Skip to main content

ctxkit — Context Portability and Knowledge Ingestion Tool

Version: 1.3.3 | Status: Implementation

A local, LLM-agnostic context delivery and knowledge sync tool for users who live across multiple subscription LLMs.


What It Does

ctxkit sits between your personal markdown knowledge base and whichever subscription LLM you are using at any given moment (Claude, ChatGPT, Gemini, or any other). It does two things:

Flow A — Context Retrieval: Given a topic, it retrieves relevant chunks from your vault, synthesises them into a coherent, cited briefing (using a local LLM), and writes the result to <vault>/ctxkit-output/. You open the file, copy it, and paste it as the opening context of your LLM conversation. Zero API calls to your subscription LLM. Zero cost beyond your existing subscription.

Flow B — Knowledge Ingestion: Given any document — a session summary from an LLM conversation, a research note, an article, a strategy doc, or any new piece of knowledge — it classifies the content against your existing vault, decides whether to update an existing note or create a new one, routes it to the right folder, detects conflicts, generates a proposal, and — only after your explicit approval — writes the changes to your vault and reindexes automatically.


Documentation


Installation

macOS — Homebrew

brew tap piyush-tyagi-13/ctxkit
brew install ctxkit

Any platform — shell script (installs uv + ctxkit)

curl -fsSL https://raw.githubusercontent.com/piyush-tyagi-13/context-portability-tool/master/install/install.sh | bash

Manual (if you already have uv or pipx)

uv tool install ctxkit-ai       # preferred
# or
pipx install ctxkit-ai

Ollama models (for local inference)

ollama pull nomic-embed-text   # embeddings
ollama pull qwen3.5:4b         # primary LLM — classification + proposals
ollama pull phi4-mini          # synthesis — fast, non-thinking

After install

ctxkit init       # interactive setup wizard
ctxkit deps install   # install any backend packages not yet present
ctxkit index      # index your vault

Commands

ctxkit init                           # Interactive setup wizard — create config
ctxkit index                          # Scan vault, show diff, confirm, index delta
ctxkit search <topic>                 # Synthesise briefing → write to <vault>/ctxkit-output/ (Flow A)
ctxkit search <topic> --raw           # Raw excerpts only — skip synthesis
ctxkit search <topic> --verbose       # Show chunk scores alongside results
ctxkit ingest                         # Paste any document — classify, route, propose (Flow B)
ctxkit ingest --file doc.md           # Ingest from a file (session summary, article, notes, etc.)
ctxkit map                            # Generate vault folder map for doc routing
ctxkit status                         # Show index health and drift warnings
ctxkit eval [topic]                   # Run quality evaluation checklist
ctxkit config                         # Open config file in editor
ctxkit config --validate              # Validate config and report errors

Multiple config profiles

ctxkit search "istio auth" --config ~/.ctxkit/config-technical.yaml
ctxkit search "career goals" --config ~/.ctxkit/config-personal.yaml

Quick Start

# 1. Configure (interactive wizard)
ctxkit init
# → asks for vault path, owner name, LLM backend, models
# → detects Ollama + pulled models, gives hardware-appropriate suggestions
# → writes ~/.ctxkit/config.yaml

# 2. Index your vault
ctxkit index

# 3. Retrieve context for an LLM conversation
ctxkit search "Bruno ingress path adaptor"
# → writes <vault>/ctxkit-output/2026-04-25-bruno-ingress-path-adaptor.md
# → open file, copy contents → paste into Claude/ChatGPT/Gemini

# 4. Ingest any document into your vault
ctxkit ingest --file my-session-summary.md   # LLM session summary
ctxkit ingest --file oss-strategy.md         # standalone research doc
ctxkit ingest                                # paste content directly
# → ctxkit classifies, routes to right folder, proposes changes → approve

Architecture

YOUR MARKDOWN VAULT
        │
        ▼
   ctxkit core
   ┌──────────┐  ┌──────────┐  ┌────────────┐
   │ Indexer  │  │Retriever │  │  Ingester  │
   └──────────┘  └──────────┘  └────────────┘
   ┌──────────┐  ┌──────────┐  ┌────────────┐
   │  Writer  │  │LLM Layer │  │VectorStore │
   └──────────┘  └──────────┘  └────────────┘
        │
   (copy-paste by user)
        │
        ▼
ANY SUBSCRIPTION LLM (Claude · ChatGPT · Gemini · Others)

ctxkit never talks to your subscription LLM directly. It prepares context (Flow A) and processes output from it (Flow B). The user is the bridge.

