CONTEXT AUTOPILOT · v0.6 · FREE & OPEN SOURCE

You already taught your agent everything.
It just wasn't listening.

Context Autopilot reads your past Claude sessions — everything you repeated, corrected, or rejected — plus the notes Claude already keeps about your projects, and turns them into rules every future chat starts with. You approve every rule before anything is written.

⬇ Download for Claude Desktop one file — double-click it and Claude installs the rest
$ claude mcp add --scope user context-autopilot -- npx -y -p context-autopilot ctxlayer-mcp using Claude Code in the terminal? paste this once — installed for every project
Free & open source · Everything stays on your computer · No API key — uses the Claude you already pay for · Claude Code, Claude Desktop & Cursor

What it does

You've spent months teaching Claude how you like things done — one chat at a time. Autopilot makes those lessons permanent.

It reads what you already taught Claude

Your chat history, your CLAUDE.md files, and the memory Claude keeps for each project. The lessons are already sitting in there — Autopilot finds them.

Writes them where every future chat can see

Approved rules go into the project's rules file and your personal global one — so every new session, in every project, starts already knowing how you work. You stop re-explaining yourself.

Costs almost nothing

Watching and scanning use zero AI — it's plain file-reading on your own computer. AI runs once, only at the moment rules are written, through the Claude plan you already have. No API key, no separate bill.

Speaks up at the right time

A silent check runs when a session starts. Only when enough new patterns have built up does Claude offer to save them — at a natural pause, not mid-task. And nothing is ever written without your yes.

You never learn commands — you just talk

Once installed, Claude picks the right tool from your plain words. Things people actually say:

What have you learned about how I like to work?
Update this project's rules from my recent sessions.
Pull anything useful from my project memories up to my global rules.
Is anything in my CLAUDE.md outdated?

Claude answers with suggested rules and the exact words you once said that justify each one. You reply "accept 1 and 3" — done.

How it works

Your chat history is a literal record of what Claude got wrong and what you had to say to fix it. Autopilot turns that record into rules Claude keeps.

01 · LOOK

It reads your history

On your own computer, it reads your past sessions and saved project notes, looking for three things: instructions you gave more than once, moments you corrected Claude, and actions you refused. Nothing is uploaded anywhere.

02 · SUGGEST

It proposes a short list of rules

Only the repeated patterns — not your whole history — get turned into plainly-worded rules. Each one comes with the exact words you once said, quoted as proof. Generic advice gets filtered out.

03 · YOU DECIDE

You say yes or no in chat

Accepted rules are written into a clearly marked section of your rules files. Anything you wrote by hand is never touched, rejected rules are never suggested again, and nothing is written without your yes.

Real output, real project

Pointed at a real project's chat history, Autopilot surfaced rules like these — each one something the person had already taught Claude the hard way:

$ ctxlayer distill
Distilling 26 signal(s) with your local `claude` CLI…

[1/8] Perform click-and-type tests before reporting any UI work complete (confidence: high)
  + Before declaring any screen done, click every button and verify it works —
    do not ship cosmetic (non-functional) buttons.
  evidence: "There are still so many buttons that dont work, like the publish…"

[2/8] Staff login cannot access or toggle to admin view (confidence: high)
  + When authenticated as staff, the admin role toggle must be hidden —
    this is access control, not a UI preference.
  evidence: "…from the staff login, I do not want to see the admin view."

[3/8] The platform ships as a self-hosted Docker image (confidence: high)
  + Clients run the image on their own server; never store or pull their
    data into any external cloud platform.
  evidence: "the data for their schedules and rosters should stay with them…"

Next: `ctxlayer apply` to review and write the ones you accept.

Why evidence-based, not generated

Auto-generated context files make agents worse

Research on machine-generated CLAUDE.md files found they reduce task success and run up your token bill — automatic scans produce generic filler Claude then has to wade through. A focused 50-line file beats a sprawling 1,000-line one.

Your corrections are the truth

Every time you told Claude "no — like this," you wrote down exactly what you want. Autopilot only proposes rules your own words support, quotes them as proof, and prefers a few strong rules over a pile of weak ones.

Works with every agent — and stays fresh

Rules land in both CLAUDE.md and AGENTS.md, so Claude Code, Cursor, Copilot, Codex, and 30+ other agents all benefit. It also catches the reverse problem: rules your project has outgrown — references to files that no longer exist — before they mislead an agent.

It learns you, not just your projects

It looks across all your projects for rules about how you personally work — "explain in plain English", "don't build while I'm brainstorming" — and keeps them in your one global rules file, so every project benefits. It also reads the memory files Claude keeps per project and lifts the universal lessons up to that global file. No other tool does evidence-based personal context.

The Context Layer Index

An independent, curated map of the context layer — who does what, without the vendor slant. Updated continuously.

Agent memory layers

Mem0Managed long-term memory; largest ecosystem
ZepTemporal knowledge graph; strong on time-aware recall
LettaOS-style paged memory (MemGPT lineage)
SupermemoryPersonal memory vault with deep MCP integration
CogneeGraph + vector memory pipelines
GraphitiOpen-source temporal graph engine
LangMemMemory inside the LangGraph loop

Context for coding agents

Context AutopilotEvidence-based context distilled from your sessions — this site
AGENTS.mdThe open cross-agent context file standard
Context7Up-to-date library docs as agent context
GitHub MCPRepo, PR & issue context over MCP
Claude CodeCLAUDE.md, rules, skills, hooks — deepest native support

Enterprise context platforms

GleanFully-managed enterprise context & search
AtlanGoverned data-catalog context layer
InterloomCaptures expert operational knowledge for agents
GraphlitManaged ingestion → agent-ready knowledge
LlamaIndexRetrieval framework powering many context stacks

Observation & capture

Screenpipe24/7 local screen+audio capture (source-available)
PiecesDeveloper workstream memory across IDE/browser
LimitlessWearable + meeting capture (acquired Rewind)
Codex Record & ReplayDemonstrate a task once → reusable skill
MindedRecord a browser task → team workflow
ScribeAuto-generated SOPs from screen recordings

Context observability

LangfuseOpen-source LLM traces, evals, prompt mgmt
BraintrustAgent observability + evals, IDE-native via MCP
LangSmithTracing & debugging for LangChain stacks
Arize PhoenixSelf-hostable, OpenTelemetry-native traces
mcpsnoop"Wireshark for MCP" — see what agents actually receive

Memory portability

MemoryXExport/import memories across ChatGPT, Claude, Gemini
AI Context FlowUniversal memory layer above every assistant
MCPThe protocol the whole layer speaks

The Context Layer, weekly

One email a week on who's winning the context layer — new tools, benchmarks, and what actually works. No filler.