infiniloom 0.4.10

Python bindings for infiniloom - Repository context engine for LLMs

Infiniloom Python Bindings

Python bindings for Infiniloom - a repository context engine for Large Language Models.

Installation

pip install infiniloom

Building from Source

git clone https://github.com/Topos-Labs/infiniloom.git
cd infiniloom/bindings/python
pip install maturin
maturin develop  # For development
maturin build --release  # For production wheel

Quick Start

Functional API

import infiniloom

# Pack a repository into Claude-optimized XML
context = infiniloom.pack("/path/to/repo", format="xml", model="claude")
print(context)

# Scan repository and get statistics
stats = infiniloom.scan("/path/to/repo")
print(f"Files: {stats['total_files']}")
print(f"Languages: {stats['languages']}")

# Count tokens for a specific model
tokens = infiniloom.count_tokens("Hello, world!", model="claude")
print(f"Tokens: {tokens}")

Object-Oriented API

from infiniloom import Infiniloom

# Create an Infiniloom instance
loom = Infiniloom("/path/to/repo")

# Get repository statistics
stats = loom.stats()
print(stats)

# Generate repository context
context = loom.pack(format="xml", model="claude", compression="balanced")

# Get repository map with important symbols
repo_map = loom.map(map_budget=2000, max_symbols=50)
for symbol in repo_map['key_symbols']:
    print(f"{symbol['name']} ({symbol['kind']}) in {symbol['file']}")

# Scan for security issues
findings = loom.scan_security()
for finding in findings:
    print(f"{finding['severity']}: {finding['message']} at {finding['file']}:{finding['line']}")

# List all files
files = loom.files()
for file in files:
    print(f"{file['path']} - {file['language']} ({file['tokens']} tokens)")

API Reference

Functions

pack(path, format="xml", model="claude", compression="balanced", map_budget=2000, max_symbols=50)

Pack a repository into an LLM-optimized format.

Parameters:

• path (str): Path to the repository
• format (str): Output format - "xml", "markdown", "json", "yaml", "toon", or "plain"
• model (str): Target model for token counting. Supports:
  • OpenAI GPT-5.x: "gpt-5.2", "gpt-5.2-pro", "gpt-5.1", "gpt-5.1-mini", "gpt-5.1-codex", "gpt-5", "gpt-5-mini", "gpt-5-nano"
  • OpenAI O-series: "o4-mini", "o3", "o3-mini", "o1", "o1-mini", "o1-preview"
  • OpenAI GPT-4: "gpt-4o", "gpt-4o-mini", "gpt-4", "gpt-3.5-turbo"
  • Anthropic: "claude" (default)
  • Google: "gemini"
  • Meta: "llama", "codellama"
  • Others: "deepseek", "mistral", "qwen", "cohere", "grok"
• compression (str): Compression level - "none", "minimal", "balanced", "aggressive", "extreme", "focused", "semantic"
• map_budget (int): Token budget for repository map (default: 2000)
• max_symbols (int): Maximum symbols to include (default: 50)

Returns: str - Formatted repository context

scan(path, include_hidden=False, respect_gitignore=True)

Scan a repository and return statistics.

Parameters:

• path (str): Path to the repository
• include_hidden (bool): Include hidden files (default: False)
• respect_gitignore (bool): Respect .gitignore files (default: True)

Returns: dict - Repository statistics including:

• name: Repository name
• path: Absolute path
• total_files: Number of files
• total_lines: Total lines of code
• total_tokens: Token counts for each model
• languages: Language breakdown
• branch: Git branch (if available)
• commit: Git commit hash (if available)

count_tokens(text, model="claude")

Count tokens in text for a specific model.

Parameters:

• text (str): Text to count tokens for
• model (str): Target model. Supports all models listed above in pack(), including GPT-5.x series

Returns: int - Number of tokens (exact for OpenAI models via tiktoken, calibrated estimates for others)

semantic_compress(text, similarity_threshold=0.7, budget_ratio=0.5)

Compress text using semantic compression while preserving important content.

Parameters:

• text (str): Text to compress
• similarity_threshold (float): Threshold for grouping similar chunks (0.0-1.0, default: 0.7). Note: Only affects output when built with "embeddings" feature.
• budget_ratio (float): Target size as ratio of original (0.0-1.0, default: 0.5). Lower values = more aggressive compression.

