{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"swarm","slug":"swarm","name":"Swarm","type":"framework","url":"https://github.com/openai/swarm","page_url":"https://unfragile.ai/swarm","categories":["ai-agents"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"swarm__cap_0","uri":"capability://planning.reasoning.stateless.multi.agent.orchestration.with.handoff.routing","name":"stateless multi-agent orchestration with handoff routing","description":"Implements a lightweight run loop (Swarm.run() in core.py) that coordinates multiple agents by detecting when a tool call returns an Agent object, automatically switching execution context without persisting state to external servers. Unlike the Assistants API, all conversation history and context variables remain client-side, enabling full control over agent transitions and state mutations through Python function returns.","intents":["I want to route conversations between specialized agents based on task type without managing separate API threads","I need to switch agents mid-conversation while preserving the full message history for context","I want to test multi-agent flows locally without relying on server-side thread persistence"],"best_for":["teams building educational multi-agent systems or prototypes","developers who need fine-grained control over agent state and transitions","builders testing agent coordination patterns before production deployment"],"limitations":["No built-in persistence — all state lives in memory; requires manual serialization for durability","Stateless design means no automatic recovery from mid-execution failures; caller must implement retry logic","Single-threaded execution loop; concurrent agent operations require external orchestration","Turn limit enforced in run loop (default behavior) but no built-in timeout management per agent"],"requires":["Python 3.9+","OpenAI API key with access to Chat Completions API","Agent objects must define instructions (string or callable) and optional functions list"],"input_types":["Agent (Python object with instructions, functions, model)","context_variables (dict)","messages (list of dicts with role/content)"],"output_types":["Response object containing final message, context_variables state, and agent reference"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"swarm__cap_1","uri":"capability://tool.use.integration.automatic.python.to.json.schema.function.conversion.with.signature.inspection","name":"automatic python-to-json-schema function conversion with signature inspection","description":"Converts Python functions into OpenAI-compatible JSON schemas via function_to_json() utility (swarm/util.py lines 31-87) using inspect module to extract parameter names, type hints, and docstrings. Automatically detects which functions require context_variables by inspecting function signatures, enabling dynamic injection of shared state without explicit parameter passing in tool definitions.","intents":["I want to register Python functions as agent tools without manually writing JSON schemas","I need context_variables automatically passed to functions that declare them in their signature","I want docstrings to automatically populate parameter descriptions in the schema"],"best_for":["Python developers building agents who want to avoid boilerplate schema definitions","teams that need tight coupling between function implementation and tool availability","prototypers who value rapid iteration over strict schema validation"],"limitations":["Type hint mapping is basic (str→'string', int→'integer', etc.); complex types like Union or generics may not convert correctly","Docstring parsing is simple string extraction; complex NumPy/Google-style docstrings may not parse parameter descriptions accurately","No validation that function signatures match declared parameters; mismatches only caught at runtime","Context_variables injection adds introspection overhead (~5-10ms per function registration) for large function sets"],"requires":["Python 3.9+ (uses inspect.signature)","Functions must have type hints for parameters to generate proper JSON schema types","Docstrings must follow basic format: 'description' or 'description\\n\\nArgs:\\n  param: description'"],"input_types":["Python function (callable with type hints and optional docstring)"],"output_types":["dict with keys: name, description, parameters (JSON schema format)"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"swarm__cap_10","uri":"capability://automation.workflow.repl.based.interactive.agent.testing.and.demonstration","name":"repl-based interactive agent testing and demonstration","description":"Swarm includes a REPL loop (referenced in architectural overview) that allows interactive testing of agents by accepting user input, running agents, and displaying responses in a command-line interface. The REPL maintains conversation history across turns and supports agent switching, enabling rapid exploration of multi-agent behavior without writing test code.","intents":["I want to manually test agent behavior and tool calls in an interactive environment","I need to explore how agents respond to different inputs before committing to automated tests","I want to debug agent behavior by stepping through conversations interactively"],"best_for":["developers prototyping agents and exploring behavior","demos and presentations showing agent capabilities","rapid iteration during agent development before writing formal tests"],"limitations":["REPL is single-threaded; no concurrent agent execution or