Metadata-Version: 2.4
Name: memographix
Version: 0.1.3
Classifier: Development Status :: 3 - Alpha
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Classifier: Programming Language :: Rust
Classifier: Topic :: Software Development
Requires-Dist: mcp>=1.0
Requires-Dist: build>=1.2 ; extra == 'dev'
Requires-Dist: pytest>=8.0 ; extra == 'dev'
Requires-Dist: ruff>=0.9 ; extra == 'dev'
Requires-Dist: mypy>=1.10 ; extra == 'dev'
Requires-Dist: twine>=5.0 ; extra == 'dev'
Provides-Extra: dev
Provides-Extra: mcp
License-File: LICENSE
Summary: Local AI agent memory layer for repeated developer tasks, fresh context packets, and low-token repo recall.
Keywords: ai-agent,mcp,codebase-memory,knowledge-graph,developer-tools,rag,context-engineering
Home-Page: https://github.com/coderalnaim/memographix
Author: Memographix Contributors
Requires-Python: >=3.10, <3.15
Description-Content-Type: text/markdown; charset=UTF-8; variant=GFM
Project-URL: Homepage, https://github.com/coderalnaim/memographix
Project-URL: Issues, https://github.com/coderalnaim/memographix/issues
Project-URL: Repository, https://github.com/coderalnaim/memographix

# Memographix

[![CI](https://github.com/coderalnaim/memographix/actions/workflows/ci.yml/badge.svg)](https://github.com/coderalnaim/memographix/actions/workflows/ci.yml)
[![PyPI](https://img.shields.io/pypi/v/memographix.svg)](https://pypi.org/project/memographix/)
[![Python](https://img.shields.io/pypi/pyversions/memographix.svg)](https://pypi.org/project/memographix/)
[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](LICENSE)

Memographix gives AI coding agents local memory for your repo.

Install it once, run setup once, and keep using Codex, Claude, Cursor, Copilot,
Gemini, OpenCode, Aider, Windsurf, or any MCP client normally. Memographix
retrieves fresh context before work and captures useful task memory after work.

## Quick Start

Run this inside the repo where you want AI-agent memory:

```bash
pipx install memographix
mgx setup
mgx savings
```

`pipx` avoids system Python conflicts on macOS and Linux. The PyPI package
includes the CLI, local indexer, MCP server, and agent integration support.
`mgx setup` creates `.memographix/`, indexes the repo, writes an MCP server
config, and installs project agent rules.

If you are already inside a virtual environment or CI job, this also works:

```bash
python -m pip install memographix
```

The PyPI badge above always shows the latest published version; install commands
stay versionless.

## Daily Use

Use your AI agent normally.

Memographix is designed to work in the background:

- Before work, the agent asks Memographix for a small context packet.
- After useful work, the agent captures the answer with changed files, commands,
  tests, and outcome.
- If old evidence changed, Memographix marks the memory stale instead of reusing
  it silently.

Check setup health:

```bash
mgx doctor
```

If `mgx doctor` says an agent needs manual MCP configuration, use the generated
file at `.memographix/mcp.json` in that agent's MCP settings.

Control it per repo:

```bash
mgx status
mgx disable --reason "not needed here"
mgx enable
```

Disabled repos keep existing memory but automatic agent calls return no context
and save nothing. Re-enabling refreshes the index before Memographix is used
again.

See the estimated token savings:

```bash
mgx savings --since 30d
```

## Advanced CLI

Manual commands are still available for debugging and non-MCP workflows:

```bash
mgx ask "how does request routing work?" --budget 800
mgx remember --question "how does routing work?" --answer "..." --evidence app/routes.py
```

Most developers should not need the manual memory command after `mgx setup`.

## Proof

On the pinned Kubernetes benchmark, Memographix wins the repeated-task metrics it
is designed for: lower repeated tokens, faster repeated recall, stale-evidence
safety, and the best deterministic quality score. See [Benchmarks](BENCHMARKS.md)
for the public corpus, exact commands, honest losses, and unavailable-tool
notes.

## Why Developers Use It

- Stop re-explaining the same codebase across chats.
- Keep memory tied to real evidence files.
- Avoid stale answers after files change.
- Send smaller context packets to AI agents.
- Run locally without a required LLM API or cloud service.

## What It Does Not Do

- It does not upload your code.
- It does not save full chat transcripts by default.
- It does not treat stale memory as correct.
- It does not install benchmark tools or competitors in the runtime package.

## Docs

- [Benchmarks](BENCHMARKS.md): public Kubernetes results and claim policy.
- [Security](SECURITY.md): local privacy, skipped secrets, and sandbox safety.
- [Agent integrations](docs/AGENT_INTEGRATIONS.md): MCP and agent setup.
- [Architecture](docs/ARCHITECTURE.md): Python/Rust design and storage model.
- [Repeat task memory](docs/REPEAT_TASK_MEMORY.md): capsules, freshness, and token reduction.
- [Contributing](docs/CONTRIBUTING.md): local development and test expectations.
- [PyPI release](docs/PYPI_RELEASE.md): trusted publishing and release checks.

