context-kernel
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@context-kernelwhat's my current context?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
π§ context-kernel
Self-hostable context memory for LLMs. Keep your professional context, preferences, and evolving knowledge in sync across Claude Code, Desktop, and chatβwithout re-pasting or semantic drift.
Status: Live. Connects via Bearer token (Claude Code CLI) and OAuth (claude.ai connector UI), both verified end-to-end against a deployed Worker.
Started as a fix for one annoying thing: re-explaining myself to an AI before it could help with a research paper. Turned into infrastructure, because the real problem was never the paper β it was that nothing remembered anything.
A lightweight, opinionated context memory built on Cloudflare Workers and KV. You curate Markdown files about yourself, your work, and your preferences. A Worker serves them to Claude (Claude Code, Desktop, chat) over a secure remote MCP connector. Agents extend the memory via an append-only journalβbut only you decide what becomes permanent.
What it solves
Running agentic sessions across machines? Stop re-pasting:
Who you are and what you do
Your communication style and output preferences
How you want figures rendered
Evolving project status, goals, and constraints
Context-kernel puts this in one place you control, reachable everywhere Claude runs. Claude pulls it automatically; you never paste again.
Quick start
Self-host on Cloudflare
You bring your own content/ (this repo ships only templates in content.example/).
npm install
cp wrangler.toml.example wrangler.toml # fill in your Cloudflare KV namespace IDs + route
wrangler kv namespace create CONTEXT_KV
wrangler kv namespace create CONTEXT_KV --preview # paste both into wrangler.toml
wrangler kv namespace create OAUTH_KV
wrangler kv namespace create OAUTH_KV --preview
wrangler secret put READ_TOKEN
wrangler secret put WRITE_TOKEN
cp -r content.example content # edit content/*.md with your context
npm run build
wrangler kv bulk put artifacts/kv-bulk.json --binding CONTEXT_KV
wrangler deployNote your deployed Worker URL (e.g., https://my-context-kernel.myname.workers.dev/mcp).
Connect Claude Code
claude mcp add --transport http context-kernel \
https://my-context-kernel.myname.workers.dev/mcp \
--header "Authorization: Bearer <READ_TOKEN>"Replace <READ_TOKEN> with your token. On session start, .claude/skills/context-kernel/SKILL.md auto-loads your context.
Connect claude.ai chat interface (via OAuth)
In Claude (Desktop or browser), go to Settings β Connectors
Add custom connector with the deployed Worker's base URL,
/mcpendpoint (e.g.,https://my-context-kernel.myname.workers.dev/mcp)Claude registers itself automatically (Dynamic Client Registration, RFC 7591)βno manual client ID or secret
You'll land on a one-field login page asking for your
READ_TOKEN(the same token fromwrangler secret put READ_TOKEN)βenter it once to authorizeAll OAuth-issued tokens grant read-only access to your context (write access remains restricted to direct plain-bearer
WRITE_TOKENonly)
The OAuth layer is optional; existing Claude Code + Bearer token setups continue to work unchanged, and the two flows share nothing but the underlying /mcp endpoint.
Local dev
printf "READ_TOKEN=dev-read\nWRITE_TOKEN=dev-write\n" > .dev.vars
npm run devJournal promotion (human review gate)
scripts/promote.ts (npm run promote) lets you review journal entries before promoting them into curated content/. Optional subagents:
.claude/agents/context-promoter.mdβ runs the promotion review.claude/agents/mcp-tester.mdβ smoke-tests a deployed Worker
Existing personal LLM memory systems (mem0, OpenMemory MCP) use semantic search over extracted facts. They're comprehensiveβbut have a known failure mode:
Fact stored: "Prod runs Postgres 14"
Fact updates: "Prod now runs Postgres 16"
Both sit in the index. Similarity search hands back whichever scores higherβusually the older, reinforced one.
Result: outdated info looks authoritative.
context-kernel avoids this by design:
Feature | context-kernel | Vector-memory |
Source of truth | Hand-edited Markdown | Extracted facts in index |
Agent write access | Append-only journal | Often can edit directly |
Stale data retirement | Manualβyou remove it | Hopes retrieval rank decays |
Semantic search | No | Yes |
Self-maintenance | Low | High |
Trustworthiness | High (you control it) | Variable (retrieval can fail) |
Tradeoff: Less automatic, no semantic searchβbut the memory stays trustworthy because you maintain it.
