Every AI coding agent starts the same way: searching the entire repo for context. It wastes tokens on irrelevant files, misses dependencies, and builds things that already exist. Then you spend the next hour undoing its work.
CodeLedger gives your agent the right files, the right constraints, and the right patterns — before it writes a single line.
Works with Claude Code · Cursor · Codex · Gemini CLI · Windsurf · any CLI-based agent
MCP integration · Multi-session ready · Zero-config hooks
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What problem are we solving?
The Problem — AI coding agents waste 40–60% of their context window on irrelevant files. Every session starts cold. Institutional knowledge lives in people’s heads and disappears when they leave. There is no risk signal before a merge.
The Solution — CodeLedger is a deterministic context control plane for software development. It scores every file in a repository, selects only what the current task requires, captures outcomes, and promotes successful patterns into reusable institutional memory.
The Intelligence Layer — The Task Intelligence Engine does not start from zero. It is seeded from day one with a curated ontology pack of golden patterns — distilled from peer organizations and leading engineering teams at organizations including Google, SAP, and Salesforce. As your team uses CodeLedger, your own earned patterns layer on top, making the system progressively more tailored to your codebase, your conventions, and your standards.
The Principle — No cloud. No training pipeline. No behavior change required. Engineering management installs it once. Every developer and every AI agent benefits automatically — from collective intelligence on day one, and from your own institutional memory from day two onward.
Logs are history. Ledger is intelligence.
CodeLedger tracks every task from intent to outcome. Each phase produces evidence, not just output.
Scans your repo, scores every file, and delivers a focused context bundle. The Task Intelligence Engine refines vague prompts and reports ISC confidence scores.
Your agent works within the context bundle. Discovery Gate checks for existing implementations. Broker delivers structured context to any surface.
Review Intelligence detects missing validation, unguarded I/O, circular dependencies, and brittle tests. Completion Integrity Check verifies claims against the diff.
Session summary shows recall, precision, and context reduction. Successful patterns are promoted. Failure hotspots compound into anti-patterns.
Not another wrapper. A full context control plane with deterministic scoring, architectural verification, and compounding intelligence.
12 weighted scoring signals per file. Dependency graph, git churn, test mappings, co-commit temporal index. Same task, same repo, same bundle.
Evaluates prompt clarity (ISC scoring), refines vague tasks, and reports prompt lift. "fix bug" becomes a scoped, actionable task with 50%+ ISC improvement.
Detects missing runtime validation, unguarded HTTP calls, circular dependencies, and brittle test patterns. Auto-fix available. Zero configuration.
Scans for existing implementations before you build. Verdicts: GO (new), EXTEND (overlap), or NO_GO (already exists). Prevents duplicate systems.
Outcome truth, agent scorecards, destabilization metrics, and value compounding. Answers: "Is AI making the codebase more stable?" with deterministic evidence.
Exposes context as MCP tools for Claude Desktop, Cursor, and Windsurf. Activation enforcement ensures no context is delivered without task linkage.
Explains what happened (explain), shows recurring patterns (learnings), and recommends next actions (next). All deterministic, no LLMs.
5-component health score: duplication risk, extension discipline, source-of-truth stability, override frequency, and discovery coverage. Includes intervention engine.
Compare legacy and candidate implementations side by side before rollout. 5 comparators, severity classification, CI-grade gate. Never trust a refactor until the new code proves itself.
Detects prompts that risk creating parallel systems or duplicate truth. Progressive intervention: none, light cue, guided refinement, two-phase stop. Seeded from 5 architectural doctrine concepts.
# Install and initialize npm install -g @codeledger/cli cd your-project codeledger init # Activate — scan + score + bundle + task intelligence codeledger activate --task "Fix auth middleware to handle expired JWT tokens" Bundle: 11 files, ~8508 tokens | Confidence: HIGH Task Intelligence ISC: 0.97 [##########] sufficient Type: auth_change (confidence: 0.50) # Verify — architectural checks before PR codeledger verify --task "Fix auth middleware" # Session recap — how well did the bundle predict your changes? codeledger session-summary Bundle predicted 8/9 files you changed (89% recall) Context: ~8.5K tokens vs ~2.1M full repo (99% reduction)
AI agents are powerful — but without the right context, they're expensive guessing machines.
Your agent reads 50 files to find the 5 that matter. You pay for the other 45 in tokens, latency, and hallucinated suggestions.
Without knowing your codebase's structure, agents create duplicate systems. You discover the overlap during code review — or worse, in production.
Every session starts from zero. Patterns that worked yesterday are forgotten today. Failures repeat. The agent never learns your codebase.
Engineering managers ask: "Is AI making us faster?" You have vibes, not evidence. No recall metrics, no outcome truth, no audit trail.
CodeLedger's Task Intelligence Engine is powered by Insight Packs — a curated ontology of 95+ prompt engineering signals and 20+ coding patterns drawn from leading AI platforms and academic research.
Curated from public best-practice documentation published by:
Meta-prompting, safety, chain-of-thought expansion, few-shot, structured output, self-critique, and enterprise governance.
After scanning your codebase, CodeLedger generates a repo-specific pack that weights signals based on your language, framework, and patterns.
The Engineering Intelligence Dashboard answers the question every engineering leader asks: "Is AI making the codebase more stable — and can you prove it?"
Overview, Integrity, Quality, Knowledge, Efficiency, and Value — all computed from your actual CodeLedger session data. No synthetic, no estimates.
Outcome truth, agent scorecards, destabilization metrics, and value compounding. Currently in proof-of-concept mode with synthetic scenarios — real data integration on the roadmap.
Enterprise tier includes automatic dashboard deployment to GitHub Pages on your organization's account — real-time data plus a synthetic welcome scenario to explore the full feature set.
codeledger serve runs a local dashboard server. codeledger dashboard build generates static HTML. Docker and Kubernetes templates included.
Individual tier includes deterministic context selection, task intelligence, review intelligence, and the full insight system — no credit card required. Team and Enterprise tiers add MCP integration, the full dashboard, architecture governance, and fleet-wide visibility.