Returns: str - Compressed text

import infiniloom

long_text = "... your long text content ..."
compressed = infiniloom.semantic_compress(long_text, budget_ratio=0.3)
print(compressed)

scan_security(path)

Scan repository for security issues.

Parameters:

• path (str): Path to the repository

Returns: list[dict] - List of security findings with:

• file: File path
• line: Line number
• severity: Severity level ("Critical", "High", "Medium", "Low", "Info")
• kind: Type of finding (e.g., "aws_access_key", "github_token")
• pattern: The matched pattern

is_git_repo(path)

Check if a path is a git repository.

Parameters:

• path (str): Path to check

Returns: bool - True if path is a git repository, False otherwise

from infiniloom import is_git_repo

if is_git_repo("/path/to/repo"):
    print("This is a git repository")

Call Graph API

Query caller/callee relationships and navigate your codebase programmatically.

build_index(path, force=False, include_tests=False, max_file_size=None)

Build or update the symbol index for a repository (required for call graph queries).

Parameters:

• path (str): Path to repository root
• force (bool): Force full rebuild even if index exists (default: False)
• include_tests (bool): Include test files in index (default: False)
• max_file_size (int): Maximum file size to index in bytes (default: 10MB)

Returns: dict - Index status with exists, file_count, symbol_count, last_built (ISO 8601 timestamp), version

import infiniloom

status = infiniloom.build_index("/path/to/repo")
print(f"Indexed {status['symbol_count']} symbols")

find_symbol(path, name)

Find symbols by name in the index.

Parameters:

• path (str): Path to repository root
• name (str): Symbol name to search for

Returns: list[dict] - List of matching symbols with id, name, kind, file, line, end_line, signature, visibility

import infiniloom

infiniloom.build_index("/path/to/repo")
symbols = infiniloom.find_symbol("/path/to/repo", "process_request")
for s in symbols:
    print(f"{s['name']} ({s['kind']}) at {s['file']}:{s['line']}")

get_callers(path, symbol_name)

Get all functions/methods that call the target symbol.

Parameters:

• path (str): Path to repository root
• symbol_name (str): Name of the symbol to find callers for

Returns: list[dict] - List of symbols that call the target

import infiniloom

infiniloom.build_index("/path/to/repo")
callers = infiniloom.get_callers("/path/to/repo", "authenticate")
print(f"authenticate is called by {len(callers)} functions")
for c in callers:
    print(f"  {c['name']} at {c['file']}:{c['line']}")

get_callees(path, symbol_name)

Get all functions/methods that the target symbol calls.

Parameters:

• path (str): Path to repository root
• symbol_name (str): Name of the symbol to find callees for

Returns: list[dict] - List of symbols that the target calls

import infiniloom

infiniloom.build_index("/path/to/repo")
callees = infiniloom.get_callees("/path/to/repo", "main")
print(f"main calls {len(callees)} functions")

get_references(path, symbol_name)

Get all references to a symbol (calls, imports, inheritance).

Parameters:

• path (str): Path to repository root
• symbol_name (str): Name of the symbol to find references for

Returns: list[dict] - List of references with symbol (dict) and kind (str)

import infiniloom

infiniloom.build_index("/path/to/repo")
refs = infiniloom.get_references("/path/to/repo", "UserService")
for r in refs:
    print(f"{r['kind']}: {r['symbol']['name']} at {r['symbol']['file']}:{r['symbol']['line']}")

get_call_graph(path, max_nodes=None, max_edges=None)

Get the complete call graph with all symbols and call relationships.

Parameters:

• path (str): Path to repository root
• max_nodes (int): Maximum number of nodes to return (default: unlimited)
• max_edges (int): Maximum number of edges to return (default: unlimited)

Returns: dict - Call graph with nodes (list), edges (list), stats (dict)

import infiniloom

infiniloom.build_index("/path/to/repo")
graph = infiniloom.get_call_graph("/path/to/repo")
print(f"{graph['stats']['total_symbols']} symbols, {graph['stats']['total_calls']} calls")

# Find most called functions
from collections import Counter
call_counts = Counter(edge['callee'] for edge in graph['edges'])
print("Most called:", call_counts.most_common(5))

Async versions

All call graph functions have async versions:

• find_symbol_async(path, name)
• get_callers_async(path, symbol_name)
• get_callees_async(path, symbol_name)
• get_references_async(path, symbol_name)
• get_call_graph_async(path, max_nodes=None, max_edges=None)

import asyncio
import infiniloom

async def analyze_codebase():
    await infiniloom.build_index_async("/path/to/repo")
    callers = await infiniloom.get_callers_async("/path/to/repo", "authenticate")
    print(f"Found {len(callers)} callers")

asyncio.run(analyze_codebase())

index_status(path)

Get the status of an existing index.

