Codmir is the Context Framework for AI-to-AI workflows — the missing layer between task tracking and AI execution.

What is Codmir?

Codmir is the Context Framework for AI-to-AI workflows. It acts as the missing layer between task tracking and AI execution, designed to help both humans and AI agents coordinate work, share context, and build on each other’s contributions.

Core Idea

Traditional tools like Jira, GitHub, and Linear track human work. But AI agents now write code, run tests, analyze logs, and fix bugs. These tools weren’t built for agents — they lack a shared memory and a structured way for AIs to pass context between each other.

Codmir solves this gap by turning tickets into Context Capsules:

Tickets = Context Capsules

A ticket is more than a “to-do.” It’s a living container that holds the story, references, code changes, logs, artifacts, and agent traces — all in a structured, machine-readable way.

Reference Graph

Every ticket connects to commits, PRs, endpoints, datasets, and tests. The result is a graph that lets agents and humans navigate the why and how of work done.

Record of Work

Each capsule records not just what was done, but by whom (human or AI), why it was done, and what evidence supports it. This builds a durable memory of progress.

What Codmir Enables

For Teams

Faster reviews: See the full story, signals, code references, and test results in one place.
Shared context: Everyone (human + AI) is working with the same capsule, no re-explaining.
Audit trail: Every action is recorded — useful for compliance, quality, and trust.

For Single Users

Personal productivity: Logs and errors become structured tickets with suggested fixes.
Symbiosis with AI: Your personal agents can propose code, tests, or next actions directly into tickets.

For AI Agents

Shared API for context: Instead of scraping repos or logs, agents fetch capsules that already include references, artifacts, and story.
AI↔AI handoff: One agent can start work, another can continue without losing context.
Portable capsules: Each ticket has a share view, making context safely consumable across agents and platforms.

Why It Matters

Humans work best when context is clear. AI works best when context is computable. Codmir provides both.

Jira = task tracker for humans.
GitHub = source code for humans.
Codmir = context framework for humans and AI.

Use Cases

Ticket Creation

Auto-create tickets from logs, analytics, or errors (with dedupe and noise suppression).
Humans or AIs can add tickets manually with full story + references.

Ticket Reading

AIs read capsules to get structured context (story, code refs, signals, artifacts).
Humans read capsules to see the reasoning, diffs, and evidence.

Record of Work

Every capsule stores the history of what was done, why it was done, and what changed.
Acts as a ledger of AI + human collaboration.

Reference Point

Each capsule is a durable reference object that can be shared with other AIs, pipelines, or tools.
Enables reproducibility: what dataset, what code, what tests went into this decision?

The Vision

Codmir is not just a ticketing system. It’s the Context OS for AI-driven development:

A durable, portable memory layer that AIs and humans can both understand.
A framework that makes tasks computable, auditable, and sharable.
The missing piece that turns AI from “autocomplete” into true collaborators.

Core Idea

Codmir solves this gap by turning tickets into Context Capsules:

A ticket is more than a “to-do.” It’s a living container that holds the story, references, code changes, logs, artifacts, and agent traces — all in a structured, machine-readable way.

Every ticket connects to commits, PRs, endpoints, datasets, and tests. The result is a graph that lets agents and humans navigate the why and how of work done.

Each capsule records not just what was done, but by whom (human or AI), why it was done, and what evidence supports it. This builds a durable memory of progress.

What Codmir Enables

Faster reviews: See the full story, signals, code references, and test results in one place.

Shared context: Everyone (human + AI) is working with the same capsule, no re-explaining.

Audit trail: Every action is recorded — useful for compliance, quality, and trust.

Personal productivity: Logs and errors become structured tickets with suggested fixes.

Symbiosis with AI: Your personal agents can propose code, tests, or next actions directly into tickets.

Shared API for context: Instead of scraping repos or logs, agents fetch capsules that already include references, artifacts, and story.

AI↔AI handoff: One agent can start work, another can continue without losing context.

Portable capsules: Each ticket has a share view, making context safely consumable across agents and platforms.

Use Cases

Auto-create tickets from logs, analytics, or errors (with dedupe and noise suppression).

Humans or AIs can add tickets manually with full story + references.

AIs read capsules to get structured context (story, code refs, signals, artifacts).

Humans read capsules to see the reasoning, diffs, and evidence.

Every capsule stores the history of what was done, why it was done, and what changed.

Acts as a ledger of AI + human collaboration.

Each capsule is a durable reference object that can be shared with other AIs, pipelines, or tools.

Enables reproducibility: what dataset, what code, what tests went into this decision?

The Vision

Codmir is not just a ticketing system. It’s the Context OS for AI-driven development:

A durable, portable memory layer that AIs and humans can both understand.

A framework that makes tasks computable, auditable, and sharable.

The missing piece that turns AI from “autocomplete” into true collaborators.