Technical deep dive into RecCall's architecture and ML-powered context management
RecCall uses a unified context model that supports three types of contexts:
Manually created templates for project documentation, coding standards, and architectural decisions. Perfect for onboarding and reference.
Automatically generated from conversations using ML. Captures summaries, key topics, code references, and embeddings for semantic search.
Static templates enhanced with ML insights. Combine the structure of templates with the intelligence of dynamic context.
RecCall's ML layer automatically processes conversations to extract meaningful context artifacts.
The ML Intelligence Layer processes conversations through multiple pipelines to generate rich context artifacts.
Extracts key points and generates concise summaries from conversations, preserving important decisions and insights.
Identifies key topics and themes from conversations, enabling better organization and discovery.
Finds code blocks, file references, and line ranges mentioned in conversations, creating traceable links.
Generates vector embeddings for semantic search, enabling intelligent context matching based on meaning.
RecCall combines keyword and semantic search to find the most relevant context quickly.
Fast text-based search for exact matches, file names, and specific terms.
AI-powered search that understands meaning and intent, finding related context even without exact keyword matches.
RecCall provides multiple ways to access and manage context, fitting into any workflow.
Model Context Protocol integration for AI IDEs like Cursor. Automatic context injection.
Command-line interface for terminal-based workflows. Full context management from command line.
HTTP API for web applications and custom integrations. Express.js and Fastify adapters available.
JavaScript SDK for programmatic access. Python and Go SDKs coming soon.
Local file-based storage with atomic writes and in-memory indexing. Fast and reliable for single-user scenarios.
Version-controlled context repositories. Share and sync contexts across teams using Git.
Enterprise storage backends for scalable deployments. ACID compliance and high-performance caching.
All context data stored locally by default. No data leaves your machine unless explicitly configured.
Storage backends support encryption at rest. Enterprise deployments can use encrypted databases.
Context-level permissions and sharing controls. Manage who has access to which contexts.
Get started in 2 minutes and see how it works.