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ContextForge.dev
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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.
Prometheus
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.
Based on our record, Prometheus seems to be more popular. It has been mentiond 300 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Prometheus scrapes metrics from the stack. Node exporter covers the host, cAdvisor covers containers, and individual services expose their own endpoints where supported. The main value isn't dashboards (though those exist) - it's having a queryable record of system state over time, and a place to hook alerts when something drifts. - Source: dev.to / 26 days ago
Prometheus is the industry-standard time-series database for infrastructure metrics. Paired with Grafana for visualization and Alertmanager for routing, it forms the backbone of monitoring at companies from startups to Netflix-scale deployments. This isn't a single tool โ it's an ecosystem. - Source: dev.to / about 1 month ago
To monitor and analyze rate limiting metrics, we're using a combination of Redis and Prometheus. We're storing rate limiting metrics in Redis and then using Prometheus to scrape the metrics and display them in a dashboard. Here's an example of how we're storing rate limiting metrics in Redis:. - Source: dev.to / about 1 month ago
In this post, we compare two forecasting models, Chronos (ChronosโBolt) and Toto, on telemetry from Prometheus and OpenSearch. We judge them with two easy metrics: MASE for point accuracy and CRPS for the quality of uncertainty. - Source: dev.to / about 2 months ago
For monitoring infrastructure, Prometheus and Grafana are widely used for pipeline metrics collection and alerting. For orchestration that includes built-in run observability, Apache Airflow tracks run history, task durations, and failure states in a web UI. Python with SQLAlchemy is the standard stack for custom pipeline implementation with relational state management. - Source: dev.to / 3 months ago
Grafana - Data visualization & Monitoring with support for Graphite, InfluxDB, Prometheus, Elasticsearch and many more databases
Agentmemory - Persistent memory for Claude Code, Codex & coding agents
Datadog - See metrics from all of your apps, tools & services in one place with Datadog's cloud monitoring as a service solution. Try it for free.
OpenMemory MCP - Your private, local memory layer for all AI tools
NewRelic - New Relic is a Software Analytics company that makes sense of billions of metrics across millions of apps. We help the people who build modern software understand the stories their data is trying to tell them.
Zabbix - Track, record, alert and visualize performance and availability of IT resources