Parameters:

• path (str): Path to repository root

Returns: dict - Index status with exists, file_count, symbol_count, last_built (ISO 8601 timestamp), version

import infiniloom

status = infiniloom.index_status("/path/to/repo")
if status["exists"]:
    print(f"Index has {status['symbol_count']} symbols")
else:
    print("No index found - run build_index first")

Chunking API

Split large repositories into manageable chunks for multi-turn LLM conversations.

chunk(path, strategy="module", max_tokens=8000, overlap=0, model="claude", priority_first=False)

Split repository into chunks for processing with limited context windows.

Parameters:

• path (str): Path to repository root
• strategy (str): Chunking strategy - "fixed", "file", "module", "symbol", "semantic", "dependency"
• max_tokens (int): Maximum tokens per chunk (default: 8000)
• overlap (int): Token overlap between chunks (default: 0)
• model (str): Target model for token counting (default: "claude")
• priority_first (bool): Sort chunks by file priority (default: False)

Returns: list[dict] - List of chunks with index, total, focus, tokens, files, content

import infiniloom

# Split large repo into manageable chunks
chunks = infiniloom.chunk("/path/to/large-repo", strategy="module", max_tokens=50000)

for c in chunks:
    print(f"Chunk {c['index']+1}/{c['total']}: {c['focus']} ({c['tokens']} tokens)")
    # Send c['content'] to LLM for analysis

# Use dependency-aware chunking for better context
chunks = infiniloom.chunk("/path/to/repo", strategy="dependency", priority_first=True)

Strategies:

• fixed: Split at fixed token boundaries
• file: One file per chunk
• module: Group by module/directory
• symbol: Group by symbol (function/class)
• semantic: Group by semantic similarity
• dependency: Group by dependency relationships

Impact Analysis API

Analyze the impact of changes to understand what code is affected.

analyze_impact(path, files, depth=2, include_tests=False, model=None, exclude=None, include=None)

Analyze the impact of changes to files or symbols.

Parameters:

• path (str): Path to repository root
• files (list[str]): List of files to analyze
• depth (int): Depth of dependency traversal (1-3, default: 2)
• include_tests (bool): Include test files in analysis (default: False)
• model (str, optional): Target model for token counting (default: "claude")
• exclude (list[str], optional): Glob patterns to exclude (e.g., ["/*.test.py", "dist/"])
• include (list[str], optional): Glob patterns to include (e.g., ["src/**/*.py"])

Returns: dict - Impact analysis with:

• changed_files: List of files being analyzed
• dependent_files: Files that depend on changed files
• test_files: Related test files
• affected_symbols: List of affected symbols with name, kind, file, line, impact_type
• impact_level: Impact severity ("low", "medium", "high", "critical")
• summary: Human-readable summary

import infiniloom

# Build index first
infiniloom.build_index("/path/to/repo")

# Analyze impact of changing a file
impact = infiniloom.analyze_impact("/path/to/repo", ["src/auth.py"])
print(f"Impact level: {impact['impact_level']}")
print(f"Summary: {impact['summary']}")

# See what else needs updating
for dep in impact['dependent_files']:
    print(f"  Dependent: {dep}")

for sym in impact['affected_symbols']:
    print(f"  {sym['impact_type']}: {sym['name']} in {sym['file']}")

Diff Context API

Get semantic context around code changes for AI-powered code review.

get_diff_context(path, from_ref="", to_ref="HEAD", depth=2, budget=50000, include_diff=False, model=None, exclude=None, include=None)

Get context-aware diff with surrounding symbols and dependencies.

