zeitcow/implicit-core

Implicit is the experience layer for AI agents.
Virtualize large agent environments. Materialize only the state each experience actually needs.
Implicit Core is AI agent experience/environment infrastructure from zeitcow/implicit-core, distributed as implicit-ai under Apache-2.0. Current stable: 1.0.1.
console
pip install implicit-ai
Website and docs · PyPI · 60-second quickstart · Measured evidence · Connect a coding agent · Install the Codex integration skill
## Why virtualize an experience?
In agent training and evaluation, the possible environment can be much larger than any individual episode. A warehouse may contain millions of orders; processing one order needs only its inventory and policy records. Instead of eagerly constructing the complete environment for every episode, an Implicit experience addresses a coordinate in a versioned environment. Your adapter supplies the initial pages and loads further required state on demand, including declared dependencies.
An experience is one addressable interaction: instruction, required state, execution and evaluation. The address identifies the environment version and coordinate. Your adapter chooses pages; your existing agent executes; your native evaluator verifies the result. Core records execution state, provenance and explicit recovery.
Immutable source versions, fixed seeds and deterministic adapters, state sources and agents support reproducible episodes. External services and effects need application-owned control or reconciliation; Implicit does not make arbitrary programs deterministic. Architecture explains the conditions.
This works at the logical experience/environment-state layer. VM or container snapshots can supply an execution sandbox; Implicit determines which addressed state is needed inside an experience. FAQ distinguishes this from filesystem paging, dataset streaming, prompt compression and lazy MCP tool discovery.
Use Implicit for large separable state, repeatable identities, expensive environment construction or durable execution evidence. Measure overhead for small, already lazy or mostly accessed environments.
## Try it in 60 seconds
Python 3.11+; zero third-party runtime dependencies. In a fresh virtual environment:
console
pip install implicit-ai
implicit --version
implicit demo
implicit benchmark
The public toy compares eager and selective serialized state, verifies the shipping result and prints a provenance hash. Its output is its own workload measurement.
The distribution is implicit-ai; the import is implicit. Use an isolated environment because the unrelated implicit recommendation library shares that import namespace. Installation includes Windows setup and verified release artifacts.
## Measured evidence and its limits
In the preserved rc1 native systems population, 155/155 comparable cases preserved equivalent state, tool behavior and reward.
| Measurement | Result |
| Aggregate retained serialized/materialized-state reduction | 93.88% |
| Mean case reduction | 94.40% |
| Median case reduction | 99.55% |