Swarms v14

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Overview

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Highlights

• OpenTelemetry tracing: agent and swarm runs emit nested spans. Turn it off with SWARMS_TELEMETRY_ON=false.
• Unified MCP manager: one MCPManager handles connections, tool discovery, headers, timeouts and OAuth.
• AutoAgentBuilder: generate a task-specific roster of agents, then run it in any structure.
• AuctionSwarm: agents bid confidence and cost for a task, and the best bid wins the work.
• Turn-based GroupChat: one speaker per turn, with a recency penalty against monologues.
• HierarchicalSwarm recovery: retry failing workers, reassign their tasks, and override the director without subclassing.
• Faster GraphWorkflow: native rustworkx algorithms, one thread pool per run, and a single-pass compile.
• Honest autonomous loop (14.0.1): real message transcripts, a mutable plan, and five correctness fixes.

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New features

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OpenTelemetry tracing

export SWARMS_TELEMETRY_ON=false

from swarms import Agent
from swarms.telemetry.otel import ContextThreadPoolExecutor, capture_init, trace_run


class MySwarm:
    def __init__(self, agents: list[Agent]):
        self.agents = agents
        capture_init(self)

    @trace_run("MySwarm.run")
    def run(self, task: str):
        with ContextThreadPoolExecutor(max_workers=8) as executor:
            return list(executor.map(lambda agent: agent.run(task), self.agents))

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Unified MCP manager

from swarms import Agent

agent = Agent(
    agent_name="Multi-MCP-Agent",
    model_name="gpt-5.4",
    mcp_urls=["http://localhost:8000/mcp", "http://localhost:8001/mcp"],
    mcp_authorization_token="your-token",
    mcp_headers={"X-Tenant-Id": "acme"},
    mcp_timeout=30,
    max_loops=1,
)

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AutoAgentBuilder

from swarms import AutoAgentBuilder, SequentialWorkflow

task = "Analyze why churn rose last quarter and write a brief for leadership."

configs = AutoAgentBuilder(max_agents=3, return_dict=True).run(task)
agents = AutoAgentBuilder(num_agents=3, agent_kwargs={"max_loops": 1}).run(task)
result = SequentialWorkflow(agents=agents, max_loops=1).run(task)

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AuctionSwarm

from swarms import Agent
from swarms.structs.auction_swarm import AuctionSwarm

specialists = [
    Agent(agent_name=name, model_name="gpt-5.4", max_loops=1)
    for name in ["SQL-Expert", "Python-Expert", "Infra-Expert"]
]

swarm = AuctionSwarm(
    agents=specialists,
    top_k=2,
    scoring=lambda confidence, cost: confidence**2 / max(cost, 0.1),
)
result = swarm.run("Optimize a slow analytical query over a 40M-row table.")

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Turn-based GroupChat

from swarms import Agent, GroupChat

agents = [
    Agent(agent_name="Optimist", system_prompt="Argue the benefits.", model_name="gpt-5.4", max_loops=1),
    Agent(agent_name="Pessimist", system_prompt="Argue the risks.", model_name="gpt-5.4", max_loops=1),
]

chat = GroupChat(agents=agents, max_loops=12, threshold=0.6, recency_penalty=0.3, recency_window=1)
result = chat.run("Should we adopt AI for medical diagnosis?")

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HierarchicalSwarm recovery and director settings

from swarms import Agent, HierarchicalSwarm

workers = [
    Agent(agent_name="DataWorker", model_name="gpt-5.4-mini", max_loops=1),
    Agent(agent_name="WritingWorker", model_name="gpt-5.4-mini", max_loops=1),
]

swarm = HierarchicalSwarm(
    agents=workers,
    director_model_name="claude-sonnet-4-6",
    director_settings={"temperature": 0.2, "max_tokens": 16000},
    planning_enabled=True,
    agent_as_judge=True,
    max_agent_retries=2,
    max_reassignment_attempts=1,
)
result = swarm.run("Produce a competitive analysis of the AI chip market.")

