Library Inventory

Agent Learning Kit is the public release home for the Future AGI agent learning engines. The code is consolidated here under one Python package and one CLI. ai-evaluation remains the active evaluation engine; its Python runtime lives under fi.evals, and its TypeScript SDK source lives under the consolidated TypeScript package.

Public Surface

Surface	Path	Purpose
Python SDK	src/fi/alk	Public facade for configuration, evaluation, simulation, optimization, red teaming, suites, and release gates.
CLI	src/fi/alk/cli.py	agent-learn command surface for doctor, eval, simulate/run, redteam, optimize, report, release-check, and release-proof.
TypeScript SDK	typescript/agent-learning-kit	Public TypeScript package published as @future-agi/agent-learning-kit, including the migrated ai-evaluation TypeScript source.
Examples	examples	Runnable cookbooks and manifests that use the consolidated public package.

Engine Code

Library or engine	Active source path	Runtime namespace	What lives there
ai-evaluation Python runtime	src/fi/evals	fi.evals	Active evaluation framework, local evaluators, metrics, guardrails, RAG and structured-output checks, OpenTelemetry evaluation processors, streaming evaluators, and agent report scoring.
ai-evaluation TypeScript SDK	typescript/agent-learning-kit/src	@future-agi/agent-learning-kit	TypeScript evaluator, local metrics, templates, execution, manager, protect, scanner, streaming, and RAG/heuristic evaluation source.
simulate-sdk	src/fi/simulate	fi.simulate	Simulation manifests, local/cloud simulation engines, framework adapter probes, LiveKit/local text engines, environment replay, report rendering, recording, suites, and CLI implementation.
agent-opt	src/fi/opt	fi.opt	Optimizer base classes, agent optimizers, mutation/evidence models, simulation integrations, deployment and observability helpers, and optimizer utilities.

The package build includes both the public facade and the active engine namespace:

[tool.hatch.build.targets.wheel]
packages = ["src/fi/alk", "src/fi"]

That means a built wheel contains the public fi.alk.* API and the engine implementations under fi.evals, fi.simulate, and fi.opt.

Import Direction

New public code should use the consolidated package:

from fi.alk import configure
from fi.alk import evals, optimize, simulate

configure(api_key="...")

Internal compatibility code may still import the engine namespaces directly:

from fi import evals
from fi import opt
from fi import simulate

Do not add new release-facing examples that require cloning ai-evaluation, simulate-sdk, or agent-opt separately. ai-evaluation changes required for v1 should be present in this repository before the Agent Learning Kit release is called complete. If a fix starts in a separate engine repo, copy the verified implementation into this repository before treating the public SDK work as done. The file the ai-evaluation source inventory (maintained in the internal-docs repo) records the ai-evaluation source snapshots that were consolidated here, and agent-learn release-check fails if those mapped file paths are missing.

Reviewer Checklist

When checking whether the migrated engines are present, inspect these paths in the release branch:

1. src/fi/evals for evaluation and scoring code.
2. typescript/agent-learning-kit/src for the TypeScript evaluation SDK source.
3. src/fi/simulate for simulation, framework adapters, and reports.
4. src/fi/opt for optimizer primitives and agent optimizers.
5. src/fi/alk for the public SDK facade that ties them together.
6. examples for runnable trinity cookbooks.
7. pyproject.toml for package inclusion and the agent-learn CLI entry point.

Migration Rule

agent-learning-kit is the source of truth for v1. ai-evaluation is not a legacy dependency for this release; it is the active evaluation engine embedded in this package. The older simulate-sdk and agent-opt repositories remain history and compatibility references during migration, not separate release requirements for the public v1 package.