[feat][round-robin-rewrite][replace shuffle with true round robin][feat][turn-awareness][add prev next speaker context][improvement][verbose-logging][gate logs behind verbose flag][improvement][remove-tenacity][drop retry decorator from round robin][improvement][drop-callback][delete unused callback hook][improvement][prompt-extract][move prompts to module functions][improvement][init-validation][consolidate agents check in init][feat][rr-examples][add round robin examples folder][feat][investment-committee][add etf committee example][feat][tumor-board][add medical treatment review example][feat][design-review][add engineering design review example][improvement][unify-imports][use top level swarms import in examples][improvement][version-bump][bump version to 12 0 2]

Author: kyegomez

commit_message.txt

@@ -0,0 +1 @@
+[feat][round-robin-rewrite][replace shuffle with true round robin][feat][turn-awareness][add prev next speaker context][improvement][verbose-logging][gate logs behind verbose flag][improvement][remove-tenacity][drop retry decorator from round robin][improvement][drop-callback][delete unused callback hook][improvement][prompt-extract][move prompts to module functions][improvement][init-validation][consolidate agents check in init][feat][rr-examples][add round robin examples folder][feat][investment-committee][add etf committee example][feat][tumor-board][add medical treatment review example][feat][design-review][add engineering design review example][improvement][unify-imports][use top level swarms import in examples][improvement][version-bump][bump version to 12 0 2]

examples/multi_agent/round_robin_examples/engineering_design_review.py

@@ -0,0 +1,81 @@
+"""
+Engineering design review: four engineers rotate through a proposed system
+design and refine it across two passes.
+
+The architect opens with the proposal, the SRE attacks operability, the
+security engineer surfaces threat model gaps, and the staff engineer
+adjudicates trade-offs. Loop 2 lets each role respond to the others'
+critiques.
+"""
+
+from swarms import Agent, RoundRobinSwarm
+
+architect = Agent(
+    agent_name="Architect",
+    agent_description="Proposes and defends the system design.",
+    system_prompt=(
+        "You are the proposing architect. State the design crisply: "
+        "components, data flow, key APIs, and the failure modes you "
+        "are willing to accept. In loop 2, revise based on the critique "
+        "you received."
+    ),
+    model_name="gpt-4.1",
+    max_loops=1,
+)
+
+sre = Agent(
+    agent_name="SRE",
+    agent_description="Site reliability engineer.",
+    system_prompt=(
+        "You are an SRE. Stress-test the proposal on operability: SLOs, "
+        "blast radius of failures, rollback story, observability gaps, "
+        "and on-call burden. Give specific, actionable pushback."
+    ),
+    model_name="gpt-4.1",
+    max_loops=1,
+)
+
+security = Agent(
+    agent_name="SecurityEngineer",
+    agent_description="Application security engineer.",
+    system_prompt=(
+        "You are a security engineer. Walk the threat model: auth, "
+        "authz, data exposure, supply chain, and trust boundaries. "
+        "Name at least one concrete attack the prior speakers missed."
+    ),
+    model_name="gpt-4.1",
+    max_loops=1,
+)
+
+staff_engineer = Agent(
+    agent_name="StaffEngineer",
+    agent_description="Senior IC arbitrating trade-offs.",
+    system_prompt=(
+        "You are a staff engineer. Adjudicate: which critiques are "
+        "blocking, which are nice-to-have, and what is the smallest "
+        "change to the design that addresses the blocking ones. End "
+        "your turn with a clear ship / iterate / reject verdict."
+    ),
+    model_name="gpt-4.1",
+    max_loops=1,
+)
+
+review = RoundRobinSwarm(
+    name="System-Design-Review",
+    agents=[architect, sre, security, staff_engineer],
+    max_loops=2,
+    output_type="all",
+    verbose=True,
+)
+
+proposal = (
+    "Proposal: replace our monolithic order-processing service with an "
+    "event-driven architecture. Orders are published to a Kafka topic, "
+    "consumed by three independent services (inventory, payments, "
+    "fulfilment), each with their own Postgres database. A new GraphQL "
+    "gateway aggregates state for the UI. Target: 10x throughput, "
+    "independent deploys per service. We have 6 engineers and 8 weeks."
+)
+
+result = review.run(proposal)
+print(result)