LLM calls in Flow A (retrieval): one local call to synthesise_model (default: phi4-mini via Ollama) to reformat raw excerpts into a coherent briefing. No calls to your subscription LLM. Skip with --raw to make Flow A fully LLM-free.

LLM calls in Flow B (ingestion): only when classification is ambiguous (score between low and high thresholds). Clear-match updates and clear new-file cases require no LLM call.


Configuration Reference

See config.yaml.example for the full annotated config. Key sections:

Section Key fields Purpose
vault path, owner_name Vault root path, owner identity for multi-person vaults
indexer chunk_size, heading_levels Chunking strategy and quality filters
embeddings backend, local_model Local (Ollama) or API-backed embeddings
retriever top_k, similarity_threshold Candidate retrieval, assembly, signposting
ingester similarity_threshold_high/low Classification thresholds, conflict detection
writer append_position, backup Append position, frontmatter injection, backups
llm model, synthesise_model Primary LLM (classify/propose) + synthesis model (search)
cli theme, verbose Terminal UI behaviour

Hardware Tiers

Hardware LLM Model Embedding Model
Apple M2 Air 16GB qwen3.5:4b nomic-embed-text
i5 + RTX 4070 qwen3:8b bge-m3
Low-end / no GPU gpt-4o-mini / claude-haiku-4-5 text-embedding-3-small

Project Structure

ctxkit/
├── cli/commands.py              # Typer commands, Rich rendering
├── core/
│   ├── indexer/                 # VaultScanner, ManifestManager, TextSplitter, ...
│   ├── retriever/               # KeywordPreFilter, VectorSearcher, ChunkStitcher, ...
│   ├── ingester/                # ClassificationEngine, ConflictDetector, ...
│   └── writer/                  # BackupManager, FrontmatterInjector, FileWriter, ...
├── llm/llm_layer.py             # classify() and propose() — single LLM abstraction
├── store/vector_store.py        # ChromaDB wrapper — 4 operations
├── config/                      # Pydantic models + YAML loader
└── utils/                       # Logging, file utilities

What ctxkit Is Not

  • Not a chatbot or RAG question-answering agent
  • Not an API wrapper around subscription LLMs
  • Not a note-taking application
  • Not an always-on background service
  • Never writes anything without your explicit approval

ctxkit - Context Portability and Knowledge Ingestion Tool v1.3.3

Metadata

Release files for ctxkit-ai 1.3.3

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for ctxkit-ai 1.3.3
File Size Uploaded
ctxkit_ai-1.3.3.tar.gz 72.7 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for ctxkit-ai 1.3.3
File Interpreter ABI Platform
ctxkit_ai-1.3.3-py3-none-any.whl Python 3 none any Details

Total release size: 156.5 kB

Release files / ctxkit_ai-1.3.3.tar.gz

Download URL ctxkit_ai-1.3.3.tar.gz
Size 72.7 kB
Tags Source
SHA-256 checksum
How to use checksums
814622ecb3d2707208b112590016fe8eb04f04030ba64d21d42dfc2ad11dddd0
BLAKE2b-256 checksum
How to use checksums
62113acef4c9246eefd68c5ce2a1146ed40cec5d1c827a4384a31c9ad2cda349
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.2.0 CPython/3.13.5

Release files / ctxkit_ai-1.3.3-py3-none-any.whl

Download URL ctxkit_ai-1.3.3-py3-none-any.whl
Size 83.8 kB
Tags Python 3
SHA-256 checksum
How to use checksums
15e3e5f82873f77caa6ed2aad5193cb4915100b078c6375b5e25d6a123144e3c
BLAKE2b-256 checksum
How to use checksums
e6bb0add1ecab6f06f5b10f96b18e7e01253cc7ba336d30ddc16a8de98959ab2
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.2.0 CPython/3.13.5

Release history Release notifications | RSS feed

This release

1.3.3 This release

2 release files

1.3.2

2 release files

1.3.1

2 release files

1.3.0

2 release files

1.2.4

2 release files

1.2.3

2 release files

1.2.2

2 release files

1.2.1

2 release files

1.2.0

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page