Agentic Systems

Agentic Systems gives Python applications one explicit, testable model for building and evaluating agentic workflows across deterministic and model-backed runtimes.

It does so through a computational grammar for building, executing, observing, and evaluating intelligent systems.

Its Python API turns that grammar into explicit, composable abstractions: tools, skills, agents, systems, graphs, environments, evals, contracts, lineage memory and stable human-readable outputs. Runtime and Provider remain separate concepts, so the same computational model supports deterministic execution, OpenAI, AWS Bedrock Runtime and OpenAI-compatible vLLM endpoints through explicit runtime selection.

Use it when agentic workloads need to be observable, testable, portable and ready for repeated execution, not just notebook demos.

Agentic Systems 1.1 keeps explicit traceability between its API, documentation, tutorials, and tests.

API -> Docs -> Tutorials -> explicit automated or manual evidence

Public concepts are defined in the API, explained in the documentation, taught through the canonical tutorials, and checked by explicit release gates.

Release status: 1.1.3 consolidates the current documentation and owner-based test architecture while preserving the 111-symbol public API and 1.1 runtime behavior.

Installation

pip install agentic-systems

import agentic_systems as toolkit

For an installed-package smoke test and provider notebook setup, follow First Run Onboarding.

Why Agentic Systems

Agent prototypes usually break when they leave the demo path because the important questions are not represented in code:

• Which provider actually ran this workload?
• Which tools were available and which ones were called?
• What evidence supports the answer?
• Can the same behavior be evaluated again?
• Can the same agent run locally, with OpenAI, with Bedrock or with vLLM?
• Can deterministic tools and language-model reasoning share one execution contract?

Agentic Systems represents those concerns as a computational grammar: runtime, tools, contracts, result envelopes, lineage, environments, eval reports and human output are first-class concepts in the same API.

What You Can Build

Layer	What it gives you
Tools	Typed executable capabilities with structured results.
Skills	Packaged tools, instructions, contracts, assets and metadata.
Agents	Context transformed into actions through deterministic or LM-backed execution.
Systems	A native workspace that registers runtime, tools, skills, agents and environments.
Graphs	Explicit state, nodes and edges for orchestrating agents and systems.
Environments	Episodic execution over records, transitions, rewards and history.
Evals	Repeatable validation cases with pass/fail reporting.
Lineage Memory	Human-readable explanation of what happened and why.
Integrations	A portable Graph backend, an optional LangGraph adapter, and explicit framework identities.

Core Model

Tool -> Skill -> Agent -> System -> Graph -> Environment -> Eval

Runtime, Provider, Contracts, Lineage Memory and Human Output support the whole cycle.

The grammar does not hide complexity. It makes intelligent computation explicit, inspectable, portable and reusable across execution backends.

Runtime And Providers

Runtime selection is explicit and inspectable:

scheduler = toolkit.scheduler(timeout_s=30, max_retries=0, max_tool_calls=5)
runtime = toolkit.runtime(provider="auto", scheduler=scheduler)

toolkit.show(runtime.describe())

Canonical providers:

Provider	Use
bedrock-runtime	AWS Bedrock Runtime provider path.
openai-runtime	Direct OpenAI provider path.
vllm-runtime	Client path for an existing OpenAI-compatible vLLM endpoint.
python-runtime	Local deterministic execution for tools, policies and smoke tests.
auto	Selects a concrete provider from environment signals before execution.

Default provider="auto" priority is bedrock-runtime, then openai-runtime, then vllm-runtime. Override it with provider_priority=[...] or AGENTIC_SYSTEMS_PROVIDER_PRIORITY=.... Bedrock is considered configured only when both a region and an AWS authentication signal are present; a region alone does not outrank a usable OpenAI or vLLM configuration.

Canonical framework facades:

Framework	Use
langgraph	LangGraph graph orchestration.
openai-agents	Style-only identity over the selected runtime; no OpenAI Agents SDK adapter.
strands	Declarative compatibility identity; no Strands SDK adapter.

Providers decide where execution runs. A real Framework adapter may own the outer orchestration loop; an accepted framework label alone does not prove adapter execution. Configuration details live in docs/ONBOARDING_FIRST_RUN.md, docs/CLI.md and the tutorials/00_runtime_* notebooks.

