libra-context 0.1.0.2

Intelligent Context Orchestration for AI Agents

libra

Intelligent Context Orchestration for AI Agents

libra is a local-first context orchestration platform that acts as an intelligent intermediary between your knowledge and AI agents. Rather than having each agent independently search for context, libra serves as a "Context Librarian" that understands what context exists, what each agent needs, and composes tailored context packages for any task.

Features

• Intelligent Context Selection: LLM-powered reasoning to select relevant context, not just embedding similarity
• Multi-Provider Support: Works with Gemini, OpenAI, Anthropic, Ollama, Azure, AWS Bedrock, HuggingFace, Together AI, and custom endpoints
• Token Budget Management: Fits context within token limits while maximizing relevance
• Multiple Librarian Modes: Rules-based (fast), LLM-based (smart), or Hybrid (balanced)
• MCP Integration: Works with Claude Desktop, Cursor, Continue.dev, and other MCP clients
• REST API: Full HTTP API for custom integrations
• CLI: Complete command-line interface for management
• Web UI: Browser-based dashboard for visual management
• Audit Logging: Complete trail of what context was served to which agent
• Local-First: All data stays on your machine with optional fully-local operation (Ollama + sentence-transformers)

Quick Start

Installation

From PyPI (recommended):

pip install libra-context

From source:

# Clone the repository
git clone https://github.com/andrewmoshu/libra-context
cd libra-context

# Install with pip
pip install -e .

# Or with UV
uv sync

Configuration

# Set your API key (Gemini is default, or choose another provider)
export GOOGLE_AI_API_KEY="your-api-key-here"  # For Gemini (default)
# Or: export OPENAI_API_KEY="..."   # For OpenAI
# Or: export ANTHROPIC_API_KEY="..." # For Anthropic

# Initialize libra with interactive setup wizard
libra init

# Or quick setup with defaults (uses Gemini)
libra init -y

The interactive libra init guides you through selecting LLM and embedding providers from 9 supported options each.

Basic Usage

# Add context manually
libra add "I prefer TypeScript over JavaScript" --type preference --tags coding

# Ingest documentation
libra ingest ~/Documents/project-docs/ --type knowledge --tags project

# Query for relevant context
libra query "Help me refactor the authentication module"

# Search contexts semantically
libra search "authentication patterns"

# Start the MCP server (for Claude Desktop integration)
libra serve

# Start the HTTP server (for REST API and Web UI)
libra serve --http

# Interactive chat with your context
libra chat

Claude Desktop Integration

Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

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

Restart Claude Desktop to enable the integration.

Supported Providers

libra supports 9 LLM providers and 9 embedding providers for maximum flexibility:

LLM Providers

Provider	Key Environment Variable	Notes
gemini	GOOGLE_AI_API_KEY	Default. Fast, high-quality, generous free tier
openai	OPENAI_API_KEY	Industry standard GPT models
anthropic	ANTHROPIC_API_KEY	Claude models
ollama	None (local)	Fully local, no API key needed
azure_openai	AZURE_OPENAI_API_KEY	Enterprise Azure deployment
aws_bedrock	AWS credentials	AWS managed service
huggingface	HF_TOKEN	HuggingFace Inference API
together	TOGETHER_API_KEY	Fast inference
custom	Configurable	Any OpenAI-compatible endpoint

Embedding Providers

Provider	Default Model	Notes
gemini	gemini-embedding-001	Default. Excellent quality
openai	text-embedding-3-small	Industry standard
ollama	nomic-embed-text	Local embeddings
local	all-MiniLM-L6-v2	Fully offline (sentence-transformers)
azure_openai	text-embedding-3-small	Enterprise Azure
aws_bedrock	amazon.titan-embed-text-v2:0	AWS managed
huggingface	sentence-transformers/all-MiniLM-L6-v2	HuggingFace API
together	togethercomputer/m2-bert-80M-8k-retrieval	Together AI
custom	Configurable	Custom HTTP endpoint

Context Types

libra organizes context into three types:

Type	Description	Examples
Knowledge	Facts, documentation, reference material	API docs, technical specs, project info
Preference	How you like things done	Coding style, communication preferences
History	Past decisions and events	Previous architectural choices, bugs fixed

CLI Commands

libra add        - Add a new context
libra list       - List contexts with filtering
libra show       - Display context details
libra delete     - Delete a context
libra query      - Get relevant context for a task
libra search     - Search contexts by similarity
libra ingest     - Ingest file or directory
libra serve      - Start MCP or HTTP server
libra audit      - View audit log
libra stats      - Show storage statistics
libra export     - Export contexts to JSON
libra import     - Import contexts from JSON
libra chat       - Interactive chat with Librarian
libra init       - Initialize libra
libra config     - Configuration management

API Endpoints

When running with libra serve --http:

Endpoint	Method	Description
/api/v1/contexts	GET	List contexts
/api/v1/contexts	POST	Create context
/api/v1/contexts/{id}	GET	Get context
/api/v1/contexts/{id}	PUT	Update context
/api/v1/contexts/{id}	DELETE	Delete context
/api/v1/query	POST	Get context for task
/api/v1/search	POST	Semantic search
/api/v1/audit	GET	View audit log
/api/v1/stats	GET	Storage statistics
/docs	GET	OpenAPI documentation

Web UI

Access at http://localhost:8377 when running libra serve --http:

• Dashboard: Overview of context store and statistics
• Contexts: Browse, search, and manage contexts
• Audit Log: Review what context was served to agents
• Settings: Configure libra behavior

Configuration

Configuration file: ~/.libra/config.yaml

librarian:
  mode: hybrid  # rules, llm, or hybrid
  llm:
    provider: gemini  # gemini, openai, anthropic, ollama, azure_openai, aws_bedrock, huggingface, together, custom
    model: gemini-2.5-flash
    # api_key: set via env var (recommended) or here
    # base_url: for ollama/custom endpoints
    # azure_endpoint: for azure_openai
    # aws_region: for aws_bedrock

embedding:
  provider: gemini  # gemini, openai, ollama, local, azure_openai, aws_bedrock, huggingface, together, custom
  model: gemini-embedding-001
  dimensions: 768

defaults:
  token_budget: 2000
  chunk_size: 512
  min_relevance: 0.5

server:
  http_port: 8377
  http_host: 127.0.0.1

See Setup Guide for detailed provider configuration examples.

Python API

from libra import LibraService, ContextType

# Create service
service = LibraService()

# Add context
context = service.add_context(
    content="Our API uses REST with JSON responses",
    context_type=ContextType.KNOWLEDGE,
    tags=["api", "architecture"]
)

# Query for relevant context
response = service.query(
    task="How should I design the new endpoint?",
    max_tokens=2000
)

for scored in response.contexts:
    print(f"{scored.relevance_score:.2f}: {scored.context.content[:100]}...")

Development

# Install dev dependencies
pip install -e ".[dev]"

# Run tests
pytest -v

# Type checking
mypy libra/

# Linting
ruff check libra/

# Format code
black libra/ tests/

Technology Stack

• Python 3.11+
• SQLite + sqlite-vec: Local storage with vector search
• Multi-provider support: Gemini (default), OpenAI, Anthropic, Ollama, Azure, AWS Bedrock, HuggingFace, Together AI, custom endpoints
• FastAPI: REST API
• Typer: CLI framework
• MCP SDK: Agent integration protocol

Documentation

• High-Level Design (HLD)
• Low-Level Design (LLD)
• Setup Guide
• API Reference (when server running)

License

Business Source License 1.1 - see LICENSE file for details.

This project is licensed under the Business Source License 1.1. After 4 years, the license automatically converts to Apache License 2.0.