Sandbox Agent SDK – unified API for automating coding agents

Provides a provider-agnostic abstraction layer that normalizes interactions with different LLM backends (OpenAI, Anthropic, local models via Ollama, etc.) through a single SDK interface. Internally maps provider-specific request/response formats, token counting, and model capabilities to a canonical schema, eliminating the need for developers to write conditional logic for each provider. Supports dynamic provider switching at runtime based on task requirements or cost optimization.

Provides isolated, containerized execution environments where agents can safely run generated code without risking the host system. Uses Docker or lightweight VM-based sandboxes to execute arbitrary code with configurable resource limits (CPU, memory, timeout), file system isolation, and network access controls. Captures stdout, stderr, and exit codes, returning structured execution results back to the agent for error handling and iteration.

Implements sophisticated error handling for agent failures including tool execution errors, LLM errors, and validation failures. Provides configurable retry strategies (exponential backoff, jitter, max retries) and automatic error recovery mechanisms (e.g., asking the agent to fix its own code, retrying with different prompts). Supports custom error handlers for domain-specific recovery logic.

Implements intelligent model selection and routing based on task characteristics, cost constraints, latency requirements, and model capabilities. Supports dynamic routing rules (e.g., use GPT-4 for complex reasoning, Claude for code generation) and automatic fallback to alternative models if the primary choice fails. Integrates with cost tracking to optimize model selection based on budget constraints.

Implements a declarative function registry where developers define tools as JSON schemas with descriptions, parameters, and return types. The SDK automatically converts these schemas into provider-specific formats (OpenAI function calling, Anthropic tool_use, Claude tool_use_block) and handles the request-response cycle: parsing tool calls from LLM output, validating arguments against schemas, executing registered handlers, and feeding results back to the agent. Supports both synchronous and asynchronous tool handlers with automatic error wrapping.

Provides built-in mechanisms for maintaining agent state across multiple turns, including message history, execution context, and intermediate reasoning steps. Supports pluggable storage backends (in-memory, Redis, PostgreSQL) for persisting conversation history and agent state. Automatically manages context windows by implementing sliding-window or summarization strategies to keep token usage within provider limits while preserving relevant history.

Implements the core agent loop (think-act-observe) with configurable termination conditions, step limits, and reasoning strategies. Supports both synchronous sequential reasoning and asynchronous parallel tool execution. Provides hooks for custom reasoning strategies (e.g., chain-of-thought, tree-of-thought, ReAct) and enables developers to inject custom logic at each step (pre-processing, post-processing, filtering). Automatically tracks reasoning traces for debugging and optimization.

Enables agents to request structured outputs (JSON, YAML, etc.) from LLMs with automatic schema validation and error handling. Uses provider-native structured output APIs (OpenAI's JSON mode, Anthropic's structured output) where available, falling back to prompt engineering and regex-based parsing for other providers. Validates LLM output against the provided schema and automatically retries with corrective prompts if validation fails.

Provides built-in instrumentation for tracking agent execution metrics including token usage, latency, cost, tool call success rates, and reasoning step counts. Integrates with observability platforms (e.g., OpenTelemetry, Datadog, custom webhooks) to export metrics in real-time. Calculates per-step and per-agent costs based on provider pricing models and enables cost-based optimization (e.g., routing to cheaper models, limiting reasoning steps).

Provides utilities for testing agents against predefined test cases, benchmarks, and evaluation metrics. Supports deterministic testing (fixed seeds, mocked LLM responses) for regression testing, as well as stochastic evaluation across multiple runs. Includes built-in metrics (accuracy, latency, cost, tool call success rate) and enables custom evaluation functions. Integrates with CI/CD pipelines for automated agent validation.

Enables building complex agents by composing simpler sub-agents, each responsible for specific tasks or domains. Provides patterns for hierarchical task decomposition where a parent agent breaks down complex problems into sub-tasks, delegates to specialized sub-agents, and aggregates results. Supports both sequential and parallel sub-agent execution with automatic error handling and fallback strategies.

Provides utilities for dynamically constructing prompts with few-shot examples, context injection, and adaptive prompt strategies. Supports prompt templates with variable substitution, automatic example selection based on task similarity, and dynamic prompt optimization based on agent performance. Integrates with memory systems to retrieve relevant examples from past successful executions.