From Zero-to-Hero

1. Deterministic Tool Agent

import agentic_systems as toolkit

@toolkit.tool
def add(a: int, b: int) -> dict:
    """Add two integers."""
    return {"result": a + b}

agent = toolkit.agent(
    name="calculator",
    instructions="Use the available tools and return a structured answer.",
    tools=[add],
    runtime=toolkit.runtime(provider="python-runtime"),
)

result = agent.run({"tool": "add", "input": {"a": 2, "b": 3}})
toolkit.human_result(result)

2. Provider-Backed Agent

runtime = toolkit.runtime(provider="auto")

agent = toolkit.agent(
    name="portable_calculator",
    instructions="Use tools when useful. Return a concise final answer.",
    tools=[add],
    runtime=runtime,
)

result = agent.run("Add 10 and 20 using the tool.")
toolkit.human_result(result, pretty=False, show_lineage=True)

3. Native AgenticSystem

Use AgenticSystem when you want a single system boundary that owns runtime, tools, skills, agents and diagnostics.

system = toolkit.system(runtime=toolkit.runtime(provider="python-runtime"))

@system.tool
def multiply(a: int, b: int) -> dict:
    return {"result": a * b}

agent = system.agent(
    name="multiplier",
    instructions="Use registered tools to solve arithmetic requests.",
)

inspection = system.inspect()
inspection.raise_if_errors()
toolkit.show(inspection.to_dict(), title="Static inspection")
print(inspection.human_text())

result = agent.run({"tool": "multiply", "input": {"a": 6, "b": 7}})
toolkit.human_result(result)

4. Skills

A skill packages tools, instructions, contracts, assets and metadata.

skill = toolkit.skill(
    name="calculator_skill",
    description="Arithmetic tools and instructions.",
    tools=[add],
    prompts={"instructions": "Use arithmetic tools and return a structured answer."},
)

agent = toolkit.agent(
    name="skill_agent",
    instructions=skill.instructions,
    skills=[skill],
    runtime=toolkit.runtime(provider="python-runtime"),
)

5. Graphs

Graphs coordinate state, nodes and edges. They do not replace tools, agents or systems; they orchestrate them.

system = toolkit.system(runtime=toolkit.runtime(provider="python-runtime"))

@system.tool
def double(value: int) -> dict:
    return {"value": value * 2, "ok": True}

agent = system.agent(
    name="doubler",
    instructions="Call double when the input asks for a doubled value.",
    tools=["double"],
)

agent_step = toolkit.agent_node(
    agent,
    input="input",
    output=lambda result, _state: {"result": result},
    trace=None,
)
graph = toolkit.graph(
    nodes={"agent_step": agent_step},
    edges=[("START", "agent_step"), ("agent_step", "END")],
    engine="portable",
)

state = {
    "input": {"tool": "double", "input": {"value": 21}},
    "history": [],
}

next_state = graph.run(state)
toolkit.show(next_state, title="Graph state")

6. Environments And Evals

Use environments when execution is episodic. Use evals when behavior must be checked repeatedly.

records = [
    {"input": {"tool": "double", "input": {"value": 21}}},
]

def reward_fn(state, row, action, env) -> float:
    return 1.0 if state.get("result", {}).get("ok") else 0.0

environment = system.environment(records, graph=graph, reward_fn=reward_fn)
environment.reset(seed=0)
environment.step()

toolkit.show(toolkit.environment_summary(environment), title="Environment summary")

cases = [
    {
        "name": "double_21",
        "input": {"tool": "double", "input": {"value": 21}},
        "expected": {"data_contains": {"value": 42, "ok": True}},
    }
]

report = system.eval(agent, cases)
toolkit.human_result(report)
report.raise_if_failed()

7. Results, Lineage And Human Output

Every execution returns a stable RunResult envelope with answer, evidence, usage, validation, errors and tool events.

result.final       # user-facing answer dictionary
result.data        # reusable evidence payload
result.text        # text fallback
result.tool_events # executed tool events
result.usage       # runtime usage metadata
result.validation  # contract validation
result.errors      # structured errors

Render one execution or a batch with human_result.

toolkit.human_result(result, pretty=False, show_lineage=True)
toolkit.human_result([result_a, result_b], pretty=False)

Use output schemas when the expected response fields matter:

schema = toolkit.output_schema(["procedure", "final_result"])
answer = toolkit.final_answer(
    {"procedure": ["2 + 3"], "final_result": 5},
    schema=schema,
)

Lineage Memory explains what happened, how it happened and why the result is supported.

memory = result.lineage(
    name="calculator.run",
    question="What is 2 + 3?",
    goal="Explain the answer from tool evidence.",
)

toolkit.show(memory)
memory.to_prompt_context(max_chars=1200)

Integrations

Use integrations when an external framework should own orchestration while Agentic Systems keeps the same tools, runtime, contracts, lineage and human output conventions.

runtime = toolkit.runtime(provider="auto")

agent = toolkit.agent(
    name="portable_agent",
    runtime=runtime,
    framework="openai-agents",
)

Integration-specific arguments stay owned by the selected framework. Agentic Systems keeps a thin facade; it does not reinterpret or hide framework-specific behavior.

CLI

The package exposes diagnostics and inspection commands:

agentic-systems version
agentic-systems doctor --json
agentic-systems runtime --provider auto --json
agentic-systems api --tier public --json
agentic-systems public-api --all --json

The CLI is for inspection, diagnostics and packaging smoke tests. It should not contain business logic.

Tutorials

The official learning path is tutorials/. It explains and exercises the public API directly from import agentic_systems as toolkit.

Provider notebooks require no activation cell. Configure the external boundary, open the corresponding notebook, and choose Run All:

Provider	Readiness inputs	Notebook
OpenAI	OPENAI_API_KEY; optional OPENAI_MODEL	00_runtime_openai_provider_api.ipynb
vLLM	VLLM_BASE_URL and VLLM_MODEL	00_runtime_vllm_provider_api.ipynb
Bedrock	Standard AWS credential chain; optional model/region overrides	00_runtime_bedrock_provider_api.ipynb

Live execution is the default when readiness passes. A provider-specific RUN_*_LIVE=0 is an explicit opt-out, not a prerequisite.

tutorials/00_runtime_api.ipynb
tutorials/00_runtime_bedrock_provider_api.ipynb
tutorials/00_runtime_openai_provider_api.ipynb
tutorials/00_runtime_scheduler_api.ipynb
tutorials/00_runtime_vllm_provider_api.ipynb
tutorials/01_tool_api.ipynb
tutorials/02_skill_api.ipynb
tutorials/03_agent_api.ipynb
tutorials/04_human_result_api.ipynb
tutorials/05_lineage_memory_api.ipynb
tutorials/06_integrations_strands_api.ipynb
tutorials/07_integrations_openai_runtime_api.ipynb
tutorials/08_system_api.ipynb
tutorials/09_graph_api.ipynb
tutorials/10_environment_eval_api.ipynb
tutorials/11_single_agentic_system_api.ipynb
tutorials/12_multi_agentic_system_api.ipynb
tutorials/13_multi_agentic_graph_api.ipynb

There is no active examples/ root. Tutorials are the executable documentation.

Documentation

Use the documentation map to choose the user guide, conceptual model, API/CLI reference or current engineering contracts. Version history is recorded in the changelog; published artifact evidence belongs to GitHub Releases.

Quality Gate

Current verified status:

Version: 1.1.3
PyPI package: agentic-systems
Tests: run `python -m pytest` for the current count
Core coverage: 100.00%
Coverage scope: Bedrock facade and internal package excluded from core; separately gated at 53.1%
Canonical notebooks: 13 deterministic executed; 5 Provider notebooks checked statically

Run validation locally:

python -m pytest -q -W error::RuntimeWarning
python -m ruff check src tests
python -m compileall -q src tests tutorials
agentic-systems doctor --json

For full coverage validation:

python -m pytest --cov=agentic_systems --cov-report=term-missing -q
python -m pytest tests/providers -q --cov-config=.coveragerc-bedrock --cov=agentic_systems.providers.bedrock_runtime --cov=agentic_systems.providers.bedrock --cov-report=term-missing

Design Principles

One public import.
Explicit runtime selection.
Provider and framework separation.
Typed tools and predictable payloads.
Stable result envelopes.
Contracts before hidden behavior.
Lineage before opaque answers.
Evaluation before claims.
Diagnostics without leaking secrets.
Tutorials as executable API documentation.

Contact

Author: Jacobo Gerardo Gonzalez Leon

E-Mail 1: jacobogerardo.gonzalez@bbva.com

E-Mail 2: jacoboggleon@gmail.com

LinkedIn: https://www.linkedin.com/in/jacoboggleon/

GitHub Repo: https://www.github.com/JacoboGGLeon/agentic_systems