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Pydantic models from a class signature

from swarms import Agent
from swarms.utils.class_to_pydantic import class_init_to_pydantic_model

AgentSchema = class_init_to_pydantic_model(
    Agent, include=("agent_name", "system_prompt", "model_name")
)
spec = AgentSchema(agent_name="Analyst", system_prompt="You analyze.")
agent = Agent(**spec.model_dump())

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ConcurrentWorkflow failure policy and pool sizing

from swarms import Agent, ConcurrentWorkflow

agents = [Agent(agent_name=f"Worker-{i}", model_name="gpt-5.4", max_loops=1) for i in range(5)]
workflow = ConcurrentWorkflow(agents=agents, on_error="store", max_workers=8)
result = workflow.run("List ten use cases for multi-agent AI systems.")

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Smaller additions

• CronJob survives failed executions and retries on the next tick. max_consecutive_errors sets an error budget, and run_many() / stop_many() run several agents on different schedules. See CronJob.
• SequentialWorkflow bounds drift-detection reruns with drift_max_retries (default 3).
• AgentLoader.load_agents_from_csv() replaces the standalone CSV-to-agent module. See AgentLoader.
• Agent.max_tokens now defaults to the model’s own output limit when you leave it unset, falling back to 16,000 for unmapped models.
• MixtureOfAgents workers now receive the task plus the previous layer’s synthesis, not the full transcript.

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Improvements

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Bug fixes

• Agent forwards llm_base_url and llm_api_key to the provider call.
• context_length and max_tokens passed to Agent were silently overwritten. Both now take effect.
• tool_execution_retry actually retries tool_retry_attempts times and surfaces the final failure.
• Each Agent gets its own tools_list_dictionary instead of sharing one list.
• An unmapped model ID falls back to defaults instead of raising at construction.
• Agent.load(), run_batched without imgs, run_concurrent_tasks, and arun positional arguments all work again.
• The autonomous loop no longer hands the loop object to built-in tools, which had broken every file and shell tool.
• Conversation can load the file it just saved.
• SequentialWorkflow.run, PlannerWorkerSwarm.run, parallel AgentRearrange steps and HeavySwarm workers now pass images through.
• planning_enabled no longer strips the director’s SwarmSpec schema.
• Concurrent helpers and MajorityVoting / MixtureOfAgents run_concurrently return results in input order.
• create_agent_map rejects duplicate agent names.
• GraphWorkflow JSON round trips work, and visualize sanitizes the workflow name.
• Pydantic tool schemas are named after the model, and two validators disabled under pydantic.v1 work again.
• Image URL fetches resolve hostnames and block private, loopback and cloud-metadata addresses.
• 14.0.0 pinned mcp below 2.0 so import swarms kept working, and 14.0.1 added the OpenTelemetry packages a clean install needs.
• 14.0.2 fixed MixtureOfAgents failing at construction without an aggregator_agent, because aggegrator_args was never stored.

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Removals and breaking changes

• Agent.persistent_memory now defaults to False. Pass persistent_memory=True to keep MEMORY.md across restarts. See Agent memory.
• swarms.tools.mcp_client_tools was removed. Use MCPManager or the mcp_* parameters on Agent.
• AOP was removed from the top-level exports in 14.0.0. The module itself was deleted in 15.0.0.
• "AutoSwarmBuilder" is no longer a SwarmRouter swarm_type. Use AutoSwarmBuilder directly.
• BaseSwarm and BaseStructure were deleted. Write custom structures as plain classes.
• RoundTableDiscussion was deleted. Eight scripted conversation patterns, such as CouncilMeeting and NegotiationSession, moved to examples/multi_agent/alternate_debates/. SkillOrchestra moved to examples/multi_agent/.
• Ten unused math-sequence swarms (FibonacciSwarm, PrimeSwarm and others) and a dozen dead schema and tool modules were removed. These include create_agent_tool, openai_tool_creator_decorator and swarms_api_schemas.
• Agent lost methods with no callers: run_multiple_images, undo_last, set_system_prompt, enable_autosave / disable_autosave and others. Use run(imgs=[...]) for several images. The unread retry_interval and tokenizer arguments are now ignored.
• The computer-use toolkit added during this cycle was removed again in 14.0.1 as a duplicate.

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Conclusion