context-manager

context-manager

Local-first, cross-session context store that reduces token usage by saving facts, decisions, and preferences, and recalling them in later sessions with token-efficient ranking and compression.

Category
Visit Server

README

context-manager

Local-first, cross-session context store built to reduce token usage. Save facts, decisions, preferences, todos, and session handoffs once — recall them in any later session or a different AI conversation, ranked and token-budgeted so they cost the model as little as possible.

  • Token-efficient. Recall is ranked (pinned > importance > recent) and capped by a token budget. The model restores context instead of re-reading files / re-deriving it.
  • Squeeze rendering (v0.3). Default recall is lossy-compressed — filler words dropped, safe abbreviations applied — while code, paths, identifiers and URLs are left intact. Typically ~40-50% fewer tokens than the full bodies, still LLM-readable.
  • Focus rerank (v0.3). --focus "<task>" blends full-text relevance into ranking so only on-topic context surfaces — load what the current task needs, not the whole store.
  • Delta recall (v0.3). Each recall prints a cursor=. Pass it back as --since next time to skip entries unchanged since then — never re-pay tokens for context already seen (pinned always kept).
  • Savings accounting (v0.3). Recall reports saved N% vs Mt full dump so the win is measurable.
  • Zero external services. Single SQLite file at ~/.context-manager/context.db.
  • Two interfaces. An MCP server (Claude Code / Cursor / any MCP client) and a ctx CLI.
  • Namespaced by project. Default namespace = current folder name; override with --project.
  • Full-text search via SQLite FTS5. No embeddings, no API keys.

Why it saves tokens

AI sessions forget everything when they end; a new session re-reads files and re-derives context — expensive. This stores the durable stuff (decisions + the why, where you left off). Next session calls context_recall (or a SessionStart hook injects it automatically) and is up to speed in a few hundred tokens instead of thousands of re-exploration tokens. Compact rendering + a token budget keep the injected context small; ranking ensures the few tokens spent are the highest-value ones.

Install

npm install
npm run build
npm link        # optional: global `ctx` command

CLI

ctx save "use Gemini Flash" -t decision -b "budget choice" -i 5 --pin --tags ai,budget
ctx append "build log" "phase 2 done"     # accumulate without overwriting
ctx recall                                 # ranked, squeezed (max savings), current folder
ctx recall --budget 400                    # cap output to ~400 tokens
ctx recall --focus "cold email sending"    # rerank to the current task
ctx recall --since 2026-06-27T07:23:58Z    # delta: only what changed since the cursor
ctx recall -f full                         # full detail (no compression)
ctx recall -f compact                      # one-line, no lossy squeeze
ctx search gemini                          # full-text search
ctx list                                   # titles-only index
ctx handoff "built MCP server" -n "wire into lead-gen project"
ctx pin 1 / ctx unpin 1                     # pin critical entries
ctx forget 3                                # delete entry #3
ctx export -p demo > demo.md                # backup / git / portable
ctx import demo.md -p demo                  # restore
ctx prune --days 30 -t session              # drop stale entries (keeps pinned)

Flags: -p/--project, -t/--type <fact|decision|preference|todo|session>, -b/--body, -i/--importance <1-5>, --pin, --tags a,b,c, -n/--limit, -f/--format <compact|full|squeeze>, --budget <tokens>, --focus <task>, --since <iso>. Override DB location with CONTEXT_DB env.

Claude Code (MCP)

Register once, available in every project (user scope):

claude mcp add --scope user context-manager -- node "F:/Tools/context-manager/dist/mcp.js"

Tools: context_recall, context_save, context_append, context_search, context_list, context_handoff, context_pin, context_forget, context_prune.

After rebuilding (npm run build), restart Claude Code so the new dist/ is loaded by the MCP server.

Auto-recall on session start (biggest token win)

Make recall automatic instead of relying on the model to ask. Add a SessionStart hook to your Claude Code settings.json that injects a small, budgeted context snapshot:

// in "hooks" -> "SessionStart" -> a "hooks" array
{
  "type": "command",
  "command": "\"C:\\Program Files\\nodejs\\node.exe\" \"F:\\Tools\\context-manager\\dist\\cli.js\" hook-recall --budget 400",
  "timeout": 10,
  "statusMessage": "Recalling saved context..."
}

hook-recall prints a compact, budget-capped snapshot (silent if the project has none). Its stdout is injected as session context — every session boots warm for a few hundred tokens.

End sessions with context_handoff (or ctx handoff) so the next start has "where we left off".

Data model

One table, entries(project, type, title, body, tags, pinned, importance, source, created_at, updated_at), unique on (project, type, title) so re-saving the same title updates in place. An FTS5 virtual table mirrors title/body/tags via triggers for search. Recall ranks by pinned DESC, importance DESC, updated_at DESC.

Develop

npm test          # vitest: store + format (28 tests)
npm run cli -- recall      # run CLI from source (tsx)
npm run mcp                # run MCP server from source (tsx)

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured