ReAct research

Professional, replaceable agent designs; course qualification in progress.

Status: in development, not course-qualified. The working source below is not a claim of a passed 3×3 live matrix. See the execution ledger for qualified facts and open gaps.

Design and adaptation boundary

Alternate grounded actions with observation-driven decisions. Reasoning is provider output when available, not fabricated private thoughts. Primary source.

Framework versus application

ResearchState retains bounded recent observations. AgentModule.prepare exposes task and evidence; reduce incorporates real ToolResult facts. Engine owns every request and action.

QitOS supplies model transactions/usage, tool permission/validation, Env execution, Session, ArtifactRef and Trajectory. The application supplies tasks, policy, independent acceptance checks and memory/skill selection. This iteration adds custom agent_factory composition, persistent skill revisions, full-body selection, explicit Memdir deletion and correct artifact data authority.

The core design increment is in agent.py below. The CLI shows configuration and resource ownership explicitly; evaluate.py is the controller checker. Tasks use repository-owned synthetic professional scenarios, not paper benchmarks or customer data.

Install, configure and run

First install the QitOS wheel built from this iteration; the current PyPI release cannot stand in for unpublished APIs. Then install this project. Keep actual addresses and credentials outside Git. The private model file is a full qitos.agent configuration; this launcher selects only its model section, never an environment-variable key.

Use a new external directory for every run; resume reconstructs this project’s factory and resolver.

The output default is 10,240 and may be raised in private model configuration. Task request/step/time guards come from configuration. validate does not call a model; —live is mandatory for execution. Docker failure must not silently fall back to the host. Retain unsuccessful results and human interventions.

Verification, exercise and composition

Independent checks examine sources/numbers or executed code, not a model’s success claim. Plan revisions, actual skill loading and child identities require separate mechanism evidence. Session restore is not filesystem rollback. Generated code executes only in the restricted Env.

Exercise: Replace the evidence-ranking strategy, keeping the same numeric/source checker.

Composition: Run identical tasks with PlanAct and compare actual request counts, corrections and independent outcomes.

The required matrix is three tasks, three repetitions each. ReAct/PlanAct share tasks; static planning, no-memory and no-skills are explicit controls. A single pass is not a performance result. Raw traces stay private until redistribution and sanitization checks authorize a derived publication.

python -m pip install .
python -m qitos_lab_react validate --config agent.yaml --root /tmp/lab-validation
python -m qitos_lab_react run --config agent.yaml --model-config /private-config/model.yaml --credentials /private-config/credentials.yaml --root /private-runs/react-attempt --task 0 --live

Complete implementation: examples/projects/react_research/src/qitos_lab_react/agent.py.

Extracted from complete source: the design increment

This excerpt is generated from the complete project, not a separately maintained implementation. Complete installable files follow.

    def prepare(self, state):
        return json.dumps(
            {
                "task": state.task,
                "phase": state.phase,
                "plan": state.plan,
                "plan_version": state.plan_version,
                "recent_results": state.evidence[-4:],
            },
            ensure_ascii=False,
        )

    def reduce(self, state, observation, decision):
        for item in observation.action_results:
            state.evidence.append(item.to_model_dict(max_chars=6000))
        state.evidence = state.evidence[-8:]
        return state

Complete files: save in the project root

src/qitos_lab_react/__main__.py

"""Explicit launch configuration; no credentials or model calls during validate."""

import argparse
from dataclasses import asdict, replace
from importlib.resources import files
import json
from pathlib import Path
import sys

from qitos.config import (
    BudgetConfig,
    LocalCredentialFileResolver,
    SessionConfig,
    TrajectoryConfig,
    build_agent_composition,
    load_agent_config,
)

