Codag

Organizational memory
for coding agents.

AI generates 41% of production code. Every agent writes blind. The senior engineer is still the bottleneck on every meaningful task.

Codag mines years of engineering decisions from git history and serves them to any agent before it writes. Sub-millisecond. No model in the query path.

$ curl -fsSL https://codag.ai/install.sh | bash
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The problem

More code ships.
Less of it is understood.

Teams that handled 10–15 PRs a week are now facing 50–100. Engineers skim, rubber-stamp, or pass reviews to AI tools. The codebase grows but the team's understanding of it doesn't. Every shortcut compounds.

Agents can write code. They can't make decisions.

What's been tried, what failed, what breaks if you touch the wrong thing. That knowledge originates from people — code reviews, architecture decisions, incident retros — and accumulates over years. Agents have no way to access any of it.

You can't spin up ten senior engineers.

Every meaningful AI-generated PR still requires the scarcest, most expensive person on the team to steer it. You can spin up ten agents. You can't scale the people who know why the code works the way it does.

The knowledge is trapped.

It lives in people's heads, PR threads, and Slack conversations. Internal docs go stale the day they're written. Custom tooling costs $150K+ and still doesn't reach the agent. When people leave, their context vanishes.

The tools that created the problem can't solve it.

Every AI coding tool ships more code, less of it understood, widening the gap every sprint. MIT found 95% of enterprise AI pilots fail because generic tools don't adapt to how a team actually works. Context is the bottleneck.

The solution

The senior engineer's knowledge. In the loop. Without them in the loop.

Point Codag at a repo. It extracts years of decisions autonomously from git history, PR discussions, code reviews, and issues. No engineer effort required.

Extraction, not retrieval

A model runs once at index time. Distills natural-language discussions into structured, file-level signals: what's dangerous to change, what was tried and rejected, what patterns to follow, why something was built the way it was.

Signals, not documents

Each signal is scored by severity and traced to its source. When an agent modifies a file, it gets 3–5 terse directives. Not a wall of text. Not a summary. Actionable constraints.

Sub-millisecond. No model in the query path.

Intelligence is baked in at index time. Serving is instant and deterministic. No cost per query. No latency. No degradation as the codebase grows.

One MCP server. Entire ecosystem.

Cursor, Copilot, Claude Code, Windsurf — any MCP-compatible agent queries Codag automatically. Not a replacement for any of them. A multiplier on all of them.

// what this looks like in practice

Agent in Cursor is about to modify a file. Codag returns:

CRITICAL — CTO rejected this exact refactor in PR #847. See review comment.
WARNING — This file changed with auth_middleware.py and session_store.py in every PR that touched it. Don't modify in isolation.
DANGER — Last engineer who removed the async wrapper caused a production outage. See incident #312.

How it works

Five minutes to memory.

Three components: Brain (extraction engine), CLI (MCP server), Console (team dashboard). One install command.

1

Install CLI, connect repo

curl -fsSL https://codag.ai/install.sh | bash — one command. Done.

2

Codag indexes your history

Git history, PRs, issues, code reviews. ~10 minutes for ~200 PRs.

3

Extraction model distills signals

Natural language from human discussions becomes structured, file-level signals. Scored by severity. Traced to source.

4

MCP server starts

Any compatible agent queries automatically. No integration work. No config.

5

Memory compounds

Webhooks keep the index current. Every merged PR, every code review, every reverted commit adds to the memory automatically. Zero maintenance.

Benchmarks

Real results. Real repos.

Blind A/B judging. 50 prompts. Real open-source repos (SQLModel, dirty-equals). Both Gemini Flash and Claude improved with Codag's context.

0123456780%

Win rate (blind A/B)

31.6%

Fewer tokens

36.5%

Lower cost

Beat GitHub's own MCP server 10–5 head-to-head using 62% fewer tokens. GitHub MCP dumps raw PR text. Codag serves distilled, actionable signals.

// why: agents stop wasting effort on approaches the team already tried and rejected

Pricing

Simple, transparent pricing.

Start free. Pay as your team grows. No surprises.

Free

For trying it out

$0
Get Started
✓ 1 public repo
✓ 50 API calls / month
✓ MCP server (brief + check)
✓ Severity 3+ signals
— Console dashboard
— API keys
— Webhook auto-reindex

Individual

Popular

For solo developers

$9 /mo
Start Trial
✓ 3 repos (public + private)
✓ 1,000 API calls / month
✓ MCP server (brief + check)
✓ All severity levels (2-5)
✓ Console overview
✓ 1 API key
✓ Webhook auto-reindex

Team

For engineering teams

$22 /dev/mo
Start Trial
✓ 15 repos
✓ Unlimited API calls
✓ Up to 15 active developers
✓ All severity levels (2-5)
✓ Full console + usage analytics
✓ 10 API keys per user
✓ Organizations + RBAC
✓ Context, owners, history APIs

Enterprise

For large organizations

Custom
Contact Us
✓ Unlimited repos
✓ Unlimited API calls
✓ Unlimited developers
✓ Unlimited API keys
✓ Unlimited organizations
✓ Self-hosted option
✓ Dedicated support
✓ Custom integrations

The category

$100M+ is flowing into agent memory.

Everyone is converging on the same thesis: AI can write code, but the infrastructure to make it trustworthy hasn't been built.

Entire

$60M seed

GitHub's ex-CEO, $300M valuation. Captures forward-looking context — prompts, transcripts, checkpoints. New platform. Context starts fresh.

Mem0

$24M raised

YC-backed, a16z. General-purpose AI memory layer. No understanding of git history or software workflows.

Letta

$10M raised

Felicis-backed. MemGPT research team. Stateful agents with long-term memory. General-purpose, not code-specific.

Cursor & Cognition

shipping features

Cursor shipped Blame. Cognition launched Agent Trace. Converging on memory as a feature, not a product.

Where Codag fits

Every competitor reads code and tries to understand it. Codag reads the human decisions that shaped the code. Entire captures knowledge only after teams adopt their platform. We mine years of it from what already exists. Value on day one. No platform migration. Memory compounds. Code snapshots don't.

Stop re-explaining your
codebase to agents.

// one command. zero config. memory grows with every merge.

$ curl -fsSL https://codag.ai/install.sh | bash
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