Oracle Generative AI · Multi-Agent Reasoning Orchestrator SDK
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Documentation · Cognitive Router · Multi-agent · DeepAgent · 56 Tutorials · Workbench

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Your first agent — 5 lines

from locus import Agent

agent = Agent(model="oci:openai.gpt-5")
print(agent.run_sync("What is the capital of France?").text)
# → Paris

That's it. Agent handles the model call, the response, and any retries. Swap "oci:openai.gpt-5" for "openai:gpt-4o" or "anthropic:claude-sonnet-4-6" — the interface stays the same.

Add a tool

Tools are plain Python functions. The model sees the docstring and decides when to call them.

from locus import Agent, tool

@tool
def get_weather(city: str) -> str:
    """Return the current weather for a city."""
    return weather_api.fetch(city)

agent = Agent(
    model="oci:openai.gpt-5",
    tools=[get_weather],
    system_prompt="You are a helpful travel assistant.",
)

print(agent.run_sync("Should I bring an umbrella to Tokyo tomorrow?").text)

The agent loops — Think → call tool → Think → answer — until it's done. Add @tool(idempotent=True) to any tool that must not fire twice (bookings, payments, alerts). The loop dedupes on (name, args) so retries are safe by design.

Install

pip install "locus-sdk[oci]"           # OCI GenAI (90+ models, day-0)
pip install "locus-sdk[openai]"        # OpenAI
pip install "locus-sdk[anthropic]"     # Anthropic
pip install "locus-sdk[sdk]"           # everything

No mandatory cloud account to start — MockModel lets every tutorial run offline.

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The cognitive router — describe what you need, get the right shape

Once you know agents, the next step is knowing which shape to use. The cognitive router takes a natural-language task, selects from eight proven coordination patterns, and instantiates the right primitive — without you hand-coding the topology.

from locus.deepagent.workflow import create_research_workflow, KEY_PROMPT

workflow = create_research_workflow(
    model=get_model(),
    tools=[web_search, web_fetch],
    grounding_threshold=0.65,
)

result = await workflow.execute({KEY_PROMPT: "What happened in mathematics in 2026?"})
print(result.final_state["summary"])

The workflow runs: execute (ReAct) → causal inference → summarize → grounding eval → lightweight regenerate or full replan if grounding is too low. Every step emits research.* SSE events you can stream in real time.

→ Cognitive router concept · Research workflow

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Seven coordination patterns

When one agent isn't enough, locus gives you seven in-process shapes plus cross-process A2A. Every pattern uses the same Agent class and the same event stream.

Pattern	When to use
SequentialPipeline	A → B → C in order; each output feeds the next
ParallelPipeline	Fan out to N agents simultaneously, merge results
LoopAgent	Refine until a condition fires (PASS/FAIL, confidence, iteration cap)
Orchestrator + Specialists	One coordinator routes to domain experts in parallel
Swarm	Open-ended research; peers share a task queue and context
Handoff	Escalation desk; conversation moves with full history to the next specialist
StateGraph	Explicit DAG with conditional edges, cycles, and human-in-the-loop gates
A2A	Cross-process meshes over HTTP; agents advertise capabilities via AgentCard

from locus import Agent, SequentialPipeline

researcher = Agent(model=model, system_prompt="Find three key facts about the topic.")
critic     = Agent(model=model, system_prompt="Identify any gaps or errors in the research.")
writer     = Agent(model=model, system_prompt="Write a clear one-paragraph summary.")

result = await SequentialPipeline(agents=[researcher, critic, writer]).run(
    "Explain quantum entanglement to a high-schooler."
)
print(result.text)

→ All patterns

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What you get

🧭 Cognitive router	Describe a task → eight named protocols → right primitive compiled automatically. LLM fills a typed schema; routing is deterministic.
🤝 Multi-agent	Seven native patterns + cross-process A2A. One Agent class. One event stream.
🔬 DeepAgent	create_deepagent (single agent, per-turn grounding) and create_research_workflow (StateGraph with post-hoc grounding eval + two-level recovery).
📡 Observability	Opt-in EventBus — one run_context() streams 40+ canonical events from every layer, no external broker. TelemetryHook for OpenTelemetry/OTLP.
🧠 Reasoning	reflexion=True · grounding=True · CausalChain · GSAR typed grounding layer (arXiv:2604.23366).
🛡 Idempotent tools	@tool(idempotent=True) — dedupes on (name, args). The model can't double-charge, double-book, or double-page.
💾 Durable memory	9 backends — OCI Object Storage, PostgreSQL, Redis, SQLite, Oracle 26ai, OpenSearch, in-memory, file, HTTP.
🔎 RAG	7 vector stores · OCI Cohere + OpenAI embeddings · multimodal (PDF, image OCR, audio).
📡 Streaming + Server	Typed events · SSE · AgentServer (FastAPI, per-principal thread isolation).
🪝 Hooks	Logging · OpenTelemetry · ModelRetry · Guardrails · Steering (LLM-as-judge).
🪙 MCP	MCPClient consumes MCP servers. LocusMCPServer exposes locus tools as MCP.
🌐 Multi-modal	Agent(web_search=…, web_fetch=…, image_generator=…, speech_provider=…) auto-registers tools.
📊 Evaluation	EvalCase / EvalRunner / EvalReport regression suites.
🧰 Models	OCI GenAI (90+ models, V1 + SDK) · OpenAI · Anthropic · Ollama.

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The agent loop

Every locus agent runs the same four-node loop — Think → Execute → Reflect → Terminate — with one immutable state flowing through.

