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 2.0 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: 2.0.0 is the Provider x Framework architecture: 78 stable top-level exports, 370 traced export/member IDs, 10 shared contract scenarios, four real Framework adapters, and no silent fallback.

Installation

pip install agentic-systems

import agentic_systems as toolkit

Install the Agentic Systems skill

The v2.0.0 GitHub release includes a credential-free skill ZIP whose archive root is agentic-systems/.

Expand-Archive .\agentic-systems-skill-2.0.0.zip `
  -DestinationPath "$env:USERPROFILE\.codex\skills" -Force

unzip agentic-systems-skill-2.0.0.zip -d ~/.codex/skills

Restart or reload Codex, then invoke $agentic-systems. OpenAI Skills upload surfaces that accept a ZIP can consume the same artifact. Download the standalone skill and the complete ten-system Studio from the v2.0.0 release.

Release assets:

• agentic-systems-skill-2.0.0.zip: Codex/OpenAI skill package.
• agentic-systems-studio-2.0.0.zip: portable Studio with ten nested systems.
• SHA256SUMS-2.0.0.txt: hashes for the Python and product artifacts.

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	Native, LangGraph, OpenAI Agents, and Strands execution over explicit Providers.

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, vllm-runtime, and ollama-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. That signal may be the standard AWS credential chain (signed with SigV4) or the native AWS_BEARER_TOKEN_BEDROCK API key; both use the same boto3 bedrock-runtime Provider and a region alone does not outrank another usable Provider.

Canonical framework facades:

Framework	Use
langgraph	LangGraph graph orchestration.
openai-agents	OpenAI Agents Runner, native Tools, handoffs, guardrails, sessions and MCP.
strands	strands.Agent, native Tools/MCP, hooks and lifecycle.

Providers decide where inference runs. The selected Framework always owns its orchestration loop, and RunResult.meta["framework_adapter"] records the adapter that executed. Configuration details live in docs/ONBOARDING_FIRST_RUN.md, docs/CLI.md and the layered tutorials/core, tutorials/providers and tutorials/frameworks paths.

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 canonical learning path is layered by responsibility:

Layer	Purpose	Default execution
tutorials/core	Agentic Systems grammar and composition	Python Runtime, Native Framework, portable Graph
tutorials/providers	Inference boundary	Explicit preflight; external execution when configured
tutorials/frameworks	Native orchestration SDK	Real SDK offline; Provider auto is optional
tutorials/api	Exact public contract	Generated manifest, documentation and CLI/Pytest traceability

Provider notebooks use one public route and choose Run All:

Provider	Readiness inputs	Notebook
OpenAI	OPENAI_API_KEY; OPENAI_MODEL optional	tutorials/providers/01_openai.ipynb
vLLM	VLLM_BASE_URL and VLLM_MODEL	tutorials/providers/03_vllm.ipynb
Bedrock	Region plus SigV4 credentials or AWS_BEARER_TOKEN_BEDROCK	tutorials/providers/02_bedrock.ipynb
Ollama	OLLAMA_MODEL; OLLAMA_BASE_URL optional	tutorials/providers/04_ollama.ipynb

The Framework notebooks execute LangGraph, OpenAI Agents and Strands SDKs for real with python-runtime; the matrix notebook covers all 20 declared pairs. Optional RUN_LIVE=1 changes only the Provider. RUN_LIVE=0 forces a fully offline validation.

The full ordered inventory of 21 Python notebooks and their 21 CLI mirrors lives in tutorials/README.md. There is no active examples/ root or duplicate notebook route.

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: 2.0.0
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 100%
Canonical notebooks: 17 deterministic executed; 3 live 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