ββββββββββββββββββββββββ
β content/*.md β β Hand-curated (sacred, never auto-written)
β (your source truth) β
ββββββββββββββββββββββββ
β
v
ββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
β npm run build β
β Compile β Validate β KV bulk-upload artifact β
ββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
β
v
ββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
β Cloudflare KV β
β β’ context:full:md (whole context) β
β β’ section:<name>:md (individual sections) β
β β’ journal:* (append-only agent notes) β
ββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
β
v
ββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
β Cloudflare Worker (Remote MCP Server) β
β π‘ Token-gated, constant-time auth β
β β
β Read Tools (READ_TOKEN): β
β β’ get_context() β full context or section β
β β’ list_sections() β available topics β
β β’ get_meta() β metadata (timestamps, versions) β
β β
β Write Tools (WRITE_TOKEN): β
β β’ append_journal(entry) β dated note β
ββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
β
v
ββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
β Claude Code / Desktop / Chat β
β Connects via MCP connector (auto-loads context) β
ββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
β
v
ββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
β npm run promote β
β You review journal, cherry-pick what becomes β
β permanent in content/ (manual gate = no rot) β
ββββββββββββββββββββββββββββββββββββββββββββββββββββββββKey design principles
Manual promotion gate: Agents append to a disposable journal. You review and hand-promote what becomes curated. This is what keeps the memory from rottingβstale content is retired because you remove it, not by accident.
Two-token security model: Read token pulls context; write token appends to journal only. Give write token to servers/agents, read token to yourself. Read token never reaches write operations.
Markdown as source of truth: No vector embeddings, no fact extraction, no semantic search. You edit plain text, version it, deploy it. What you see is what agents know.
Aspect | Detail |
Token auth | Every request authenticated before any data read |
Read token | Serves your context to Claude. Safe to embed in Claude Code config. |
Write token | Allows journal appends only. No read, no delete. Give to agents/servers. |
Leaked write token | Agent can leave poisoned notesβbut manual promotion means it can't silently corrupt your curated context. You see it. |
Leaked read token | Attacker sees your context. Rotate immediately. |
Token comparison | Constant-time (no timing attacks). |
Secrets storage | Cloudflare Workers secrets (encrypted, never in repo). |
See SECURITY.md for the detailed threat model and incident reporting.
Personal memory layers for LLMs already exist and are more mature than this project. Worth naming honestly:
OpenMemory MCP (mem0): self-hostable, user-owned memory across MCP clients, with a dashboard, per-client ACLs, and audit logs.
mem0-mcp-selfhosted: self-hosted memory for Claude Code with an optional knowledge graph.
Claude Code's own Auto Memory / Session Memory: already extracts and carries forward notes and summaries between sessions, no extra infra required.
If the goal were only "stop re-pasting who I am," any of these would work today.
The reason this project exists anyway: those tools are vector-store-backed, they extract facts automatically and retrieve by semantic similarity. That design has a known failure mode, described plainly by one such tool's own author: self-hosting fixes where memory lives, it does not fix what happens when a stored fact stops being true. If an agent writes "prod runs on Postgres 14" and it later becomes 16, both rows sit in the store, and similarity search hands back whichever scores higher, usually the older, more-reinforced one. Nothing retracts a fact.
That failure mode maps directly onto how a research context actually changes: current projects, course load, and priorities shift term to term, and a system that quietly keeps surfacing last term's status alongside this term's is worse than no memory at all, because it looks authoritative.
context-kernel avoids this by construction, not by tuning:
The curated store is hand-edited Markdown, not extracted facts in a vector index. Nothing becomes "memory" without a human writing or approving the sentence.
Agents can only append to a disposable journal. They cannot edit curated context, so they cannot silently overwrite or contradict it.
Promotion is a manual, human-run step. Stale or superseded content is retired because the owner removes it, not because a retrieval score happened to favor the newer entry.
The tradeoff is honest: this is less automatic than a vector-memory tool, and it does not do semantic search over your history. It optimizes for the memory being trustworthy over it being self-maintaining.
profile, goals, current-work, resume, writing-prefs, figure-prefs, answer-prefs,
skills, env-constants. Add only what an authorized Claude session should see; leave out
contact-heavy details.
This project was built end-to-end with Claude Code (Sonnet 5 + Haiku 4.5, caveman-compressed communication mode), across all sessions from scaffold to deployed OAuth fix.
Metric | Value |
Commits | 19 |
Test coverage | 100/100 passing |
Output tokens (all sessions) | ~353K |
Cache-read tokens (all sessions) | ~102.9M |
Cache-creation tokens (all sessions) | ~2.6M |
Estimated total cost | ~$26 (Sonnet 5 intro + Haiku 4.5 pricing) |
Committed: engine source, tests, artifact generator, promotion script, personal skill, subagent
definitions, content.example/ templates, config example.
Ignored: content/ (your real data), generated artifacts/, node_modules/, real wrangler.toml,
.dev.vars, .promoted-ids.json (local promotion-review state).
Related MCP server: me-db
License
MIT. See LICENSE.
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