Swarm vs Claude Agent SDK

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.

Unique: Uses Python function return values as the handoff mechanism (isinstance(result.value, Agent) check in core.py line 276) rather than explicit routing tables or configuration, making agent transitions first-class language constructs that are testable and debuggable as normal Python code.

vs alternatives: Simpler and more testable than Assistants API for multi-agent flows because state stays client-side and handoffs are explicit function returns, not opaque server-side thread transfers.

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.

Unique: Detects context_variables requirement via inspect.signature() and automatically injects the dict into function calls without requiring explicit parameter declaration in the tool schema, reducing boilerplate while maintaining type safety through Python's native function signatures.

vs alternatives: More Pythonic than manual schema definition (vs LangChain's @tool decorator approach) because it leverages native Python introspection; less verbose than Anthropic's tool_use pattern which requires explicit parameter mapping.

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.

Unique: REPL is built into the Swarm repository as a demo loop, not a separate tool; it uses the same Swarm.run() API as production code, ensuring that interactive behavior matches programmatic behavior.

vs alternatives: More integrated than external chat interfaces (vs Gradio or Streamlit) because it's part of the framework; simpler than full IDE integration because it's just a Python loop reading stdin.

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.

Unique: Example is a complete, runnable application (not just code snippets) that demonstrates the full Swarm lifecycle: agent creation, tool definition, handoff logic, and conversation management in a realistic domain.

vs alternatives: More comprehensive than isolated code examples (vs scattered snippets) and more realistic than toy examples because it shows multi-agent routing and tool integration together.

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.

Unique: Instructions are first-class callables in the Agent type definition, allowing instruction logic to be versioned, tested, and swapped as Python functions rather than embedded in prompt strings, enabling programmatic instruction composition and A/B testing.

vs alternatives: More flexible than static system prompts (vs basic LLM APIs) and simpler than full prompt template engines (vs Langchain's PromptTemplate) because it's just Python functions with access to context_variables.

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.

Unique: Uses a lightweight Result type (not a full state machine) to couple return values with context mutations, allowing tools to be pure functions that explicitly declare state changes rather than relying on closures or global state, making execution flow traceable and testable.

vs alternatives: Simpler than LangChain's AgentAction/AgentFinish pattern because Result is just a dataclass, not part of a larger action/observation loop; more explicit than implicit context mutation via function side effects.

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.

Unique: Streaming is optional and transparent to the agent logic; the same run() method handles both streaming and non-streaming by yielding Response objects, allowing callers to choose rendering strategy without agent code changes.

vs alternatives: More integrated than manual streaming wrappers (vs calling OpenAI API directly) because the run loop handles token accumulation and tool call parsing; simpler than LangChain's streaming callbacks because it's just a generator parameter.

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.

Unique: Message history is a simple list of dicts passed by reference, allowing callers to inspect, modify, or persist it directly without API abstractions; tool results are formatted as 'tool' role messages that the LLM natively understands, not wrapped in custom structures.

vs alternatives: More transparent than Assistants API (which hides message history) and simpler than LangChain's BaseMemory because it's just a Python list that callers fully control.

anthropics/claude-agent-sdk-python | DeepWiki Loading... Index your code with Devin DeepWiki DeepWiki anthropics/claude-agent-sdk-python Index your code with Devin Edit Wiki Share Loading... Last indexed: 5 June 2026 ( f83c87 ) Overview Quick Start Installation and Setup Version Information and Changelog Core Concepts Architecture Overview Type System and Message Architecture ClaudeAgentOptions Configuration Reference Bundled CLI Version Management Basic Usage query() Function ClaudeSDKClient Message Types and Content Blocks Transport and Communication Subprocess CLI Transport Control Protocol Message Streaming and Buffering Extension Points Custom Tools (SDK MCP Servers) Permission System and Callbacks Lifecycle Hooks Plugins and External MCP Servers Advanced Features Session Management and Forking SessionStore: Transcript Persistence File Checkpointing and Rewinding Resource Limits and Cost Control Sandbox Settings Model Selection, Thinking, and Output Formats Skills System Distributed Tracing (OpenTelemetry) Examples and Usage Patterns Interactive Streaming Examples Tool Integration Examples Error Handling Patterns Stderr Callback and Agents Examples Development Guide Project Structure Testing Strategy Build and Release Process Code Quality Standards Claude AI Integration in CI Glossary Menu Overview Relevant source files CHANGELOG.md CLAUDE.md

