Cipher

Cipher is an AI agent orchestration framework and LLM context memory layer. It provides a collaborative AI knowledge base and a context synchronization service that allows AI agents and CLI tools to maintain a persistent, structured memory of project decisions and codebase patterns across different sessions and machines.

The system distinguishes itself through a version-controlled context model, using branches and commits to track how project knowledge evolves. It features a hierarchical knowledge store where information is organized as markdown files and can be synchronized between local environments and a remote cloud host.

The platform covers a broad range of capabilities including AI memory governance with role-based access control, multi-tenant space isolation, and an agent-skill plugin architecture. It also includes tools for project knowledge curation, memory topic discovery, and a skill marketplace to extend agent capabilities.

User identity and team coordination are managed via browser-based OAuth authentication, team membership management, and a dedicated billing portal for subscription plans.

Features

Cross-Agent Context Managers - Provides a persistent and structured memory store for AI agents to recall project decisions and codebase patterns.

AI Agent Orchestration Frameworks - Provides a runtime for managing autonomous agent loops and integrating external AI agents and CLI tools.

Agentic Project Memories - Links local project folders to persistent memory spaces to maintain project-specific conventions and guidelines.

AI Provider Integrations - Implements configuration interfaces to connect with various external or local large language model providers.

AI Agent Integrations - Provides connectors for integrating external AI agents and coding assistants into the development workflow.

AI Knowledge Management - Provides a cloud-synchronized system for maintaining centralized knowledge bases to support AI-driven query responses.

Coding Agent Integrations - Integrates AI assistants directly into software development environments to support coding workflows and context retrieval.

Context Memory Management - Provides a persistent knowledge store that maintains structured project memory and history for LLM context windows.

Contextual Information Retrieval - Retrieves stored project information and context before tasks to maintain consistency across AI sessions.

Knowledge Versioning Systems - Implements a version-controlled context model using branches and commits to track how project knowledge evolves.

Memory Retrieval Systems - Implements mechanisms for retrieving complete memory entries to provide detailed context for AI agent tasks.

Remote Context Synchronization - Synchronizes AI-powered context between local project directories and remote cloud memory spaces.

Automated Context Synchronization - Ships an automated service to synchronize project knowledge trees across multiple machines to align AI context.

Version-Controlled Knowledge Bases - Tracks the evolution of project knowledge using distributed version control workflows with branches and commits.

Knowledge Tree Synchronization - Synchronizes shared knowledge trees to a hosted platform to align AI context across multiple machines.

Cross-Category Memory Search - Provides unified querying to retrieve decisions, patterns, and constraints across different types of project memory stores.

Hierarchical Information Architectures - Organizes project information into a hierarchical tree of domain-specific topics for structured retrieval.

Knowledge Tree Synchronization - Synchronizes structured markdown knowledge trees between local environments and remote cloud hosts.

Collaborative Memory Spaces - Creates group-accessible memory environments for collaborative querying and recording of project information.

Database Memory Persistence - Ensures future sessions inherit project context by persisting structured memories and patterns.

AI Agent Tenant Isolation - Implements strict memory boundaries to isolate personal, team, and project data within the AI agent infrastructure.

Context Versioning Systems - Tracks changes to the knowledge tree using branches and commits to manage different versions of project context.

Memory Knowledge Updates - Modifies previously saved knowledge with updated decisions to keep project context current.

Shared Knowledge Graph Memory - Creates shared memory environments where teams synchronize project context across different machines.

Agent Capability Extensions - Allows the installation of external packages and connectors to expand the functional capabilities of AI agents.

Agent Skill Frameworks - Provides a framework for defining and registering specific capabilities and skills for AI agents.

Skill Marketplaces - Provides a portal for discovering and downloading community-created skill sets and context bundles for AI agents.

LLM Tooling Integrations - Provides connectors and interfaces that allow LLMs to access external data and execute software tools.

Team Management - Implements tools for inviting new users via email and managing their access to shared workspaces.

Team Collaboration Management - Provides administrative tools for managing user groups and sharing memory spaces within teams.

Memory Space Categorization - Categorizes AI memory into private, team, or shared spaces to manage information visibility and access.

Knowledge Base Hierarchies - Organizes project information into a hierarchy of markdown files for structured AI retrieval.

Knowledge Curation - Implements a review workflow to curate and approve codebase information added to the structured memory store.

Governed Agent Memory - Controls access roles and visibility for private and team memory spaces to ensure data isolation.

Role-Based Access Control - Manages visibility and permissions for memory spaces using assigned user roles like Owner, Admin, Editor, or Viewer.

Workspace Role Assignments - Manages visibility and edit permissions for shared memory spaces using granular workspace roles.

Multi-tenant Isolation Policies - Enforces data boundaries and isolation between personal, team, and project memory spaces.

Project-to-Memory Mappings - Binds local directory paths to remote memory identifiers to route context queries correctly.

Agent Space Onboarding - Links running agents to specific project spaces to initialize shared memory environments.

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Features

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