AgentInputLab

A Python toolkit and typed JavaScript client for composing and evaluating input interventions for LLM agents.

This branch contains reusable library code, worker adapters, synthetic tests and documentation. It contains no paper results, experiment logs, datasets, trained weights or private execution configuration. It starts a new Git history: do not make the entire historical research repository public to publish this branch.

Install

The commands below work without GitHub access. The source distribution also includes docs/getting-started.md with a real HTTP backend example and troubleshooting.

Python 3.10+; optional model dependencies have their own Python/platform constraints.

python -m pip install agentinputlab==0.1.0a2
python -m agentinputlab --version
python -m agentinputlab doctor --offline --json
python -m agentinputlab smoke --json

To obtain the public source and included documentation from PyPI:

python -m pip download --no-deps --no-binary=agentinputlab agentinputlab==0.1.0a2
tar -xzf agentinputlab-0.1.0a2.tar.gz
cd agentinputlab-0.1.0a2
python -m pip install .

Maintainers with repository access can instead use the Git checkout (the research repository remains private):

git clone --single-branch --branch release/toolkit-public https://github.com/Data-Designer/AgentInputLab.git
cd AgentInputLab
python -m pip install .
agentinputlab doctor --offline
agentinputlab smoke --json

The Python distribution is an initial alpha. The JavaScript SDK remains source-only while npm publication is deferred. To build local Python and SDK artifacts:

python -m pip install build
python -m build
python -m pip install dist/agentinputlab-0.1.0a2-py3-none-any.whl
cd packages/sdk
npm ci
npm test
npm pack
# From a JavaScript application:
npm install /absolute/path/to/agentinputlab-sdk-0.1.0-alpha.1.tgz

Python: apply a real intervention

from agentinputlab import FrozenAgentState, InterventionContext
from agentinputlab.api import create_intervention, run_intervention

method = create_intervention("plan.plan_and_solve.ps_prompt")
result = run_intervention(method, FrozenAgentState(query="Find a suitable flight"),
                          InterventionContext(seed=17))
print(result["state"]["instruction"])

This executes the Plan-and-Solve prompt intervention. It is not an LLM response or a scored benchmark result. For model-driven methods, supply an explicit backend, retriever or external worker in InterventionContext; constructors retain their documented method-specific parameters. See real execution.

from agentinputlab.api import catalog
from agentinputlab.registry import AlgorithmRegistry

print(catalog()["benchmarks"])
registry = AlgorithmRegistry.default()
for method, profile in registry.list_releases():
    print(method.method_id, profile.id, profile.status.value, profile.blocking_reasons)

The catalog exposes 27 method identities and 31 benchmark entries, including ToolTalk and datasets outside the paper's reporting subset. Choose a release profile and provision its declared code, checkpoints, environment and native scorer. Profile registration and real execution are separate compatibility dimensions; see validation and profile prerequisites. Composite agents retain their native interfaces.

For Rewrite-Retrieve-Read (RRR), start with rewrite.rrr.common_support_prompt: the controlled prompt adapter uses your configured backbone and retriever for the rewrite → retrieve → reader chain. Author warm-up/PPO checkpoints are separate, optional external profiles.

For offline optimization, see Q-RAG value-policy training and R²AG reader-aligned training and generation. These recipes save fitted artifact identities and separate training costs from subsequent inference.

JavaScript / TypeScript

The SDK connects to the same Python execution core; it does not reimplement algorithms. In Python, configure a server with explicit profile factories and dependency contexts. The CLI exposes only NO_OP and Plan-and-Solve by default:

# Set a newly generated local token of at least 24 characters in your shell.
export AGENTINPUTLAB_API_TOKEN="$(python -c 'import secrets; print(secrets.token_urlsafe(32))')"
agentinputlab-serve --port 8765

import { AgentInputLab } from '@agentinputlab/sdk';
const client = new AgentInputLab({ token: process.env.AGENTINPUTLAB_API_TOKEN! });
const inventory = await client.catalog();
const result = await client.intervene({
  release_id: 'plan.plan_and_solve.ps_prompt',
  state: { query: 'Find a suitable flight', budget: { max_model_calls: 8, max_output_tokens: 4096 } },
  seed: 17,
});
console.log(result.state);

The bundled HTTP server is authenticated and loopback-only. It is a development bridge, not a public multi-tenant hosting service. Browser CORS is intentionally disabled. Do not expose its token in browser bundles. See API contract.

Tests versus real measurements

python -m pip install pytest
pytest
python tools/check_distribution.py dist/*.whl dist/*.tar.gz
python tools/compatibility_inventory.py --output /tmp/agentinputlab-compatibility.json

Offline tests exercise algorithm behavior with explicit synthetic fixtures, transport, budgets, typed failures and worker contracts. The compatibility inventory checks every registered method, benchmark and requested backbone identity; it does not run models and labels untested cells as untested. Model names are user-configured, not hardcoded as a supported-model allowlist.

Real acceptance requires licensed data, pinned assets, an actual serving endpoint and the benchmark's native scorer. A patch, a synthetic fixture, a mock HTTP reply or an imported adapter is never an official score. See the staged validation policy.

License

MIT for this repository's code. External dependencies retain their own licenses; consult THIRD_PARTY_NOTICES.md before downloading or distributing them. Registry publication follows the acceptance and ownership checks.