Core Concepts | anthropics/claude-agent-sdk-python | DeepWiki Loading... Index your code with Devin DeepWiki DeepWiki anthropics/claude-agent-sdk-python Index your code with Devin Edit Wiki Share Loading... Last indexed: 5 June 2026 ( f83c87 ) Overview Quick Start Installation and Setup Version Information and Changelog Core Concepts Architecture Overview Type System and Message Architecture ClaudeAgentOptions Configuration Reference Bundled CLI Version Management Basic Usage query() Function ClaudeSDKClient Message Types and Content Blocks Transport and Communication Subprocess CLI Transport Control Protocol Message Streaming and Buffering Extension Points Custom Tools (SDK MCP Servers) Permission System and Callbacks Lifecycle Hooks Plugins and External MCP Servers Advanced Features Session Management and Forking SessionStore: Transcript Persistence File Checkpointing and Rewinding Resource Limits and Cost Control Sandbox Settings Model Selection, Thinking, and Output Formats Skills System Distributed Tracing (OpenTelemetry) Examples and Usage Patterns Interactive Streaming Examples Tool Integration Examples Error Handling Patterns Stderr Callback and Agents Examples Development Guide Project Structure Testing Strategy Build and Release Process Code Quality Standards Claude AI Integration in CI Glossary Menu Core Concepts Relevant source files CHANG

Architecture Overview | anthropics/claude-agent-sdk-python | DeepWiki Loading... Index your code with Devin DeepWiki DeepWiki anthropics/claude-agent-sdk-python Index your code with Devin Edit Wiki Share Loading... Last indexed: 5 June 2026 ( f83c87 ) Overview Quick Start Installation and Setup Version Information and Changelog Core Concepts Architecture Overview Type System and Message Architecture ClaudeAgentOptions Configuration Reference Bundled CLI Version Management Basic Usage query() Function ClaudeSDKClient Message Types and Content Blocks Transport and Communication Subprocess CLI Transport Control Protocol Message Streaming and Buffering Extension Points Custom Tools (SDK MCP Servers) Permission System and Callbacks Lifecycle Hooks Plugins and External MCP Servers Advanced Features Session Management and Forking SessionStore: Transcript Persistence File Checkpointing and Rewinding Resource Limits and Cost Control Sandbox Settings Model Selection, Thinking, and Output Formats Skills System Distributed Tracing (OpenTelemetry) Examples and Usage Patterns Interactive Streaming Examples Tool Integration Examples Error Handling Patterns Stderr Callback and Agents Examples Development Guide Project Structure Testing Strategy Build and Release Process Code Quality Standards Claude AI Integration in CI Glossary Menu Architecture Overview Relevant source

anthropics/claude-agent-sdk-python | DeepWiki Loading... Index your code with Devin DeepWiki DeepWiki anthropics/claude-agent-sdk-python Index your code with Devin Edit Wiki Share Loading... Last indexed: 5 June 2026 ( f83c87 ) Overview Quick Start Installation and Setup Version Information and Changelog Core Concepts Architecture Overview Type System and Message Architecture ClaudeAgentOptions Configuration Reference Bundled CLI Version Management Basic Usage query() Function ClaudeSDKClient Message Types and Content Blocks Transport and Communication Subprocess CLI Transport Control Protocol Message Streaming and Buffering Extension Points Custom Tools (SDK MCP Servers) Permission System and Callbacks Lifecycle Hooks Plugins and External MCP Servers Advanced Features Session Management and Forking SessionStore: Transcript Persistence File Checkpointing and Rewinding Resource Limits and Cost Control Sandbox Settings Model Selection, Thinking, and Output Formats Skills System Distributed Tracing (OpenTelemetry) Examples and Usage Patterns Interactive Streaming Examples Tool Integration Examp