• Think — model decides the next action or final answer.
• Execute — runs tool calls in parallel; @tool(idempotent=True) dedupes on (name, args).
• Reflect — Reflexion, Grounding, Causal on cadence or on error.
• Terminate? — typed stop conditions: MaxIterations(10) | ToolCalled("submit") & ConfidenceMet(0.9).

Every node emits a write-protected typed event — same stream powers SSE, telemetry hooks, and your own async for event in agent.run(…) consumer.

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56 tutorials

examples/ has 56 progressive tutorials, each a single runnable file. Every tutorial runs offline with MockModel; set one env var to upgrade to a real provider.

git clone https://github.com/oracle-samples/locus.git
cd locus && pip install -e .

python examples/tutorial_01_basic_agent.py          # start here
python examples/tutorial_02_agent_with_tools.py     # add tools
python examples/tutorial_41_deepagent.py            # deep research
python examples/tutorial_51_cognitive_router.py     # routing
python examples/tutorial_56_research_workflow.py    # full research pipeline

Track	What you learn
Foundations (01–05, 21, 27, 28, 37)	Agent, tools, memory, streaming, hooks, server, termination
Graphs (06–10, 25, 35, 36)	StateGraph, conditional routing, reducers, HITL, composition
Multi-agent (11, 16–18, 34, 41–45)	Swarm, handoff, orchestrator, A2A, DeepAgent, real-world crews
Reasoning (13, 14, 39)	Structured output, reflexion + grounding, GSAR typed grounding
RAG (22–24)	Basics, providers, RAG agents
Skills, playbooks, plugins (12, 15, 31–33)	MCP, playbooks, plugins, steering
Production (19, 20, 26, 29, 30, 38, 40)	Guardrails, checkpoints, evaluation, model providers, DAC
Real-world workflows (46–50)	Incident response, procurement, contract review, audio
Cognitive router + observability (51–56)	Routing, EventBus, agent yield bridge, event catalogue, research

→ Full tutorials index

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Workbench

Try every tutorial in your browser. Bring your own model key — no install required.

# or open in GitHub Codespaces (badge above)
cd workbench && docker-compose up

Three model slots (A / B / C) so multi-agent tutorials can mix a fast triage model with a deeper specialist. Tutorials tab, Skills tab, Protocols tab (shows all eight cognitive router protocols with cost + latency metadata).

→ Workbench guide

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Deploy

pip install "locus-sdk[oci,server]"

AgentServer is a drop-in FastAPI app: POST /invoke, POST /stream, GET/DELETE /threads/{id}, GET /health.

from locus.server import AgentServer

server = AgentServer(agent=my_agent, api_key=os.environ["API_KEY"])
server.run(host="0.0.0.0", port=8080)

The repo ships a multi-stage Dockerfile and a Helm chart at deploy/helm/locus-agent/ — Deployment, HPA, Ingress, OCI workload-identity hooks.

→ Deploy guide

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Repo layout

src/locus/
├── agent/          Agent runtime, config, SequentialPipeline / ParallelPipeline / LoopAgent
├── core/           AgentState, Message, events, termination algebra, Send
├── loop/           ReAct nodes (Think, Execute, Reflect)
├── router/         Cognitive router — GoalFrame, ProtocolRegistry, PolicyGate, CognitiveCompiler
├── deepagent/      create_deepagent + create_research_workflow + 6 node primitives
├── observability/  EventBus, run_context, agent yield bridge, EV_* constants
├── memory/         BaseCheckpointer + 9 backends
├── models/         Provider registry + OCI, OpenAI, Anthropic, Ollama
├── multiagent/     Orchestrator, Swarm, Handoff, StateGraph, Functional
├── a2a/            Cross-process Agent-to-Agent protocol
├── reasoning/      Reflexion, Grounding, Causal, GSAR
├── rag/            Embeddings + 7 vector stores + retrievers
├── providers/      Multi-modal: web search, web fetch, image, speech
├── tools/          @tool decorator, registry, builtins, executors
├── hooks/          Logging, telemetry, retry, guardrails, steering
├── skills/         AgentSkills.io filesystem-first capability disclosure
├── playbooks/      Declarative step plans + PlaybookEnforcer
├── server/         FastAPI AgentServer with thread persistence
├── evaluation/     EvalCase + EvalRunner + EvalReport
└── integrations/   MCP (client + server)

workbench/          Browser playground — Tutorials / Skills / Protocols tabs,
                    three model slots, SSE event stream, Codespaces-ready.
examples/           56 progressive tutorials, each a single runnable file.
tests/unit/         Deterministic, no external deps. Runs in CI on every PR.
tests/integration/  Live OCI / OpenAI / Oracle Database 26ai. Gated on credentials.

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Contributing

git clone https://github.com/oracle-samples/locus.git
cd locus && pip install -e ".[dev,all]"
hatch run check        # ruff + mypy
hatch run test         # unit tests across Python 3.11–3.14
pre-commit install

See CONTRIBUTING.md. Every PR runs format, lint, mypy, unit tests, DCO sign-off.

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Citing GSAR

@article{kamelhar2026gsar,
  title   = {GSAR: Typed Grounding for Hallucination Detection and Recovery in Multi-Agent LLMs},
  author  = {Kamelhar, Federico A.},
  journal = {arXiv preprint arXiv:2604.23366},
  year    = {2026},
}

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Security

Please consult the security guide for our responsible security vulnerability disclosure process.

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License

Copyright (c) 2026 Oracle and/or its affiliates.

Released under the Universal Permissive License v1.0 as shown at https://oss.oracle.com/licenses/upl/.