from .agent import build_factory
from .evaluate import evaluate


def main():
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument("command", choices=["validate", "run", "resume", "inspect"])
    parser.add_argument("--config", type=Path, required=True)
    parser.add_argument("--model-config", type=Path)
    parser.add_argument("--credentials", type=Path)
    parser.add_argument("--root", type=Path, required=True)
    parser.add_argument("--task", type=int, choices=[0, 1, 2], default=0)
    parser.add_argument("--session")
    parser.add_argument("--live", action="store_true")
    parser.add_argument(
        "--variant",
        choices=["default", "static", "no-memory", "no-skills"],
        default="default",
    )
    args = parser.parse_args()
    config = load_agent_config(args.config)
    tasks = json.loads(files(__package__).joinpath("tasks.json").read_text())
    task = tasks[args.task]
    if args.command == "validate":
        print(
            json.dumps(
                {"status": "configuration_valid", "tasks": len(tasks), "live": False}
            )
        )
        return 0
    root = args.root.resolve()
    if any((parent / ".git").exists() for parent in (root, *root.parents)):
        raise ValueError("run_root_must_be_outside_git")
    root.mkdir(parents=True, exist_ok=True, mode=0o700)
    if args.command == "inspect":
        from qitos.qita.reader import candidate_file_reader

        reader = candidate_file_reader(root / "trajectory.journal")
        print(json.dumps({"runs": [asdict(run) for run in reader.discover_runs()]}))
        return 0
    if not args.live or args.credentials is None:
        raise ValueError("explicit_live_and_credentials_required")
    if args.model_config is not None:
        private = args.model_config.resolve()
        if any((parent / ".git").exists() for parent in private.parents):
            raise ValueError("model_config_must_be_outside_git")
        config = replace(config, model=load_agent_config(private).model)
    output_limit = max(10240, config.model.request.max_tokens or 0)
    request = replace(config.model.request, max_tokens=output_limit)
    config = replace(
        config, model=replace(config.model, request=request, max_tokens=output_limit)
    )
    workspace = root / "input"
    if args.command == "run":
        workspace.mkdir(exist_ok=False)
        for name, content in task["inputs"].items():
            target = workspace / name
            target.parent.mkdir(parents=True, exist_ok=True)
            target.write_text(content)
    config = replace(
        config,
        budgets=config.budgets
        or BudgetConfig(max_steps=80, max_requests=80, max_runtime_seconds=3600),
        runtime=replace(
            config.runtime,
            environment=replace(config.runtime.environment, workspace=str(workspace)),
            session=SessionConfig(store="sqlite", path=str(root / "sessions.sqlite3")),
            trajectory=TrajectoryConfig(output=str(root / "trajectory.journal")),
        ),
    )
    resolver = LocalCredentialFileResolver(args.credentials, repository_root=Path.cwd())
    factory = build_factory(task, root=root, variant=args.variant)
    with build_agent_composition(
        config, credential_resolver=resolver, agent_factory=factory
    ) as composition:
        session = (
            composition.restore(args.session)
            if args.command == "resume"
            else composition.session(task["task"])
        )
        (root / "session.json").write_text(
            json.dumps({"session_id": session.session_id.value})
        )
        result = session.run()
        verdict = evaluate(result, task)
        report = {
            "session_id": session.session_id.value,
            "run_id": result.run_id,
            "stop_reason": str(result.state.stop_reason),
            "evaluation": verdict,
            "tool_calls": result.tool_calls_by_name,
            "variant": args.variant,
        }
        (root / "report.json").write_text(
            json.dumps(report, ensure_ascii=False, indent=2)
        )
        print(json.dumps(report, ensure_ascii=False))
        return 0 if verdict["passed"] else 2


if __name__ == "__main__":
    try:
        raise SystemExit(main())
    except Exception as error:
        # Raw transport/factory errors can contain private endpoint details.
        print(
            json.dumps({"status": "failed", "error_type": type(error).__name__}),
            file=sys.stderr,
        )
        raise SystemExit(2) from None

