Deterministic context infrastructure for AI agents.

Context Engine provides a reproducible, auditable, and offline-first system for delivering exactly the right context to language models — every time, on every machine.

It replaces heuristic retrieval and opaque pipelines with content-addressed documents, immutable caches, and fully deterministic selection.

Modern AI systems depend on context retrieval that is:

• non-deterministic
• difficult to audit
• hard to reproduce across environments
• coupled to external services
• unpredictable under token constraints

This creates operational risk for teams deploying AI in production.

Context Engine treats context as infrastructure, not a runtime guess.

For identical inputs, it guarantees byte-identical outputs across:

• operating systems
• hardware architectures
• supported compiler versions

No randomness. No hidden state. No network dependencies.

The platform is composed of three layers:

Deterministic selection and immutable cache model.

→ context-core

Features:

• content-addressed documents (SHA-256)
• token-budget-first selection
• deterministic ordering and scoring
• offline operation

Lifecycle management for deterministic context artifacts.

→ context-cli

Capabilities:

• build reproducible context caches
• inspect integrity and metadata
• locally verify agent behavior
• CI/CD-native workflows

Agents never build context at runtime — they consume caches produced here.

Standardized integration via the Model Context Protocol (MCP).

→ context-mcp-server

Provides:

• JSON-RPC 2.0 over stdio
• deterministic tool responses
• local cache access for agents
• zero external dependencies

→ context-compat

A standalone verification suite that enforces:

• deterministic behavior across versions
• frozen output schemas
• protocol stability
• backward compatibility of caches

This harness validates the platform’s core guarantees externally using only CLI and MCP interfaces.

Context Engine is built around explicit invariants:

✔ Deterministic selection
✔ Immutable, content-addressed artifacts
✔ Explicit token budgeting
✔ Fully explainable results
✔ Offline-first operation
✔ Stable machine-readable contracts

These guarantees are enforced by specifications and compatibility tests, not convention.

Context Engine is designed for environments where reproducibility matters:

• enterprise AI platforms
• regulated deployments
• on-prem and air-gapped systems
• CI/CD-driven AI infrastructure
• audit and compliance workflows
• multi-agent systems requiring stable behavior

If your system must be explainable, reproducible, and inspectable — this is the foundation.

The platform is currently at v0:

• Core contracts frozen
• Determinism enforced via compatibility harness
• MCP interface stable
• Designed for production evaluation

Future releases will expand scoring strategies, language SDKs, and hosted infrastructure while preserving the deterministic contract.

Core components are open source under the Apache License 2.0.

Context Engine defines a new category: deterministic context infrastructure.

AI systems should be reproducible by design.
Context should be an artifact, not a guess.
Infrastructure should be inspectable, not probabilistic.