OpenAI Swarm Multi-Agent Orchestration: The Complete Engineering Guide

In the demo, your agents coordinate. In production, they stumble over handoffs, lose context, and contradict each other.

The study from the University of California, Berkeley (UC Berkeley), published at NeurIPS 2025, annotated 1,642 execution traces across seven multi-agent frameworks and found overall failure rates between 41% and 86.7%. Most failures involved design and coordination rather than raw model errors: 41.8% traced to system design issues and 36.9% to inter-agent misalignment.

OpenAI built Swarm to address these coordination problems by keeping autonomous agents lightweight, stateless, and bound by explicit handoff functions. OpenAI has since replaced it with the OpenAI Agents SDK. The Swarm README calls the SDK "a production-ready evolution of Swarm," and the core primitives carried over almost intact.

What is the OpenAI Swarm framework?

The OpenAI Swarm framework is a lightweight, open-source framework for building and orchestrating multi-agent AI systems. Three components coordinate focused language-model workflows without heavy infrastructure: autonomous agents, handoffs, and routines.

The repository retains its "experimental, educational" label. Its README directs production use toward the Agents SDK, which adds durable execution and a native sandbox.

Understanding core architecture and specialization patterns

Swarm strips orchestration down to direct function calls instead of complex memory systems. Each autonomous agent combines instructions with a set of tools, and that narrow scope stays small enough to test independently. Sequential chains work like assembly lines, while conditional handoffs behave more like decision trees.

Handoffs occur when a specialist finishes its task or reaches its limits. The Agents SDK preserves this architecture and exposes handoffs as tools: a handoff to Refund Agent becomes transfer_to_refund_agent, and each specialist declares its permitted destinations, as the SDK handoff documentation explains.

Integrating with existing large language model workflows

You can adopt Swarm-style orchestration without replacing your existing large language model (LLM) API calls, vector searches, or database queries. Autonomous agents are regular classes and their tools are normal functions, so business logic stays put.

Keep your current client and route requests through the runner. Split one complex prompt into two specialists, then compare against your single-agent baseline. Instrument the workflow before you add specialists.

Swarm ran on the Chat Completions API; the Agents SDK defaults to the Responses API, which OpenAI recommends for new projects.

How to build your first OpenAI Swarm application

Defining production-agent roles and boundaries

Say you're moderating marketplace listings. A triage specialist checks each listing for risky language and transfers uncertain cases to a review specialist, which recommends approval, rejection, or escalation.

Start by documenting four boundaries:

• The input each production agent accepts
• The decision each production agent owns
• The tools each production agent may call
• The conditions that trigger a handoff

Schemas cut down malformed calls. They can't guarantee sound judgment.

Configuring handoff logic and context

A handoff should transfer only the information the receiving production agent needs. Swarm kept one autonomous agent in charge at a time with no persistent state between calls, so every handoff had to carry sufficient context. By default in the Agents SDK, the receiving specialist sees the whole conversation history, and input filters on HandoffInputData can remove irrelevant or sensitive material.

SDK sessions backed by SQLiteSession, SQLAlchemySession, or RedisSession persist history across runs. RunContextWrapper carries application state that isn't sent to the model. Keep authentication, database handles, and internal identifiers local; put only model-relevant information in conversation history.

Test timeout, malformed-input, and unavailable-tool paths; each route should terminate or escalate rather than loop.

Implementing error handling and agent observability

Wrap each tool call with defensive code and return structured errors rather than raw exceptions.

Swarm included no native tracing, so teams needed external logging from the first run. The Agents SDK now enables tracing by default. It records model generations, tool calls, and handoffs as spans, and each handoff span captures source and destination.

Track these operational layers together:

1. Tool success, parameter validity, and retry counts
2. Handoff source, destination, and transferred context
3. End-to-end latency, cost, and task completion
4. Human escalations and unrecoverable failures

Six critical OpenAI Swarm failures that crash multi-agent systems

Even well-designed multi-agent systems break down in predictable ways—here are the six failure modes you'll encounter most often in production.

Preventing coordination workflows from collapsing

Two autonomous agents can pass every isolated test while their shared workflow fails. The first omits a required identifier, the second proceeds on incomplete context, and your customer gets a broken result with no exception raised.

