memographix 0.1.6

Local AI agent memory layer for repeated developer tasks, fresh context packets, and low-token repo recall.

Memographix

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:

pipx install memographix
mgx setup
mgx doctor --live
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 MCP config for supported agents, registers the repo for global MCP routing, and installs project agent rules.

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

python -m pip install memographix

PyPI resolves the latest release automatically, so install commands stay versionless.

Daily Use

Use your AI agent normally. Memographix works 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:

mgx doctor --live

mgx doctor --live verifies that the MCP server starts, expected tools are available, and the router can resolve this repo. Restart agents after setup if they were already open so they reload MCP tools.

Control it per repo:

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:

mgx savings --since 30d

If savings are all zero, Memographix now tells you whether no agent tool calls have been recorded yet. Run mgx doctor --live, restart the agent, and either open the chat from the repo or mention a registered repo name.

List repos registered for global routing:

mgx repos

Repair stale duplicate MCP entries:

mgx repair --mcp

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 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.

Boundaries

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

Docs

• Benchmarks: public Kubernetes results and claim policy.
• Security: local privacy, skipped secrets, and sandbox safety.
• Agent integrations: MCP and agent setup.
• Architecture: Python/Rust design and storage model.
• Repeat task memory: capsules and freshness.
• Contributing: local development and test expectations.
• PyPI release: trusted publishing and release checks.