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ContextForge.dev
Agentmemory
OpenMemory MCP
ContextForge is persistent, searchable memory for AI coding agents โ built on the Model Context Protocol (MCP).
Your AI assistant forgets everything when the session ends. ContextForge fixes that: save architectural decisions, naming conventions, and debugging context once, and any MCP client recalls it later with semantic search โ across sessions and across projects.
Works with: Claude Code, Claude Desktop, Cursor, GitHub Copilot, ChatGPT, and Windsurf.
Runalyze ContextForge.devContextForge.dev's answer:
ContextForge is memory that lives at the MCP layer, so it works across every AI coding agent at once โ Claude Code, Cursor, GitHub Copilot, ChatGPT, and Windsurf โ not just one. Save a decision once and any client recalls it later with semantic search. It goes beyond a note store: automatic git sync turns your commits and PRs into searchable knowledge, plus task tracking, snapshots, and team sharing โ all through a single MCP server you add with one command.
ContextForge.dev's answer:
Most memory tools are tied to a single agent or are just a key-value store. ContextForge is MCP-native, so it's portable across all your AI tools; it adds git sync so your codebase history becomes searchable context automatically; and it includes team features (shared spaces, collaborators) that solo-memory tools lack. Setup is one command, there's a genuine free-forever tier with no credit card, and paid plans start at just $9/month.
ContextForge.dev's answer:
Software developers and engineering teams who use AI coding assistants โ Claude Code, Cursor, GitHub Copilot, ChatGPT, Windsurf โ and are tired of re-explaining their project, architecture, and conventions every session. It fits solo developers working across multiple projects as well as small teams that need shared, persistent context.
ContextForge.dev's answer:
ContextForge was born from a simple frustration: AI coding agents forget everything the moment a session ends. Every new conversation meant re-explaining the same architecture, naming conventions, and past decisions. ContextForge was built to give AI agents a permanent, searchable memory through the Model Context Protocol โ so knowledge is captured once and reused forever, across sessions and projects. It even dogfoods its own memory to help build itself.
ContextForge.dev's answer:
Next.js 16 (App Router), React and Tailwind CSS for the dashboard, hosted on Vercel. Supabase (PostgreSQL) with pgvector powers the semantic vector search, and Deno edge functions serve the API. Embeddings use OpenAI text-embedding-3-small. The MCP client is a Node.js package (contextforge-mcp) on npm, implementing the Model Context Protocol.
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I wasn't sure where to set expectations given my interrupted training, but I decided to aim for 3:03:00, as that's where my Garmin and Runalyze predicted my finish. I planned to set out at a 6:55/mi pace and adjust based on feel. Source: over 2 years ago
Pre-race, Garmin estimated a 3:00:xx, and Runalyze, another great website, thought I'd hit 3:01:xx. This was slightly discouraging, but I know they're just estimates so I didn't take it personally. Source: almost 3 years ago
Has anyone else tried Runalyze (https://runalyze.com) to track running progress and performance? Source: almost 3 years ago
Https://runalyze.com/ is a great tool that can do this! Source: about 3 years ago
I'm currently 5'9" and 82kg and just ran a half marathon in 1:47 a few weeks back. I lost weight while training, but started off barely being able to break an 11 minute mile back in October. The 255 would work just fine for you. If you enjoy the data side of things, there are great sites that link up to Garmin (I use runalyze.com and intervals.icu) for more information. Source: about 3 years ago
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