, key patterns
generate_context	Full AI-optimized context pack with all preset options
find_files	Search files by name, extension, or keyword
read_files	Read specific file contents
list_packs	List available pack presets
cache_status	Show cache hit/miss statistics
refresh_cache	Force-refresh cached analysis

Smart cache

Results are cached in memory with automatic invalidation. First call analyzes the project (~700ms), subsequent calls return in ~2ms until files change on disk. The cache monitors file mtimes and manifest files — when you edit code, the next call recomputes automatically.

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Install

Option A: pip (recommended)

pip install contexta-ai

Optional AI API integrations:

pip install contexta-ai[claude]    # Claude API
pip install contexta-ai[gemini]    # Gemini API
pip install contexta-ai[openai]    # OpenAI API
pip install contexta-ai[all-ai]    # All three

Option B: Portable executables

• Windows: download contexta.exe
• Linux: download contexta-linux.tar.gz

Option C: From source

git clone https://github.com/pablokaua03/Contexta.git
cd Contexta
pip install -r requirements.txt
python contexta.py

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CLI usage

# Generate context packs
contexta ./project                                          # default pack
contexta ./project --pack onboarding                        # understand a new codebase
contexta ./project --pack raw_files                         # clean path + content dump
contexta ./project --pack pr_review --diff --copy           # review changes
contexta ./project --mode debug --focus "auth flow"         # debug a specific area

# AI API integration — send context + question directly to an AI
contexta ./project --ask "what are the main security risks?"
contexta ./project --ask "explain the architecture" --provider claude

# Configure AI API keys
contexta --configure-ai

CLI flags

Flag	Description
--pack	Preset: custom, chatgpt, onboarding, pr_review, risk_review, debug, backend, frontend, changes_related, raw_files
--mode	Context mode: full, debug, feature, diff, onboarding, refactor
--compression	full, balanced, focused, signatures, lean
--ai	Target AI profile: generic, chatgpt, claude, gemini, copilot
--task	Task profile: general, ai_handoff, bug_report, code_review, explain_project, risk_analysis, refactor_request, pr_summary, write_tests, find_dead_code
--focus	Bias scoring around a topic (e.g. "auth flow", "database")
--diff / --staged	Use git changes as context
--ask	Send context pack + prompt to an AI API
--provider	AI provider: claude, gemini, openai
--api-key	API key (also reads ANTHROPIC_API_KEY, GOOGLE_API_KEY, OPENAI_API_KEY)
--configure-ai	Interactive AI API setup
-c / --copy	Copy output to clipboard
-o / --output	Custom output path

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Context packs

Pack	Best for
onboarding	Understanding a new codebase fast
pr_review	Code review with change context
risk_review	Regression hotspots, missing coverage, broad-impact modules
debug	Bug hunting with changed/suspicious files prioritized
backend / frontend	Bias toward one side of the app
changes_related	Git changes + nearby relevant files
raw_files	Clean dump of important files — path + full content, no analysis
chatgpt	General-purpose ChatGPT preset
custom	Full manual control

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What Contexta exports

Depending on pack, mode, and task, the output can include:

• Project summary with detected technologies, entry points, purpose, and central modules
• Read-this-first path through the repository
• Main execution flow narrative
• Core files, supporting files, related tests, and changed-file context
• Relationship maps and risk notes
• Score breakdown explaining why each file was selected
• Curated Markdown payload ready for any AI tool

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Main features

Feature	Detail
MCP Server	contexta serve — AI tools call it as a tool server with smart caching
AI API integration	--ask sends context directly to Claude, Gemini, or OpenAI
Smart cache	~2ms cached responses with automatic mtime-based invalidation
Raw files pack	Clean path + content dump for simple AI workflows
GUI + CLI	Desktop app for visual use, CLI for scripting and automation
Project fingerprinting	Detects stack, frameworks, domain, and project type automatically
Syntax-aware analysis	tree-sitter + heuristic fallback for symbol extraction
Multi-language	Python, JS/TS, Go, Rust, PHP, Java, C#, Kotlin, Swift, C++ and more
Token guidance	tiktoken-backed estimates for pack sizing
Inline blob protection	Collapses base64/binary literals automatically
PyPI package	pip install contexta-ai with optional AI extras

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Build from source

# Windows (requires Visual Studio C++ Build Tools)
.\build.bat

# Linux / macOS
chmod +x build.sh && ./build.sh

Build outputs: dist/contexta.exe (Windows), dist/contexta (Linux/macOS), dist/contexta-linux.tar.gz (Linux bundle).

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Run tests

python -m pytest tests/ -k "not test_relation_score"

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Security and behavior

• Read-only: Contexta does not modify the scanned project
• No runtime telemetry or network requirement (except optional --ask AI calls)
• Scan limits prevent runaway exports
• Embedded binary/blob payloads are suppressed automatically
• AI API keys are stored locally in ~/.contexta/ai_config.json when using --configure-ai

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Contributing

See CONTRIBUTING.md

Changelog

See CHANGELOG.md

License

MIT © pablokaua03