parallel exploration","No built-in command history or session persistence; closing the REPL loses conversation history","Limited to text input/output; no support for multimodal interactions or file uploads","No profiling or performance metrics; hard to identify bottlenecks in agent execution"],"requires":["Python 3.9+","OpenAI API key","Terminal or command-line environment"],"input_types":["text input from user"],"output_types":["formatted text responses with agent name and message content"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"swarm__cap_11","uri":"capability://planning.reasoning.airline.customer.service.example.with.specialized.agent.routing","name":"airline customer service example with specialized agent routing","description":"Swarm includes a complete airline customer service example (referenced in Examples section) that demonstrates multi-agent patterns: a triage agent routes customers to specialized agents (rebooking, refunds, general support) based on issue type. Each agent has specific instructions and tools, and handoffs are implemented as function returns, showing how to structure real-world multi-agent applications.","intents":["I want to see a complete example of multi-agent routing in a realistic domain","I need to understand how to structure agents with different responsibilities and tools","I want to learn how handoffs work by studying a working implementation"],"best_for":["developers learning Swarm patterns through example code","teams building customer service or support systems","architects designing multi-agent systems and needing reference implementations"],"limitations":["Example is simplified; real customer service systems need persistence, authentication, and external integrations","Tools in the example are mocked (e.g., database lookups return hardcoded data)","No error handling or edge cases; example assumes happy path execution","Example doesn't show how to scale to many agents or handle concurrent requests"],"requires":["Python 3.9+","OpenAI API key","Understanding of Swarm core concepts (Agent, handoffs, tools)"],"input_types":["customer service queries (text)"],"output_types":["responses from specialized agents"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"swarm__cap_2","uri":"capability://text.generation.language.dynamic.instruction.generation.with.callable.based.context.awareness","name":"dynamic instruction generation with callable-based context awareness","description":"Allows Agent instructions to be either static strings or callables that receive context_variables and return instruction strings at runtime (swarm/core.py lines 159-161). This enables instruction content to adapt based on conversation state, user metadata, or external data without re-creating Agent objects, implementing a lightweight form of dynamic prompting.","intents":["I want agent instructions to change based on user context or conversation history without creating new agents","I need to inject dynamic data (user tier, conversation count, external API results) into prompts at runtime","I want to test different instruction variants by swapping callables without code changes"],"best_for":["applications with user-specific or context-dependent agent behavior","teams A/B testing instruction variants at runtime","systems where agent behavior must adapt to external state (user profile, database lookups)"],"limitations":["Callable instructions are invoked on every run() call; no caching, so expensive computations (DB queries, API calls) will block the run loop","No built-in error handling for callables that raise exceptions; failures propagate to caller","Instruction callables must complete synchronously; async callables not supported","No observability into which instruction variant was used; requires manual logging for audit trails"],"requires":["Python 3.9+","If using callable: function signature must accept **context_variables or specific context keys","Callable must return a string (no type checking enforced)"],"input_types":["str (static instruction) or Callable[[dict], str] (dynamic instruction factory)"],"output_types":["str (resolved instruction sent to LLM)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"swarm__cap_3","uri":"capability://tool.use.integration.tool.call.execution.with.result.wrapping.and.context.mutation","name":"tool call execution with result wrapping and context mutation","description":"Executes tool functions returned by the LLM and wraps results in a Result object (swarm/types.py lines 11-15) that can optionally include updated context_variables. The run loop (core.py lines 250-264) detects Result objects and merges context updates back into the shared state dict, enabling functions to mutate agent context without side effects or global state.","intents":["I want tool functions to update shared context that persists across subsequent tool calls","I need to return both a result value and state mutations from a single function call","I want to track state changes across the agent execution without using global variables"],"best_for":["multi-step agent workflows where tools need to accumulate state","applications tracking conversation metadata, user preferences, or intermediate results","teams that need explicit, auditable state mutations rather than side effects"],"limitations":["Result wrapping is optional; plain return values are treated as-is, creating inconsistency if some tools use Result and others don't","Context updates are shallow merged; nested dict mutations in context_variables won't be detected or validated","No conflict resolution if multiple tools try to update the same context key; last write wins","No rollback mechanism if a tool updates context then fails; partial state mutations persist"],"requires":["Python 3.9+","Tool functions must return either a plain value or Result(value=..., context_variables={...