CodeLedger is a Context Control Plane that captures and retains context across coding tasks, enabling agents to build a persistent memory of successful patterns and outcomes. It integrates with various AI agents to enhance coding efficiency and knowledge retention.
From the registry: Deterministic context selection for AI coding agents. Local memory that compounds across sessions.
$ npm install -g @codeledger/cli$ codeledger initPlease install the `codeledger` MCP server into my current AI client (that's you).
Required prerequisites (do these first if not already done):
- **Install @codeledger/cli globally** — Install the CodeLedger CLI globally via npm. Run: `npm install -g @codeledger/cli` (https://github.com/codeledgerECF/codeledger)
- **Initialize CodeLedger in repo** — Run `codeledger init` in your project root to set up the .codeledger directory. Run: `codeledger init`
- **Team or Enterprise tier** — MCP server access requires Team or Enterprise tier.
Canonical MCP server config (stdio transport):
- command: `codeledger`
- args: ["mcp","start"]
Note: MCP server is gated behind paid Team/Enterprise tiers.
Add this MCP server to my current client's config in the correct format for you. If you need secrets or credentials I haven't provided, ASK me — do not invent values or leave raw placeholders. After adding it, tell me how to verify the server is connected.Real-time infrastructure monitoring with metrics, logs, alerts, and ML-based anomaly detection.
Persistent codebase knowledge graph. Survives session restarts and context compaction.
Validate oh-my-posh configurations and segment snippets against the official schema.