Multi-Agent Coordination &
Memory Layer

Reduce your rework, review overhead, and context bloat in AI code generation.

Kawa Code provides a state-of-the-art AI harness that autonomously organizes intents and decisions, inferred from your prompts or your agents. It then injects relevant, critical information into your workflow, such as past decisions and intent conflicts.

Token consumption comparison: Kawa Code reaches the right solution in one step (~590k tokens) while a standard LLM harness keeps climbing across several debugging cycles (~2500k tokens)
Kawa Code can often one-shot the right solution, while a standard harness requires several debugging cycles

60-second demo — see it in action

Start Free Dive in

Free 14-day trial — no credit card required.

Today's AI tools assume:
more context = better decisions.

That assumption is wrong. LLMs degrade with context bloat — relevance dilutes, contradictions compound, attention thins, cost climbs.

Biology already solved this. No cell dumps its whole genome into every reaction. That's what Cursor rules do. That's what Memory MCP does. That's what most context tooling does today. Cells would die if they tried.

Kawa Code makes the opposite bet. We curate. We deprecate. We express only what matters, only when it matters. The way a cell expresses a gene.

Kawa Code: your project's decision genomics.

60-second demo — see it in action

From capture to expression

Every intent and decision flows into the AI genome. Only the relevant subset surfaces back to the current task.

Decision Genome workflow: intelligence capture (chat, prompts, thought-chains, GitHub) flows into the Decision Genome archive, which is curated and selectively expressed as relevant decisions surfaced to the current task.

AI writes the code. The reasoning evaporates.

? + −

You can read what an agent produced — you can't see:

why this approach, over the ones it discarded
which constraint it was honoring
which earlier decision it quietly contradicted

Kawa Code keeps the why — it captures the micro-decisions behind each change as they happen, and tracks how they evolve over time. The reasoning travels with the code instead of vanishing the moment the diff lands.

“But why not just Claude Code + a wiki?”

One agent makes dozens — sometimes hundreds — of micro-decisions per session. No human can write them all down and triage them by hand.

A wiki waits for you to write it. Kawa Code captures decisions automatically, from the work you're already doing.
Notes go stale; pasting context just buries the model in noise. It surfaces only the few decisions that matter — right inside your agent's workflow.
A doc just sits there. Kawa Code acts on the reasoning — flagging conflicts and keeping humans and agents aligned across sessions.

A wiki remembers. Kawa Code reasons with you.

How Decision Genomics works

Where Decision Genomics sits

Memory tools recall. Context tools inject. Orchestrators coordinate. None of them structure or evolve the reasoning behind your code — that's the layer Kawa Code adds.

Layer What it does Who's here today
Long-term / vector memory Generic recall of past text mem0, Letta, RAG
Context injection Feeds stored context into the prompt Cursor rules, Memory MCP
Agent orchestration Coordinates agents on a task Agent frameworks
Decision Genomics Kawa Code Structures & evolves the reasoning behind changes — expresses only what matters, and aligns humans + agents around it — the missing layer

Frameworks vs. Kawa Code

Agent harnesses run a single task well. Kawa Code is the layer that lets every task — human or agent — build on the judgment of the ones before it.

Axis Agent frameworks & harness tooling Kawa Code
What's remembered Execution state and retrieved text — messages, tool outputs, document chunks The reasoning behind code changes — intents and decisions, the why behind one approach over another
Who it serves One agent's single run Humans and agents together, coordinating across many sessions
Time horizon Until the thread ends — then discarded Outlives the session, the run, even the developer
Bound to A process or thread ID A place in the code — anchors that survive rebases
Checkpoint

A checkpoint is mechanical, ephemeral, single-process, machine-readable state whose only purpose is to resume this run. When the thread dies, it's garbage.

Intent

An intent is semantic, durable, multi-actor, human-meaningful commitment whose purpose is to inform whoever works here next. It's anchored to code and queryable forever.

Four pillars, working as one. Together they form the project's decision genomics.

01

Capture

Automatic instrumentation of decisions from communication channels, code, and AI conversations. The team doesn't write reasoning down — the system extracts it.

