CodeLedger

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 turns every coding action into a persistent, compounding asset.

Without CodeLedger:

• Context is lost between agents
• Every task starts from scratch
• Failures repeat

With CodeLedger:

• Context persists across sessions and agents
• Success patterns compound
• Engineering becomes measurable and auditable

This is not another AI tool. This is a Context Control Plane for your repo.

Works with: Claude Code | Cursor | Codex | GitHub Copilot | Gemini CLI | Any CLI-based agent

What happens when you use CodeLedger?

1. You run a task
2. CodeLedger selects context deterministically
3. The outcome is recorded
4. Future tasks improve automatically

Over time, your repo builds its own intelligence layer.

The Two-Loop Model

CodeLedger helps in two ways:

⚡ Now — Assembles the minimal context needed for your current task. Fewer irrelevant files, fewer retries, faster execution.

💎 Next — Captures what worked and builds reusable memory so future tasks start smarter. Successful patterns compound into institutional knowledge.

⚡ Now                          💎 Next
Select context → Execute →      Record evidence → Promote patterns →
                                Future tasks start smarter

AI Agent Integration (MCP)

CodeLedger includes an MCP server that gives Claude, Cursor, and Windsurf direct access to your repo's memory:

codeledger mcp start     # Launch MCP server (stdio transport)
codeledger mcp status    # Check readiness + connection instructions

Tools available to agents:

• query_ledger — Search for verified patterns before coding
• get_active_context — Get the task-specific context bundle
• record_interaction — Report outcomes for memory compounding

MCP integration requires Team or Enterprise tier. See Feature Tiers.

Engineering Dashboard

Generate a repo-local engineering dashboard from your Context Ledger:

codeledger dashboard build    # Generate static HTML dashboard
codeledger dashboard open     # Open in browser (no server needed)

The dashboard shows: system health, integrity signals, quality metrics, pattern reuse intelligence, and estimated engineering value — all derived from real execution evidence.

Full dashboard requires Team or Enterprise tier. Individual tier receives a placeholder with teaser stats.

Semantic Merge Verification

Prevent silent merge failures where code compiles but types or config are semantically broken:

codeledger merge-check --save-baseline    # Before parallel work
codeledger merge-check --verify           # After pulling a merge
codeledger merge-check                    # Quick health check

Catches removed types with active importers, config fields accessed but missing from defaults, and name collisions across packages.

Feature Tiers

codeledger features           # See what's available at your tier
codeledger upgrade            # Explore Team / Enterprise

Why this matters

CodeLedger is built on a local-first Context Ledger:

• Append-only memory of engineering activity
• Outcome-linked learning (what worked vs what failed)
• Cross-agent continuity
• Deterministic context selection

Your repo becomes an evolving system — not just code.

Get CodeLedger

Download Latest Release · npm install -g @codeledger/cli · Getting Started Guide · CLI Command Reference

npm install -g @codeledger/cli   # or download the zip from Releases
cd your-project
codeledger ready
git add .codeledger/bin/ .gitignore
git commit -m "chore: init codeledger"
./.codeledger/bin/codeledger task --task "Fix null handling in user service"
codeledger task --task "Fix null handling in user service"
# Your agent now has .codeledger/active-bundle.md with the right context

Your agent reads the right files first. Every time.

For browser/cloud sessions, the committed .codeledger/bin/ runtime package is what gets executed. codeledger ready initializes the repo, scans it, and deploys the canonical standalone build so the checked-in runtime matches the version you tested locally. Inside a vendored repo, ./.codeledger/bin/codeledger <command> is the easiest interactive entry point.

Truth Control Plane

Once a repo is initialized, CodeLedger can reconcile reality across drift, outcomes, snapshots, handoffs, and release state:

codeledger drift --history --verify-integrity
codeledger outcome --json --verify-integrity
codeledger harvest --preview --verify-integrity
codeledger context-handoff --target codex --verify-integrity
codeledger snapshot --verify-integrity
codeledger time-travel --to <snapshot-id> --verify-integrity
codeledger reality-check --verify-integrity

These commands extend CodeLedger's existing verification and memory systems. They do not create a separate ledger or memory store.

What Happens After Install

Installing CodeLedger gives you the CLI. To use it in a project, initialize that project:

codeledger init

That sets up CodeLedger inside the repo by creating:

• .codeledger/ for project-local cache, bundles, sessions, and runtime data
• .codeledger/bin/ for the vendored standalone runtime
• .claude/hooks.json for automatic integration
• updates to CLAUDE.md so agents know how to use the context bundle

Your normal flow after install is:

1. Install CodeLedger

npm install -g @codeledger/cli

2. Go to your project

cd your-project

3. Initialize and scan CodeLedger in that repo

codeledger ready

4. Commit the vendored runtime if you want browser/cloud support

git add .codeledger/bin/ .gitignore
git commit -m "chore: initialize codeledger"

5. Start using it

codeledger ready --task "your task here"

🌐 Beyond CodeLedger

CodeLedger is just the beginning.

It’s built on ContextECF — a broader context infrastructure approach for making AI systems deterministic, auditable, and cumulative instead of probabilistic and forgetful.

At the core is a simple idea:

Every interaction with AI should make the system smarter, more reliable, and more accountable over time.

🧠 What This Means (In Practice)

While you’re using CodeLedger for development, the same foundation extends to:

• 🧑‍💻 Engineering → Shared memory across agents, verified execution, no rework
• 📊 Decision-making → Faster, context-rich executive decisions
• 🗂️ Knowledge → Institutional memory that doesn’t disappear
• 🤖 AI agents → Coordinated, governed, and trustworthy systems
• 💬 Communication → Smarter meetings, better alignment
• 📈 Revenue → Relationship intelligence and proactive insights
• 🔐 Governance → Built-in auditability and compliance

🚀 Why It Matters

Most AI tools:

• Recompute context every time
• Lose what they learned
• Can’t prove what happened

ContextECF + CodeLedger:

• Remember
• Verify
• Compound value over time

What you’re building isn’t just output.

You’re building a long-term context asset for yourself — and potentially your entire organization.

🤝 Share This

If CodeLedger is helping you, it’s worth a quick share with:

• CTO / VP Engineering
• Platform / DevEx teams
• AI / Data leaders

🏢 About

Intelligent Context AI Inc is the creator of CodeLedger and ContextECF.

If your team is exploring AI at scale, agent systems, or enterprise context management:

📩 customersuccess@intelligentcontext.ai
📞 916-753-7432

CodeLedger helps you capture truth in code.
ContextECF helps you scale that truth across the enterprise.