ThoughtFrame
ThoughtFrame is an engineering-first, composable platform for digital learning, AI collaboration, and knowledge management.
The core building block is the Frame: a fully-contained, multi-user workspace defining the strict boundaries for users, data, analytics, and AI operations. Each Frame functions as a transfer function and provides the execution context for everything inside.
Within each Frame, you can create and navigate a Mesh-a network of linked concepts, knowledge, resources, or tasks, visualized and operated on in context. Frames do not share data or logic unless explicitly connected.
Every feature is production-grade, available live, and used internally-no demo sandboxes, no staged UIs. Platform improvements are deployed continuously; what you see is what you use.
Dogfooding by default. No marketing layer. Everything is real.
ThoughtFrame Core
ThoughtFrame Core is the foundation and runtime for all platform features and modules. It delivers a robust, extensible infrastructure for:
- Rendering Engine: Velocity-based template system for dynamic content, live dashboards, emails, API responses, and page assembly.
- Function Calling & Orchestration: Advanced function-calling layer for deterministic LLM orchestration, trigger-based flows, event chaining, and direct parameterized requests between system modules and AI endpoints.
- MCP Connections: Native support for Model-Client Protocol (MCP) connections-integrate and manage external LLMs (OpenAI, local, Anthropic, Gemini, etc.) via secure, pluggable adapters.
- User Management: Fully featured identity and access system supporting SSO, OAuth2, passwordless magic links, roles, groups, permissions, and user-scoped data boundaries.
- Group & Role Structures: Hierarchical group management for multi-tenant setups, classes, teams, and organizational structures.
- Content Management: File storage, search, tagging, metadata, document versions, and all media types (leveraging EnterMedia integration and your own systems).
- Real-Time Analytics (xAPI): Built-in learning record store (LRS), fine-grained event tracking, and real-time reporting, all scope-limited to the active Frame.
- API Gateway: REST and WebSocket endpoints for programmatic access, event listening, and automation hooks.
- Security & Isolation: Frame-level sandboxing-no data, user, or analytic leakage between Frames unless explicitly bridged.
- Extensibility: All major subsystems are modular-new modules, data types, and integrations can be hot-deployed with zero downtime.
Every other module, tool, or user experience in ThoughtFrame is powered and isolated by Core.
ThoughtFrame Library
ThoughtFrame Library is a universal, enterprise-grade repository for all your knowledge, documents, and media-supporting any file format and unlocking the data inside for AI, automation, and your teams. All content is indexed, enriched, and securely organized within each Frame.
- Any Format, Instantly Usable: Store, manage, and search PDFs, Office docs, images, audio, video, datasets, code, and more. Integrated extraction, OCR, and preview tools automatically unlock the content for search and processing.
- Metadata & Vector Search: Combine classic metadata queries (tags, custom fields, permissions) with modern vector search. Retrieve assets by natural language, semantic similarity, keywords, or custom filters-all at scale.
- RAG-Ready by Design: Every document and data asset is available to Retrieval-Augmented Generation tools and AI modules. Frame boundaries guarantee strict privacy and context-no leakage between teams or projects.
- Powerful DAM Foundation: Built on and fully compatible with EnterMedia: battle-tested asset management, versioning, workflows, permissions, and audit trails.
- Unified Access for All Tools: The Library is accessible to every part of the ThoughtFrame platform-LMS, code tools, chat, analytics, and automation workflows. All modules leverage the same source of truth.
- APIs & Integration: Full REST and WebSocket APIs for ingest, sync, export, and integration with S3, Google Drive, local storage, and third-party platforms.
AI & RAG Integration
- Instant AI Access: Files become AI-accessible on upload-ready for question answering, summarization, extraction, and chat, via both vector and metadata-based RAG.
- Secure, Contextual, and Auditable: All AI queries are scoped to the active Frame, with full event logging and usage tracking for compliance and transparency.
ThoughtFrame Library is the foundation for secure, discoverable, and AI-augmented knowledge-across any content, any module, any workflow.
ThoughtFrame LMS
ThoughtFrame LMS is a modular, production-grade learning management system for teaching, assessment, and skill-building-across classrooms, organizations, or private Pods. It brings AI-powered features, granular analytics, and universal content access to every learning context.
- Pod-Scoped Learning: Every class, team, or cohort has its own isolated Pod-containing users, assignments, analytics, and progress. Strict separation and data privacy by default.
- Real-Time AI Chat & Assistance: Each Pod includes instant, context-aware AI chat for tutoring, Q&A, explanations, and feedback-scoped only to that Pod’s content and participants.
- Library-Driven Content: All materials (documents, media, code, datasets, etc.) are pulled from the unified ThoughtFrame Library. AI and search are always available for any attached resource.
- Courses, Quizzes, Adaptive Flows: Full-featured authoring for content, assignments, coding challenges, auto-scored quizzes, and detailed feedback. Everything is analytics-ready and exportable.