Parameters:

• path (str): Path to repository root
• from_ref (str): Starting ref - "" for unstaged, "HEAD" for staged, commit hash, branch name
• to_ref (str): Ending ref - "HEAD", commit hash, branch name
• depth (int): Context expansion depth (1-3, default: 2)
• budget (int): Token budget for context (default: 50000)
• include_diff (bool): Include actual diff content (default: False)
• model (str, optional): Target model for token counting (default: "claude")
• exclude (list[str], optional): Glob patterns to exclude (e.g., ["/*.test.py", "dist/"])
• include (list[str], optional): Glob patterns to include (e.g., ["src/**/*.py"])

Returns: dict - Diff context with:

• changed_files: List of changed files with path, change_type, additions, deletions, diff (if requested)
• context_symbols: Related symbols with name, kind, file, line, reason, signature
• related_tests: List of related test file paths
• total_tokens: Estimated token count

import infiniloom

# Build index for full context (optional but recommended)
infiniloom.build_index("/path/to/repo")

# Get context for uncommitted changes
context = infiniloom.get_diff_context("/path/to/repo")
print(f"Changed: {len(context['changed_files'])} files")

# Get context for last commit with diff content
context = infiniloom.get_diff_context(
    "/path/to/repo",
    from_ref="HEAD~1",
    to_ref="HEAD",
    include_diff=True
)
for f in context['changed_files']:
    print(f"{f['change_type']}: {f['path']}")
    if 'diff' in f:
        print(f['diff'])

# Get context for a PR (branch comparison)
context = infiniloom.get_diff_context(
    "/path/to/repo",
    from_ref="main",
    to_ref="feature-branch",
    depth=3
)
print(f"Related symbols: {len(context['context_symbols'])}")
print(f"Related tests: {len(context['related_tests'])}")

Classes

Infiniloom(path)

Object-oriented interface for repository analysis.

Methods:

load(include_hidden=False, respect_gitignore=True)

Load the repository into memory.

stats()

Get repository statistics. Returns same structure as scan() function.

pack(format="xml", model="claude", compression="balanced", map_budget=2000)

Pack the repository. Returns formatted string.

map(map_budget=2000, max_symbols=50)

Get repository map with key symbols. Returns dict with:

• summary: Text summary
• token_count: Estimated tokens
• key_symbols: List of important symbols

scan_security()

Scan for security issues. Returns list of findings.

files()

Get list of all files. Returns list of dicts with file metadata.

GitRepo(path)

Git repository wrapper for accessing git operations like status, diff, log, and blame.

Constructor:

• path (str): Path to the git repository

Raises: InfiniloomError if path is not a git repository

Methods:

current_branch()

Get the current branch name.

Returns: str - Current branch name (e.g., "main", "feature/xyz")

current_commit()

Get the current commit hash.

Returns: str - Full SHA-1 hash of HEAD commit (40 characters)

status()

Get working tree status (both staged and unstaged changes).

Returns: list[dict] - List of file status objects with:

• path: File path
• status: Status type ("Added", "Modified", "Deleted", "Renamed", "Copied", "Unknown")
• old_path: Old path for renames (optional)

log(count=10)

Get recent commits.

Parameters:

• count (int): Maximum number of commits to return (default: 10)

Returns: list[dict] - List of commit objects with:

• hash: Full commit hash
• short_hash: Short commit hash (7 characters)
• author: Author name
• email: Author email
• date: Commit date (ISO 8601 format)
• message: Commit message (first line)

file_log(path, count=10)

Get commits that modified a specific file.

Parameters:

• path (str): File path relative to repo root
• count (int): Maximum number of commits to return (default: 10)

Returns: list[dict] - List of commits that modified the file

blame(path)

Get blame information for a file.

Parameters:

• path (str): File path relative to repo root

Returns: list[dict] - List of blame line objects with:

• commit: Commit hash that introduced the line
• author: Author who wrote the line
• date: Date when line was written
• line_number: Line number (1-indexed)

ls_files()

Get list of files tracked by git.

Returns: list[str] - Array of file paths tracked by git

diff_files(from_ref, to_ref)

Get files changed between two commits.

Parameters:

• from_ref (str): Starting commit/branch/tag
• to_ref (str): Ending commit/branch/tag

Returns: list[dict] - List of changed files with:

• path: File path
• status: Status ("Added", "Modified", "Deleted", "Renamed", "Copied")
• additions: Number of lines added
• deletions: Number of lines deleted

uncommitted_diff(path)

Get diff content for uncommitted changes in a file.

