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Context Engine

Context Engine is a Python library for preparing context for LLM systems through document retrieval and re-ranking, compression, memory management and token-budget enforcement.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Context Engine — OpenViST project profile in RAG & Knowledge Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when an LLM application needs to control which retrieved documents and conversation turns enter a prompt, particularly when history accumulates or the token budget is tight. For latency requirements below 50 ms, the README recommends keyword or TF-IDF instead of hybrid retrieval.

Who it is for

  • Developers integrating context management into Python-based LLM applications.
  • Teams building multi-turn systems that need to balance retrieved documents and conversation history within a prompt budget.

Documented uses

  • Retrieve relevant documents using keyword, TF-IDF or hybrid search.
  • Re-rank retrieved material using tag-weighted scoring.
  • Compress selected content with truncation, sentence-based or extractive strategies.
  • Manage conversation memory with decay, importance scoring and deduplication.
  • Allocate a token budget across system prompt, history and documents, then produce a context packet.
Before you choose

Requirements & limits

The repository is public and identifies the project as MIT-licensed. The README lists Python 3.10 or later, NumPy as required and sentence-transformers as optional for hybrid retrieval.

  • Core functionality uses the Python standard library and NumPy; NumPy is required.
  • Hybrid retrieval uses sentence-transformers when installed; otherwise it falls back to random embeddings with a warning.
  • The README reports hybrid retrieval at about 85 ms and recommends keyword or TF-IDF modes for sub-50 ms requirements.
  • The project notes that token estimates use a simplified approach.
  • The library assembles context for an LLM; it does not itself provide the model.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install context-engine and its required dependency

Clone the repository and install NumPy, which the core library requires.

Before you start

  • Python 3.10 or later
  • Git and pip

Steps

  1. Clone the context-engine repository: the command below.

    Documented command
    git clone https://github.com/Emmimal/context-engine.git

  2. Change into the repository directory: cd context-engine.

  3. Install the required dependency: the command below.

    Documented command
    pip install numpy

  4. Optionally install sentence-transformers to enable hybrid retrieval.

Expected outcome

The repository is cloned and the required NumPy dependency is installed; installing sentence-transformers additionally enables hybrid retrieval.

Guide limitations

  • Without sentence-transformers, hybrid mode falls back to random embeddings with a warning; the README describes this as useful for development and testing.
Documentation 1 ↗
Source-based practical guide

Build a context packet and add conversation turns to memory

Use ContextEngine to build query context from documents, then add turns for a subsequent build.

Before you start

  • Python 3.10 or later
  • The context-engine repository cloned and NumPy installed

Steps

  1. Import ContextEngine and Document from context_engineering, then create a document list using Document(id=..., content=..., tags=...).

  2. Initialize ContextEngine with the documents, a token budget, a retrieval mode such as hybrid, and a compression strategy such as extractive.

  3. Call engine.build(query) and use packet.to_prompt_string() to obtain the context prompt string.

  4. Add user and assistant turns with engine.remember(role, text), then call engine.build() with a subsequent query; packet.diagnostics() is shown for inspecting that packet.

Expected outcome

The build call returns a context packet assembled from the configured documents and budget; remembered turns can participate in a later build.

Guide limitations

  • The README notes that hybrid mode uses random embeddings with a warning if sentence-transformers is not installed.
  • Token estimation uses 1 to …
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
14 October 2026
Original website title
Emmimal/context-engine · Official homepage title · retained in its source language
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