})","Context_variables dict must be JSON-serializable if persistence is needed"],"input_types":["function return value (any type) or Result object"],"output_types":["Result object with value and optional context_variables dict, or plain value"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"swarm__cap_4","uri":"capability://automation.workflow.streaming.aware.message.handling.with.token.level.response.iteration","name":"streaming-aware message handling with token-level response iteration","description":"Integrates with OpenAI's streaming API to yield partial responses token-by-token via get_chat_completion() (core.py line 165), allowing callers to display agent responses in real-time. The run loop accumulates streamed tokens into full messages before processing tool calls, maintaining compatibility with the non-streaming execution path while enabling progressive output rendering.","intents":["I want to display agent responses to users as they stream in, not wait for full completion","I need to handle both streaming and non-streaming execution paths with the same agent code","I want to show token-level progress for long-running agent operations"],"best_for":["web applications and chat interfaces requiring real-time response feedback","CLI tools and REPL environments where streaming improves perceived latency","applications with strict latency budgets that benefit from progressive rendering"],"limitations":["Streaming adds complexity to error handling; partial responses may be rendered before tool call failures occur","Tool calls cannot be streamed (they require full LLM response to parse); streaming stops during tool execution","No built-in backpressure handling; fast consumers may overwhelm slow renderers","Streaming state is not persisted; reconnection or resumption requires re-running from last full message"],"requires":["Python 3.9+","OpenAI API key with streaming support enabled","Caller must handle generator protocol (for loop or next() calls) to consume streamed tokens"],"input_types":["stream=True parameter in run() call"],"output_types":["generator yielding Response objects with partial content, or full Response if stream=False"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"swarm__cap_5","uri":"capability://memory.knowledge.agent.aware.message.history.management.with.role.based.filtering","name":"agent-aware message history management with role-based filtering","description":"Maintains a conversation history as a list of dicts with 'role' and 'content' keys, automatically appending user messages and agent responses while filtering out internal tool calls from the LLM's perspective. The run loop (core.py lines 139-229) manages message ordering and ensures tool results are formatted as 'tool' role messages that the LLM can process for subsequent decisions.","intents":["I want to maintain a clean conversation history that includes user, assistant, and tool messages in the correct order","I need to replay conversations or inspect the full execution trace for debugging","I want to pass conversation history to a new agent for context without manual reformatting"],"best_for":["multi-turn conversation applications requiring audit trails","debugging and testing agent behavior across conversation sequences","applications that need to export or persist conversation history"],"limitations":["Message history is in-memory only; no automatic persistence or snapshots","No deduplication or compression; long conversations accumulate full token cost on every API call","Tool call details (function name, arguments) are embedded in assistant messages but not separately indexed","No built-in privacy filtering; sensitive data in messages is not masked or redacted"],"requires":["Python 3.9+","Messages must follow OpenAI Chat Completions format: {role: 'user'|'assistant'|'tool', content: str}"],"input_types":["list of dicts with 'role' and 'content' keys"],"output_types":["list of dicts (same format), updated with new messages from run()"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"swarm__cap_6","uri":"capability://automation.workflow.turn.limited.execution.with.configurable.loop.termination","name":"turn-limited execution with configurable loop termination","description":"Implements a configurable turn limit in the run loop (core.py line 139) that terminates execution after a maximum number of agent-LLM interactions, preventing infinite loops or runaway tool call chains. The limit is enforced before each API call, allowing graceful termination with the current message history intact for inspection.","intents":["I want to prevent agents from getting stuck in infinite tool call loops","I need to set execution budgets (e.g., max 5 turns) for cost control or latency SLAs","I want to detect when an agent has exhausted its turn budget and handle it explicitly"],"best_for":["production systems with strict latency or cost budgets","applications where agent behavior is unpredictable and needs guardrails","testing frameworks that need deterministic execution bounds"],"limitations":["Turn limit is a hard stop; no graceful degradation or fallback if limit is reached mid-task","No distinction between 'productive' and 'unproductive' turns; a turn