The Who and When benchmark found that the best automated method identified the responsible specialist only 53.5% of the time, and the failing step only 14.2% of the time. Test golden paths, partial-context paths, and deliberate handoff corruption, and record the source, destination, and payload of every transfer.

Catching incorrect tools and parameters

Your billing specialist selects the correct refund API but supplies an order ID where the tool expects a transaction ID. The call succeeds — but no refund occurs, and dashboards stay green while support tickets pile up.

Tool schemas help with types and required fields. They don't prove that a parameter represents the correct business entity. Evaluate tool behavior at three levels:

• Whether the production agent selected the correct tool
• Whether every parameter was present and semantically correct
• Whether the tool result advanced the requested task

Your alerting should distinguish execution success from action completion. Compare tool traces with known-good examples and add negative tests for similar identifiers, ambiguous dates, and optional fields.

Detecting format and semantic drift

Structured output can pass validation while carrying the wrong meaning. A support specialist may return valid JSON with the wrong account tier or renewal date, and downstream code accepts the object.

Add semantic evals that compare important values with reference answers and business rules. For moderation, test whether the risk label matches the policy rather than checking that the risk_label field exists.

Build your eval set from production-like edge cases:

• Similar categories with different required actions
• Missing context that should trigger clarification
• Conflicting evidence that should trigger escalation
• Valid structures containing deliberately incorrect values

Feed reviewer corrections on false positives and false negatives back into your judging rules. Splunk Agent Observability's Autotune turns those corrections into stronger judging criteria for your LLM-as-a-judge evaluators.

Covering domain-specific edge cases

Generic quality scores overlook the procedural requirements that define success in your domain. Your fintech workflow may require approval before a transfer, and an e-commerce workflow may prohibit refunds after shipment.

AgentPex research evaluated 424 traces and found procedural violations inside 83% of traces with perfect outcome scores. The final answer looked correct, but the route ignored instructions or used tools unsafely.

Define domain-aware checks for both outcome and process. Ask whether the agents followed required steps, respected tool restrictions, and escalated uncertainty.

Restoring visibility in long-running workflows

A multi-hour research workflow finishes, but its recommendation contradicts the source material. Logs show successful API responses, and your on-call engineer still can't identify which handoff introduced the unsupported claim. Context may degrade gradually, retries may inflate latency, and one specialist may reinterpret another's output, none of which raises an exception.

Organize agent observability around sessions, traces, and spans:

• A session groups the complete conversation or evaluation run.
• A trace represents one workflow.
• Spans capture model generations, tool calls, and handoffs.

Keep development, staging, and production traces in separate environments so a debugging run never contaminates your production baseline.

Stopping quality problems from compounding

A weak output from one production agent becomes trusted input for the next. Small factual errors accumulate across research, analysis, drafting, and approval stages while the final response sounds more confident.

The Hallucination Snowball study injected 346 hallucinations into a four-agent financial pipeline. GPT-4o's detection rate fell from 72.0% at stage one to 50.9% at stage four, and another 23.7% reached the final output undetected. Boundary checks reduced hallucination survival from 58.4% to 16.2%.

Evaluate every handoff instead of relying only on a final review. High-risk failures should block, transform, or escalate the workflow, and the same boundary checks contain a malicious instruction accepted early in the chain.

Ship reliable multi-agent systems with Splunk Agent Observability

Splunk's mission is to provide the visibility and insights that keep digital systems secure and reliable. That mission now extends to the agents running your business. Splunk is the intelligence layer for trusted agentic operations.

Splunk Agent Observability traces agent decisions, tool calls, and handoffs:

• Timeline and Graph views: Visualize multi-agent decision paths, tool calls, and handoffs for faster root-cause analysis.
• Signals: Detects recurring and previously unknown failure patterns across production traces.
• Luna evaluation models: Runs production-scale evals at under 200ms latency and up to 96% lower cost than frontier LLM-as-a-judge evaluators.
• Runtime guardrails: Applies runtime policy that blocks, transforms, or routes risky agent outputs before they reach customers.
• Metrics: Measures action completion, tool selection, reasoning coherence, safety, and workflow quality.

ReadThe Agentic Shift: Redefining Observability for the AI Era for Splunk's view on observability in the agentic era.

FAQs about OpenAI Swarm