Parameters:

• path (str): File path relative to repo root

Returns: str - Unified diff content

all_uncommitted_diffs()

Get diff for all uncommitted changes.

Returns: str - Combined unified diff for all changed files

has_changes(path)

Check if a file has uncommitted changes.

Parameters:

• path (str): File path relative to repo root

Returns: bool - True if file has changes

last_modified_commit(path)

Get the last commit that modified a file.

Parameters:

• path (str): File path relative to repo root

Returns: dict - Commit information object

file_change_frequency(path, days=30)

Get file change frequency in recent days.

Parameters:

• path (str): File path relative to repo root
• days (int): Number of days to look back (default: 30)

Returns: int - Number of commits that modified the file in the period

file_at_ref(path, git_ref)

Get file content at a specific git ref (commit, branch, tag).

Parameters:

• path (str): File path relative to repo root
• git_ref (str): Git ref (commit hash, branch name, tag, HEAD~n, etc.)

Returns: str - File content

repo = GitRepo("/path/to/repo")
old_version = repo.file_at_ref("src/main.py", "HEAD~5")
main_version = repo.file_at_ref("src/main.py", "main")

diff_hunks(from_ref, to_ref, path=None)

Parse diff between two refs into structured hunks with line-level changes. Useful for PR review tools that need to post comments at specific lines.

Parameters:

• from_ref (str): Starting ref (e.g., "main", "HEAD~5", commit hash)
• to_ref (str): Ending ref (e.g., "HEAD", "feature-branch")
• path (str, optional): File path to filter to a single file

Returns: list[dict] - List of diff hunks with:

• old_start: Starting line in old file
• old_count: Number of lines in old file
• new_start: Starting line in new file
• new_count: Number of lines in new file
• header: Hunk header
• lines: List of line dicts with change_type, old_line, new_line, content

repo = GitRepo("/path/to/repo")
hunks = repo.diff_hunks("main", "HEAD", "src/index.py")
for hunk in hunks:
    print(f"Hunk at old:{hunk['old_start']} new:{hunk['new_start']}")
    for line in hunk['lines']:
        print(f"{line['change_type']}: {line['content']}")

uncommitted_hunks(path=None)

Parse uncommitted changes (working tree vs HEAD) into structured hunks.

Parameters:

• path (str, optional): File path to filter to a single file

Returns: list[dict] - List of diff hunks for uncommitted changes

staged_hunks(path=None)

Parse staged changes into structured hunks.

Parameters:

• path (str, optional): File path to filter to a single file

Returns: list[dict] - List of diff hunks for staged changes only

Example:

from infiniloom import GitRepo, is_git_repo

# Check if path is a git repo first
if is_git_repo("/path/to/repo"):
    repo = GitRepo("/path/to/repo")

    # Get current state
    print(f"Branch: {repo.current_branch()}")
    print(f"Commit: {repo.current_commit()}")

    # Get recent commits
    for commit in repo.log(count=5):
        print(f"{commit['short_hash']}: {commit['message']}")

    # Get file history
    for commit in repo.file_log("src/main.py", count=3):
        print(f"{commit['date']}: {commit['message']}")

    # Get blame information
    for line in repo.blame("src/main.py")[:10]:
        print(f"Line {line['line_number']}: {line['author']}")

    # Check for uncommitted changes
    if repo.has_changes("src/main.py"):
        diff = repo.uncommitted_diff("src/main.py")
        print(diff)

Async Functions

Infiniloom provides async versions of the main functions for use in async/await contexts. These use a thread pool executor to avoid blocking the event loop.

import asyncio
import infiniloom

async def main():
    # Pack repository asynchronously
    context = await infiniloom.pack_async("/path/to/repo", format="xml", model="claude")

    # Scan repository asynchronously
    stats = await infiniloom.scan_async("/path/to/repo")

    # Count tokens asynchronously
    tokens = await infiniloom.count_tokens_async("Hello, world!", model="claude")

    # Scan security asynchronously
    findings = await infiniloom.scan_security_async("/path/to/repo")

    # Semantic compress asynchronously
    compressed = await infiniloom.semantic_compress_async(long_text, budget_ratio=0.3)

asyncio.run(main())

