Aictx documentation

Aictx gives AI coding agents a project memory they can come back to.

Use it when you are tired of re-explaining the same product intent, architecture decisions, repo conventions, setup steps, and known traps every time a new chat or agent session starts. Aictx stores that durable context as local, reviewable files under .aictx/, then compiles a focused context pack for the task in front of the agent.

The everyday loop is small:

load relevant memory -> do the work -> save what future agents should remember

Core memory commands do not require a cloud account, embeddings, hosted sync, an external model API, or network access.

What Aictx is for

Aictx is local-first project memory for AI coding agents. It is not a chat UI, an Obsidian clone, or a hosted memory service.

It helps a coding agent answer two questions:

• Before work: what does this agent need to know for this task?
• After work: what should future agents not have to rediscover?

Memory can hold source records, decisions, constraints, facts, gotchas, workflows, open questions, product concepts, and compact syntheses for areas such as product intent, feature maps, roadmap, architecture, conventions, and agent guidance.

Tip

A good first memory is not a diary of what changed. It is something future agents can use: “release smoke tests run with pnpm test:local”, “billing webhooks retry in the worker”, or “the viewer is read-only except Obsidian export”.

How it works

1. Initialize Aictx inside an existing project with aictx init.
2. Load task-focused context with aictx load "<task summary>".
3. Save durable knowledge with aictx save --stdin or save_memory_patch when MCP is already configured.
4. Inspect the result later with aictx view, aictx diff, Git tools, or MCP diff_memory when available.

Saved memory is active immediately after Aictx validates and writes it. Aictx never commits for you.

Start here

• Getting started gets a project initialized and walks through the first load/save/diff loop.
• Capabilities maps the v1 features to the jobs users and agents actually need to do.
• Mental model explains canonical memory, generated state, object types, and retrieval.
• Specializing Aictx shows how to tailor memory to your repo’s product intent, workflows, and agent guidance.
• Agent integration gives agents the concrete workflow and guardrails.

CLI and MCP

The CLI is the default interface for routine memory work. MCP is available when the agent client has launched and connected to aictx-mcp.

MCP exposes exactly load_memory, search_memory, inspect_memory, save_memory_patch, and diff_memory. Setup, maintenance, recovery, export, registry, viewer, docs, suggest, audit, stale, and graph workflows are CLI-only in v1. These CLI-only commands are part of the v1 integration model rather than MCP parity gaps.

Local MCP is the near-term integration path for local agent harnesses. Remote MCP, hosted sync, cloud auth, cloud hosting, and ChatGPT App SDK UI are future work. Future ChatGPT-compatible search/fetch names are adapter aliases over search and inspect behavior, not local MCP tool names.

For agents

This site is also published with agent-readable documentation files:

• /llms.txt
• /llms-full.txt
• /llms-small.txt

These files provide compact public documentation for coding agents without crawling the full website navigation.