MegaMemory and context-engineering

MegaMemory
50
Established
context-engineering
50
Established
Maintenance 10/25
Adoption 8/25
Maturity 20/25
Community 12/25
Maintenance 10/25
Adoption 6/25
Maturity 15/25
Community 19/25
Stars: 59
Forks: 7
Downloads:
Commits (30d): 0
Language: TypeScript
License: MIT
Stars: 17
Forks: 17
Downloads:
Commits (30d): 0
Language: Python
License: MIT
No risk flags
No Package No Dependents

About MegaMemory

0xK3vin/MegaMemory

Persistent project knowledge graph for coding agents. MCP server with semantic search, in-process embeddings, and web explorer.

MegaMemory helps AI coding agents remember project details across different work sessions. It takes natural language descriptions of code concepts, architecture, and decisions, then allows the agent to semantically search and recall these details for future tasks. This tool is for developers who use AI coding assistants like OpenAI Codex, Claude Code, or Antigravity and want them to maintain a consistent understanding of a project over time.

AI coding assistant developer tools knowledge management software development agent workflow

About context-engineering

timothywarner-org/context-engineering

🧠 Stop building AI that forgets. Master MCP (Model Context Protocol) with production-ready semantic memory, hybrid RAG, and the WARNERCO Schematica teaching app. FastMCP + LangGraph + Vector/Graph stores. Your AI assistant's long-term memory starts here.

This project helps you build AI assistants that can 'remember' past interactions and information, preventing the common problem of AI forgetting context. You feed it data and instructions, and it produces an AI system with robust long-term memory capabilities. This is for AI developers, researchers, and engineers who want to create more intelligent and consistent conversational AI.

AI-development conversational-AI large-language-models semantic-memory AI-engineering

Scores updated daily from GitHub, PyPI, and npm data. How scores work