that only calls tools counts the same as one that produces output","Limit is global per run() call; no per-agent or per-tool-type limits","No built-in metrics or logging when turn limit is reached; requires manual inspection of Response object"],"requires":["Python 3.9+","max_turns parameter (int, default typically 10-20) passed to run() method"],"input_types":["max_turns: int"],"output_types":["Response object with agent and messages; caller must check if execution was terminated due to turn limit"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"swarm__cap_7","uri":"capability://automation.workflow.mock.client.testing.infrastructure.for.deterministic.agent.validation","name":"mock client testing infrastructure for deterministic agent validation","description":"Provides a MockClient class (referenced in Testing section) that replaces the OpenAI API client for unit testing, allowing tests to inject predefined LLM responses without making real API calls. Tests can verify agent behavior, tool call sequences, and state mutations in isolation with full control over LLM outputs.","intents":["I want to unit test agent behavior without hitting the OpenAI API or incurring costs","I need to verify that agents call the correct tools in the correct order for a given scenario","I want to test edge cases and error conditions that are hard to trigger with real LLM responses"],"best_for":["development teams building agents with CI/CD pipelines","rapid prototyping where API costs and latency are blockers","testing frameworks that need deterministic, repeatable agent behavior"],"limitations":["MockClient requires manual specification of LLM responses; doesn't simulate actual LLM reasoning or behavior","Tests using MockClient don't catch issues that only appear with real LLM outputs (e.g., unexpected tool calls)","Mock responses must be updated when agent instructions or tools change; brittleness to refactoring","No built-in support for probabilistic or non-deterministic LLM behavior; all responses are hardcoded"],"requires":["Python 3.9+","Test framework (pytest, unittest, etc.)","MockClient implementation (part of swarm/testing module)"],"input_types":["predefined LLM responses (list of dicts or structured responses)"],"output_types":["deterministic agent execution trace for assertion"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"swarm__cap_8","uri":"capability://tool.use.integration.multi.function.agent.capability.registration.with.optional.filtering","name":"multi-function agent capability registration with optional filtering","description":"Agents can declare a list of functions they have access to (swarm/types.py Agent.functions field), which are converted to JSON schemas and sent to the LLM as available tools. The LLM can then choose to call any of these functions, and Swarm routes the calls to the correct Python function via name matching and argument unpacking.","intents":["I want to define which tools an agent can use without modifying the agent's instructions","I need to create specialized agents with different tool sets for different tasks","I want to dynamically enable/disable tools based on context or permissions"],"best_for":["applications with role-based or permission-based tool access","multi-agent systems where different agents have different capabilities","teams that want to separate tool definitions from agent logic"],"limitations":["No built-in access control; all functions in the list are available to the LLM without permission checks","Function name collisions across agents are not detected; last registered function wins","No tool versioning; if a function signature changes, all agents using it must be updated","Tool filtering is static at agent creation time; no runtime tool enable/disable based on state"],"requires":["Python 3.9+","Functions must be registered in Agent.functions list as callables","Function names must be unique within an agent's function set"],"input_types":["list of Python callables (functions)"],"output_types":["JSON schema representations of functions sent to LLM"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"swarm__cap_9","uri":"capability://planning.reasoning.model.aware.agent.execution.with.per.agent.model.selection","name":"model-aware agent execution with per-agent model selection","description":"Each Agent can specify a model field (e.g., 'gpt-4', 'gpt-3.5-turbo') that determines which OpenAI model is used for that agent's completions. The run loop passes the agent's model to get_chat_completion(), enabling multi-model workflows where different agents use different models based on capability or cost requirements.","intents":["I want to use cheaper models for simple agents and more capable models for complex reasoning","I need to test agent behavior across different model versions without code changes","I want to route requests to different models based on task complexity or user tier"],"best_for":["cost-optimized systems that balance capability and expense","applications testing model compatibility or comparing model outputs","multi-tenant systems with different SLA requirements per user"],"limitations":["Model selection is static per agent; no dynamic model switching based on conversation state","No fallback mechanism if a model is unavailable or deprecated; agent creation fails if model doesn't exist","Tool calling behavior varies across models; agents may work with gpt-4 but fail with gpt-3.5-turbo","No built-in cost tracking or model usage metrics; 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