examples/multi_agent/round_robin_examples/investment_committee_etf.py

@@ -0,0 +1,77 @@
+"""
+Investment-committee roundtable on whether to launch a thematic AI ETF.
+
+Four committee members rotate in fixed order across two full loops, so each
+member gets two turns. Each turn, the speaker sees the full transcript so
+far plus who spoke before them and who is up next, which is what makes the
+round-robin (rather than parallel) structure useful here: later speakers
+can directly respond to the prior speaker's case.
+"""
+
+from swarms import Agent, RoundRobinSwarm
+
+cio = Agent(
+    agent_name="CIO",
+    agent_description="Chief Investment Officer.",
+    system_prompt=(
+        "You are the Chief Investment Officer. Lead with the strategic "
+        "case: market fit, expected AUM, and how the product slots into "
+        "the existing lineup. Be concrete about target investors."
+    ),
+    model_name="gpt-4.1",
+    max_loops=1,
+)
+
+risk_officer = Agent(
+    agent_name="RiskOfficer",
+    agent_description="Head of risk management.",
+    system_prompt=(
+        "You are the Head of Risk. Stress-test the prior speaker's "
+        "argument: concentration risk, drawdown scenarios in an AI "
+        "selloff, tracking error, liquidity of the underlying basket. "
+        "Quantify wherever possible."
+    ),
+    model_name="gpt-4.1",
+    max_loops=1,
+)
+
+allocation_strategist = Agent(
+    agent_name="AllocationStrategist",
+    agent_description="Portfolio construction lead.",
+    system_prompt=(
+        "You are the Allocation Strategist. Propose the index "
+        "methodology: weighting scheme, rebalance cadence, inclusion "
+        "criteria, and how it differs from existing thematic ETFs in "
+        "the market."
+    ),
+    model_name="gpt-4.1",
+    max_loops=1,
+)
+
+compliance = Agent(
+    agent_name="Compliance",
+    agent_description="Compliance and regulatory counsel.",
+    system_prompt=(
+        "You are Compliance. Flag any regulatory, disclosure, or "
+        "naming-rule concerns (e.g. SEC Names Rule 35d-1). State what "
+        "must be true for the product to be approved."
+    ),
+    model_name="gpt-4.1",
+    max_loops=1,
+)
+
+committee = RoundRobinSwarm(
+    name="AI-ETF-Investment-Committee",
+    agents=[cio, risk_officer, allocation_strategist, compliance],
+    max_loops=2,
+    output_type="all",
+    verbose=True,
+)
+
+result = committee.run(
+    "Should we launch a new thematic ETF targeting the AI infrastructure "
+    "supply chain (chips, datacenter REITs, power/cooling, networking)? "
+    "Reach a recommendation."
+)
+
+print(result)