- Live Analytics & Reporting: Track engagement and progress per user, per Pod, or per course, with real-time dashboards and reporting.
- Flexible Access & API Integration: Granular roles and permissions, SSO/OAuth support, and a fully-documented API for automation and custom workflows.
AI, real content, and deep analytics-combined in one Pod, for every learning environment.
ThoughtFrame Code
ThoughtFrame Code is a fully integrated code orchestration, assessment, and developer workflow engine-engineered for secure, scalable, and real-world technical evaluation and learning.
- First-Class Git Integration: Every Frame can be linked directly to one or more Git repositories-supporting full clone/push/pull, commit history, branches, tags, and granular file access. No simulated sandboxes: work with real repositories and production-grade flows.
- Gradle-Native Build/Test Automation: Leverage industry-standard Gradle for compiling, testing, static analysis, and dependency management. Automate builds, code linting, and end-to-end tests per Frame, user, or submission.
- Docker Orchestration for Security: All code execution and builds run in isolated Docker containers for perfect reproducibility, security, and resource control. Fully supports custom images and environment configuration for different stacks and languages.
- Applicant Testing & Tech HR Workflows: Deliver real coding challenges, take-home projects, or live technical interviews with true Git workflows. Assignments are tracked, versioned, and auto-scored by triggers (commit, PR, or manual event).
- Frame-Scoped Collaboration: Candidate and reviewer workspaces (Frames) are isolated-enabling secure peer review, threaded feedback, inline comments, and collaborative learning for classrooms or teams.
- Pluggable Code Analysis & AI Review: Integrate custom static analysis, code similarity, test harnesses, or AI-driven code review (LLM function calls, RAG-driven code search, etc.) for each Frame.
- Actionable Analytics: Every build, commit, test, or review is tracked, timestamped, and reportable-enabling deep analytics for HR teams, instructors, and candidates.
- Flexible API & Automation: Trigger CI/CD events, auto-grade assignments, or integrate with external HR, DevOps, or LMS systems via REST/WebSocket APIs.
ThoughtFrame Mesh Skunkworks
ThoughtFrame Mesh is our next-generation, experimental module for non-linear knowledge management and collaboration-currently in active development as a “skunkworks” project, with early-access features available in select Frames.
- Visual Knowledge Graphs: Organize and traverse documents, concepts, tasks, or people as linked nodes within each Frame. Mesh enables complex, contextual relationships-beyond simple folders or hierarchies.
- Live Graph Editing & Exploration: Drag, drop, and link resources, ideas, or assignments. Build, refactor, or annotate learning flows and knowledge maps collaboratively.
- Embedded AI & Search: Mesh nodes are AI-aware-enabling semantic search, RAG, and question answering that follows links and connections, not just flat lists.
- Rich, Frame-Scoped Context: Every Mesh exists entirely within a Frame-guaranteeing strict privacy, context, and collaborative boundaries.
- Open, Extensible API (in progress): Early developer hooks for exporting, syncing, or automating Mesh graphs-future plans include LLM-assisted graph generation and multi-frame Mesh federation.
- Inspired by Real Use Cases: Mesh is being shaped by real-world R&D, curriculum design, RAG testbeds, and AI-assisted project management-always dogfooded before public launch.
ThoughtFrame Chain
ThoughtFrame Chain provides a unified layer for real-time blockchain data indexing, extraction, and event generation across multiple chains and protocols. It enables search, analytics, and AI to operate on meaningful, structured on-chain data-serving as the foundation for advanced monitoring, alerting, and automation tools.
- Cross-Chain Indexing: Aggregates and normalizes transactions, events, and protocol data from Ethereum, Bitcoin, Tron, and other networks. Structured storage allows instant querying and replay.
- Semantic Event Extraction: Parses raw blockchain activity into high-level events-transfers, swaps, position changes, liquidations, payments-enriched with metadata and ready for downstream consumption.
- Search & Analytics Ready: All indexed data is accessible for advanced search, AI-driven analysis, reporting, or custom workflow triggers. Enables ad hoc queries on user accounts, tokens, protocols, and address histories.
- Event Bus & Trigger System: Every extracted event can emit signals to connected applications-supporting real-time notifications, dashboards, or external automation.
- Foundation for Monitoring & Alerting: Built to be the core analytics substrate for whale tracking, wallet monitoring, payment detection, and compliance systems. You define the business logic; Chain provides the data, events, and search.
- Open & Extensible: Add new chains, protocol decoders, and custom event types with minimal friction-extensible API for integration with any analysis or alerting stack.
ThoughtFrame Chain powers data extraction, indexing, and event-driven analytics for crypto and DeFi-enabling rich search, AI, and automation across all chains and protocols.
Request a Demo / Your Own Frame
Want to see ThoughtFrame in action, or try your own private Frame with real modules and data?
Email us at info@thoughtframe.ai and we’ll get you set up.