src/qitos_lab_react/tasks.json

[
  {
    "task": "Audit whether candidate B reduces latency without reducing reliability. Reconcile the preliminary memo with raw data and the registered weighting rule. Compute weighted_latency_a, weighted_latency_b, latency_reduction_percent and failure_rate_b_percent. Produce a qualified recommendation, not a blanket claim.",
    "inputs": {
      "protocol.md": "Protocol R17. Compare weighted latency using production mix 80% small, 20% large. Report failure rate over all attempted requests; do not remove failures. Latency measurements are milliseconds for successful requests. This is a synthetic teaching dataset, not a real deployment benchmark.\n",
      "results.csv": "group,latency_a,latency_b,attempts_b,failures_b\nsmall,100,80,800,8\nlarge,500,450,200,12\n",
      "preliminary.md": "PRELIMINARY, not validated: candidate B halves latency and has no errors. Based only on a smoke test. Superseded by results.csv.\n",
      "operations.md": "Reliability policy: failure rate must be <=1%. A performance improvement alone is insufficient for adoption. Error recovery must be investigated separately.\n",
      "limitations.md": "Two workload groups, one run. No confidence interval or long-duration measurement is available.\n"
    },
    "required_sources": [
      "protocol.md",
      "results.csv",
      "operations.md"
    ],
    "expected_metrics": {
      "weighted_latency_a": 180,
      "weighted_latency_b": 154,
      "latency_reduction_percent": 14.444444,
      "failure_rate_b_percent": 2
    }
  },
  {
    "task": "Audit experiment R18 for sample leakage and subgroup regressions. Compute valid_samples, excluded_samples, baseline_accuracy_percent, candidate_accuracy_percent, minority_change_points using only valid rows. Explain why aggregate results are insufficient for adoption.",
    "inputs": {
      "protocol.md": "R18: exclude rows with split=leaked. Accuracy = total correct / total count. Report minority change as candidate accuracy minus baseline accuracy, in percentage points. Real deployment qualification is out of scope.\n",
      "results.csv": "group,split,count,correct_a,correct_b\nmajority,valid,80,64,72\nminority,valid,20,16,12\nmajority,leaked,10,10,10\n",
      "preliminary.md": "An early memo claims all subgroups improved. It included leaked samples and did not break down minority outcomes.\n",
      "operations.md": "Adoption requires no subgroup regression. Minority group is small; collect more evidence before a final deployment decision.\n",
      "limitations.md": "Synthetic teaching experiment, no repeat runs or variance estimates.\n"
    },
    "required_sources": [
      "protocol.md",
      "results.csv",
      "operations.md"
    ],
    "expected_metrics": {
      "valid_samples": 100,
      "excluded_samples": 10,
      "baseline_accuracy_percent": 80,
      "candidate_accuracy_percent": 84,
      "minority_change_points": -20
    }
  },
  {
    "task": "Reconcile R19 throughput and recovery claims. Compute steady_throughput, recovery_seconds, availability_percent from the authoritative log under the registered formula. Explain the contradiction with the sales memo and distinguish process recovery from external exactly-once effects.",
    "inputs": {
      "protocol.md": "R19 window is 100 seconds. Availability excludes seconds explicitly marked unavailable. Steady throughput excludes recovery and equals steady completed tasks / steady seconds. Recovery duration is restored_at minus failure_at.\n",
      "results.csv": "steady_tasks,steady_seconds,failure_at,restored_at,unavailable_seconds\n720,90,30,40,10\n",
      "preliminary.md": "Sales draft: zero downtime, throughput 10/s, exactly-once external effects. These statements were never independently tested.\n",
      "operations.md": "Logs prove process restoration only. External write acknowledgement was lost; effect reconciliation remains unresolved. Do not equate retry success with exactly-once delivery.\n",
      "limitations.md": "One induced failure; no cross-host replication or high-availability evidence.\n"
    },
    "required_sources": [
      "protocol.md",
      "results.csv",
      "operations.md"
    ],
    "expected_metrics": {
      "steady_throughput": 8,
      "recovery_seconds": 10,
      "availability_percent": 90
    }
  }
]

src/qitos_lab_react/evaluate.py

"""Controller-side evaluator, never staged into the agent's workspace."""

import math


def evaluate(result, task):
    submitted = []
    read_sources = set()
    for record in result.records:
        for item in record.action_results:
            output = item.output
            if (
                item.tool_name == "read_file"
                and item.status == "success"
                and isinstance(output, dict)
            ):
                if isinstance(output.get("path"), str):
                    read_sources.add(output["path"])
            if (
                item.tool_name == "submit_report"
                and item.status == "success"
                and isinstance(output, dict)
            ):
                if "submitted_report" in output:
                    submitted.append(output["submitted_report"])
    report = submitted[-1] if submitted and isinstance(submitted[-1], dict) else {}
    citations = report.get("citations", [])
    valid_citations = isinstance(citations, list) and all(
        isinstance(item, str) for item in citations
    )
    limitations = report.get("limitations", [])
    checks = {
        "report_submitted": bool(submitted),
        "conclusion": isinstance(report.get("conclusion"), str)
        and bool(report.get("conclusion")),
        "limitations": isinstance(limitations, list)
        and bool(limitations)
        and all(isinstance(item, str) and item.strip() for item in limitations),
        "citations": valid_citations
        and set(task["required_sources"]) <= set(citations),
        "actual_reads": set(task["required_sources"]) <= read_sources,
        "final": str(result.state.stop_reason) == "final",
    }
    metrics = report.get("metrics", {})
    for key, expected in task["expected_metrics"].items():
        value = metrics.get(key) if isinstance(metrics, dict) else None
        checks[key] = type(value) in (int, float) and math.isclose(
            value, expected, rel_tol=0.005, abs_tol=0.005
        )
    return {"passed": all(checks.values()), "checks": checks, "submitted": report}