examples/multi_agent/round_robin_examples/medical_treatment_review.py

@@ -0,0 +1,68 @@
+"""
+Tumor-board style roundtable: three specialists deliberate on a treatment
+plan for a single patient case.
+
+Each specialist contributes their perspective in fixed order, then the cycle
+repeats so each gets to react to what the others said. This is a
+discussion-aid demo only — not medical advice.
+"""
+
+from swarms import Agent, RoundRobinSwarm
+
+oncologist = Agent(
+    agent_name="MedicalOncologist",
+    agent_description="Medical oncology lead.",
+    system_prompt=(
+        "You are a medical oncologist. Frame the systemic-therapy "
+        "options (chemo, targeted, immunotherapy), expected response "
+        "rates for the staging given, and contraindications. Be precise "
+        "about which regimens you would consider first-line."
+    ),
+    model_name="gpt-4.1",
+    max_loops=1,
+)
+
+surgeon = Agent(
+    agent_name="SurgicalOncologist",
+    agent_description="Surgical oncology specialist.",
+    system_prompt=(
+        "You are a surgical oncologist. Evaluate whether the case is "
+        "resectable, the morbidity of the surgical option, and how "
+        "surgery sequences with the systemic plan the prior speaker "
+        "proposed (neoadjuvant vs adjuvant)."
+    ),
+    model_name="gpt-4.1",
+    max_loops=1,
+)
+
+palliative = Agent(
+    agent_name="PalliativeCare",
+    agent_description="Palliative care and quality-of-life lead.",
+    system_prompt=(
+        "You are the palliative care lead. Center the patient's "
+        "goals-of-care, performance status, and symptom burden. Push "
+        "back if the proposed plan trades quality of life for marginal "
+        "survival benefit. Suggest concrete symptom-management additions."
+    ),
+    model_name="gpt-4.1",
+    max_loops=1,
+)
+
+tumor_board = RoundRobinSwarm(
+    name="Tumor-Board-Roundtable",
+    agents=[oncologist, surgeon, palliative],
+    max_loops=2,
+    output_type="all",
+    verbose=True,
+)
+
+case = (
+    "Case: 68-year-old, ECOG 1, newly diagnosed stage IIIA non-small-cell "
+    "lung adenocarcinoma, EGFR exon 19 deletion. Tumor 4.5 cm, single "
+    "ipsilateral mediastinal node positive. No comorbidities of note. "
+    "Patient prioritizes time at home with family. "
+    "Question: recommend a treatment sequence and explain the trade-offs."
+)
+
+result = tumor_board.run(case)
+print(result)

examples/multi_agent/round_robin_examples/round_robin_example.py

@@ -0,0 +1,54 @@
+from swarms import Agent, RoundRobinSwarm
+
+# Three specialists. RoundRobinSwarm will visit them in this exact order
+# every loop: Optimist -> Skeptic -> Synthesiser -> Optimist -> ...
+# Each agent reads the full transcript so far and knows who spoke before
+# it and who is up next (injected into the prompt automatically).
+
+optimist = Agent(
+    agent_name="Optimist",
+    agent_description="Argues for the upside of the proposal.",
+    system_prompt=(
+        "You are an optimist. Your job is to identify the strongest "
+        "reasons the proposal under discussion will succeed. Be specific."
+    ),
+    model_name="gpt-4.1",
+    max_loops=1,
+)
+
+skeptic = Agent(
+    agent_name="Skeptic",
+    agent_description="Stress-tests claims and surfaces risks.",
+    system_prompt=(
+        "You are a skeptic. Your job is to challenge the previous "
+        "speaker's strongest claim with a concrete failure mode or "
+        "missing assumption."
+    ),
+    model_name="gpt-4.1",
+    max_loops=1,
+)
+
+synthesiser = Agent(
+    agent_name="Synthesiser",
+    agent_description="Distils the discussion into a working position.",
+    system_prompt=(
+        "You are a synthesiser. Reconcile the optimist's case and the "
+        "skeptic's pushback into one clear recommendation."
+    ),
+    model_name="gpt-4.1",
+    max_loops=1,
+)
+
+swarm = RoundRobinSwarm(
+    name="Decision-Roundtable",
+    agents=[optimist, skeptic, synthesiser],
+    max_loops=1,  # two full cycles → 6 turns total
+    output_type="all",
+)
+
+result = swarm.run(
+    "Should a 30-person Series A startup build their own LLM "
+    "fine-tuning pipeline or rent one from a vendor?"
+)
+
+print(result)

pyproject.toml

@@ -5,7 +5,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "swarms"
-version = "12.0.1"
+version = "12.0.2"
 description = "Swarms - TGSC"
 license = "Apache-2.0"
 authors = ["Kye Gomez <kye@swarms.world>"]