02

Curate

Structured types, evolution graph, deprecation. The decision layer prunes itself — instead of accumulating monotonically like every other AI-memory tool.

The technical moat
03

Surface

Relevant decisions, surfaced at the right moment. Past reasoning appears at the moment of work — not when you remember to search for it.

04

Align

Conflict and intersection detection across the team — before merge time, before architectural drift.

Other tools accumulate. Kawa Code curates.

Most AI coding workflows optimize for accumulation: more rules, more chats, more embeddings, more retrieved context. Over time, that creates context bloat — irrelevant information, stale assumptions, and contradictory past decisions competing for the model's attention.

Kawa Code takes a different approach. It captures the why behind architectural and implementation decisions, curates that knowledge over time, and surfaces only the intent relevant to the current task. The result is faster reasoning, more consistent outputs, and AI systems that retain long-term coherence as projects evolve.

See It In Action

Real AI efficiency multiplier

One minute demo

Watch how Kawa Code captures development intent in real time — recording decisions as they happen during AI-assisted coding sessions.

No workflow disruption. No manual documentation. Just quiet, continuous memory.

Avoid working on the wrong fix

One minute demo

Watch how Kawa Code helps Claude Code correctly identify the issue, instead of trying to fix the wrong part of the system.

Faster development, higher quality. No more back-and-forth on endless debug cycles.

Kawa Code team collaboration in IntelliJ

Collaborative for both human and AI

Real-time teamwork visibility

See where teammates are working before changes are committed. Intersection detection highlights overlapping edits across your team — so you coordinate early, not at merge time.

Kawa Code translating code into natural languages

Bonus extension: Showing code in your native language

Open your doors to non-English speakers

All the code generated by AI or human contributors can be automatically translated into any human natural language, to make reading the code and validating logic available to anyone on the planet.

Trunk-Based Dev Meets Agentic Velocity.

The Problem

Trunk-based development works because humans code at a predictable pace. When you introduce AI agents pushing code 100x faster, your main branch becomes a bottleneck. Automated tests catch broken syntax after the push, but they can't see conflicting architectural decisions before they collide.

The Shift

Kawa Code is the real-time Situation Room for human-AI teams. We map the intent and micro-decisions of your developers and agents locally, flagging logical conflicts before they ever hit your repository.

More about conflict resolution

Get Started

Kawa Code itself is a desktop application providing you with a rich dashboard of information as your AI makes progress on your code. Your Claude sessions, agents included, connect to Kawa Code via a locally installed MCP server.

1

Install the Kawa Code desktop application

Download and install Kawa Code, open it, and follow the setup instructions in the Setup Guide.

2

Optional: Install the Translation Extension

For international teams, install kawa.i18n to translate code, intents, and decisions into your team's preferred languages.

Plans

Every plan starts with a 14-day free trial — no credit card required. Cancel anytime.

Manage Billing

Security & Privacy

Kawa Code follows a zero-knowledge architecture.

Code blocks and diffs are encrypted client-side
The server stores encrypted data and cannot read your code
Organizations benefit from reasoning memory without exposing proprietary source code
Read the full security model →

Is it earning its value?

Kawa Code works quietly in the background. Here's how to tell it's paying off.

Your agent stops asking you to re-explain settled decisions — it already has them.
Conflicts surface before a merge, not after — while two people's work still differs in the editor.
Returning to old code is faster, because the intent and decisions come back with it.
The Orchestration panel fills with decisions you'd want a teammate to read — not boilerplate.
Ask your agent

"How did Kawa Code help in this session? Which intents, decisions, or past context did you use, and what would have been harder without them?"

One reflective question at the end of each big task is the easiest way to keep an honest read on the value you're getting.

Built with Kawa Code

Real products shipped with Kawa Code curating the reasoning behind every change.

Kawa Code desktop app — Team Intents and History panels tracking development intents and decisions

Kawa Code

A multi-repo ecosystem — its decisions and team coordination managed by Kawa Code itself.

TypeScriptRustAI/MLWebSocketIPC
Get Kawa Code →
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