src/qitos_lab_react/agent.py

"""Evidence-driven policy: the framework owns requests, tools and Session."""

from dataclasses import dataclass, field
import json

from qitos.core.agent_module import AgentModule
from qitos.core.state import StateSchema
from qitos.core.function_tool_decorator import function_tool


@dataclass
class ResearchState(StateSchema):
    evidence: list[dict] = field(default_factory=list)
    plan: list[str] = field(default_factory=list)
    plan_version: int = 0
    phase: str = "execute"


class ResearchAgent(AgentModule):
    def init_state(self, task, **kwargs):
        return ResearchState(task=task, max_steps=self.config.get("max_steps", 80))

    def base_persona_prompt(self, state):
        return (
            "You are an evidence-driven research agent. Inspect the actual input files. "
            "Distinguish preliminary claims from verified evidence. Do calculations "
            "with tools. Correct mistaken assumptions when observations disagree."
        )

    def task_policy_prompt(self, state):
        return (
            "Produce report.json containing conclusion (string), metrics (object of numbers), "
            "citations (list of exact input filenames), and limitations (list of strings). "
            "Cite at least three relevant sources. Read before concluding. Use submit_report "
            "to submit the full JSON as a string, then give a concise final answer. "
            "Do not claim evidence that you did not inspect."
        )

    # docs:start design
    def prepare(self, state):
        return json.dumps(
            {
                "task": state.task,
                "phase": state.phase,
                "plan": state.plan,
                "plan_version": state.plan_version,
                "recent_results": state.evidence[-4:],
            },
            ensure_ascii=False,
        )

    def reduce(self, state, observation, decision):
        for item in observation.action_results:
            state.evidence.append(item.to_model_dict(max_chars=6000))
        state.evidence = state.evidence[-8:]
        return state

    # docs:end design


def build_factory(task, **resources):
    @function_tool(read_only=True)
    def submit_report(report_json: str):
        """Submit the complete report JSON; independent evaluation runs afterwards."""
        value = json.loads(report_json)
        if not isinstance(value, dict):
            raise ValueError("report_must_be_object")
        return {"submitted_report": value}

    def factory(*, config, model, tool_registry, protocol, parser):
        tool_registry.register(submit_report)
        return ResearchAgent(
            llm=model,
            tool_registry=tool_registry,
            model_protocol=protocol.id,
            max_steps=config.max_steps,
            model_parser=parser,
        )

    return factory

src/qitos_lab_react/__init__.py

"""Independently installable research course."""

pyproject.toml

[build-system]
requires = ["setuptools>=68"]
build-backend = "setuptools.build_meta"
[project]
name = "qitos-lab-react"
version = "0.1.0"
requires-python = ">=3.10"
dependencies = ["qitos[openai]"]
[tool.setuptools.packages.find]
where = ["src"]
[tool.setuptools.package-data]
qitos_lab_react = ["tasks.json"]

agent.yaml

schema: qitos.agent
agent:
  name: design-lab-react
  protocol: json_decision_multi_v1
model:
  provider: openai_compatible
  model: example-model
  base_url: https://provider.example/v1
  credential:
    ref: research-model
  request:
    max_tokens: 10240
    timeout_seconds: 180
    retries: 0
tools:
  preset: env_coding
runtime:
  environment:
    type: docker
    workspace: .
    image: python:3.12-slim
  session:
    mode: durable
    store: sqlite
    path: ./sessions.sqlite3
  trajectory:
    enabled: true
    output: ./trajectory.journal
budgets:
  max_steps: 80
  max_requests: 80
  max_runtime_seconds: 3600
failure_policy:
  tool: continue