agentic-systems 1.0.7

Agentic Systems: auditable agentic systems with tools, skills, agents, systems, graphs, environments and evals across Python Direct, OpenAI Runtime, Bedrock Runtime and vLLM Runtime.

Agentic Systems

Agentic Systems is a Python framework for building industrial, auditable and provider-agnostic agentic systems.

It gives you one public API to compose tools, skills, agents, systems, graphs, environments, evals, contracts, lineage memory and stable human-readable outputs. It 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.

pip install agentic-systems

import agentic_systems as toolkit

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 makes those concerns first-class: runtime, tools, contracts, result envelopes, lineage, environments, eval reports and human output are all part of 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	Thin facades for LangGraph, Strands and OpenAI Agents-style workflows.

Core Model

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

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

The goal is not to hide complexity. The goal is to make agentic computation explicit, inspectable and reusable.

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	OpenAI-compatible vLLM provider path for local or Colab GPU inference.
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=....

Canonical framework facades:

Framework	Use
langgraph	LangGraph graph orchestration.
openai-agents	OpenAI Agents-style integration over the selected runtime.
strands	Strands integration over the selected runtime.

Providers decide where execution runs. Frameworks decide who owns the outer orchestration loop. 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.AgenticSystem(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.",
)

system.inspect().raise_if_errors()

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.AgenticSystem(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"],
)

graph = toolkit.build_single_agent_step_graph(agent, input="input")

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

next_state = graph.invoke(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.

tutorials/00_runtime_api.ipynb
tutorials/00_runtime_bedrock_provider_api.ipynb
tutorials/00_runtime_openai_provider_api.ipynb
tutorials/00_runtime_vllm_provider_api.ipynb
tutorials/00_runtime_scheduler_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_multi_agentic_system_api.ipynb
tutorials/12_multi_agentic_graph_api.ipynb
tutorials/13_single_agentic_system_api.ipynb
tutorials/14_multi_agentic_system_api.ipynb

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

Documentation

docs/API.md
docs/CLI.md
docs/ARCHITECTURE.md
docs/BOUNDARIES.md
docs/ONBOARDING_FIRST_RUN.md
docs/RUNRESULT_FINAL_ANSWER.md
docs/PYTEST_COVERAGE_REPORT.md
docs/CONTRIBUTING_CHECKLIST.md
docs/ROADMAP_CHECKPOINTS.md

Quality Gate

Current verified status:

Version: 1.0.7
PyPI package: agentic-systems
Tests: 308 passed, 0 skipped
Coverage: 100.00%
TOTAL statements: 5380
TOTAL missing: 0

Run validation locally:

python -m pytest -q
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

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: [e-mail]

E-Mail 2: [e-mail]

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

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