A pure, logic-only library for routing context, handling RAG fallacies, and managing context windows. Layer 1 Primitive - no agent dependencies.
Project description
Context-as-a-Service (CaaS) — Public Preview
Part of Agent OS - Kernel-level governance for AI agents
Context management for RAG systems.
CaaS provides stateless utilities for routing, prioritizing, and filtering context in retrieval-augmented generation (RAG) pipelines. It manages what information gets sent to the LLM, reducing token waste and improving response quality.
Installation
pip install agentmesh-context
Quick Start
from caas import DocumentStore, VirtualFileSystem
store = DocumentStore()
store.add_document({"content": "API auth uses JWT", "timestamp": "2025-01-15"})
vfs = VirtualFileSystem()
vfs.create_file("/project/main.py", "print('hello')", agent_id="agent-1")
Features
- Document storage with time-based prioritization
- Virtual File System for multi-agent collaboration
- Context routing to appropriate model tiers
- Stateless design with no framework dependencies
Contributing
See CONTRIBUTING.md for development setup and guidelines.
License
MIT License — see LICENSE for details.
Project details
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