Available Async Functions

• pack_async(path, format="xml", model="claude", compression="balanced", ...) - Async pack
• scan_async(path, include_hidden=False, respect_gitignore=True) - Async scan
• count_tokens_async(text, model="claude") - Async token counting
• scan_security_async(path) - Async security scanning
• semantic_compress_async(text, similarity_threshold=0.7, budget_ratio=0.5) - Async compression

Formats

XML (Claude-optimized)

Best for Claude models. Uses XML structure that Claude understands well.

context = infiniloom.pack("/path/to/repo", format="xml", model="claude")

Markdown (GPT-optimized)

Best for GPT models. Uses Markdown with clear hierarchical structure.

context = infiniloom.pack("/path/to/repo", format="markdown", model="gpt")

JSON

Generic JSON format for programmatic processing.

context = infiniloom.pack("/path/to/repo", format="json")

YAML (Gemini-optimized)

Best for Gemini. Query should be placed at the end.

context = infiniloom.pack("/path/to/repo", format="yaml", model="gemini")

TOON (Token-Efficient)

Most token-efficient format (~40% smaller than JSON). Best for limited context windows.

context = infiniloom.pack("/path/to/repo", format="toon")

Compression Levels

• none: No compression (0% reduction)
• minimal: Remove empty lines, trim whitespace (15% reduction)
• balanced: Remove comments, normalize whitespace (35% reduction) - Default
• aggressive: Remove docstrings, keep signatures only (60% reduction)
• extreme: Key symbols only (80% reduction)
• focused: Key symbols with small context (75% reduction)
• semantic: Heuristic semantic compression (~60-70% reduction)

Integration Examples

With Anthropic Claude

import infiniloom
import anthropic

# Generate context
context = infiniloom.pack(
    "/path/to/repo",
    format="xml",
    model="claude",
    compression="balanced"
)

# Send to Claude
client = anthropic.Anthropic()
response = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=4096,
    messages=[{
        "role": "user",
        "content": f"{context}\n\nExplain the architecture of this codebase."
    }]
)
print(response.content[0].text)

With OpenAI GPT

import infiniloom
import openai

context = infiniloom.pack("/path/to/repo", format="markdown", model="gpt")

client = openai.OpenAI()
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{
        "role": "user",
        "content": f"{context}\n\nWhat are the main components?"
    }]
)
print(response.choices[0].message.content)

With Google Gemini

import infiniloom
import google.generativeai as genai

context = infiniloom.pack("/path/to/repo", format="yaml", model="gemini")

genai.configure(api_key="YOUR_API_KEY")
model = genai.GenerativeModel("gemini-1.5-pro")
response = model.generate_content(f"{context}\n\nSummarize this codebase")
print(response.text)

Advanced Usage

Custom Token Budget

from infiniloom import Infiniloom

loom = Infiniloom("/large/repo")

# Generate smaller context for models with limited context windows
compact_map = loom.map(map_budget=1000, max_symbols=25)

# Generate larger context for models with large context windows
detailed_map = loom.map(map_budget=5000, max_symbols=200)

Security Scanning

from infiniloom import Infiniloom

loom = Infiniloom("/path/to/repo")
findings = loom.scan_security()

# Filter by severity
critical = [f for f in findings if f['severity'] == 'Critical']
high = [f for f in findings if f['severity'] == 'High']

print(f"Critical: {len(critical)}, High: {len(high)}")

for finding in critical:
    print(f"{finding['file']}:{finding['line']}")
    print(f"  {finding['category']}: {finding['message']}")

File Filtering

from infiniloom import Infiniloom

loom = Infiniloom("/path/to/repo")
files = loom.files()

# Get Python files only
python_files = [f for f in files if f['language'] == 'python']

# Get high-importance files
important_files = [f for f in files if f['importance'] > 0.7]

# Get large files
large_files = [f for f in files if f['tokens'] > 1000]

Performance

Infiniloom is built in Rust for maximum performance:

• Fast scanning: Parallel file processing with ignore patterns
• Memory efficient: Streaming processing, optional content loading
• Native speed: No Python overhead for core operations

Requirements

• Python 3.8+
• Rust 1.91+ (for building from source)

License

MIT License - see LICENSE for details.

Links

• GitHub
• Documentation
• PyPI