{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hn-46795584","slug":"sandbox-agent-sdk-unified-api-for-automating-codin","name":"Sandbox Agent SDK – unified API for automating coding agents","type":"framework","url":"https://github.com/rivet-dev/sandbox-agent","page_url":"https://unfragile.ai/sandbox-agent-sdk-unified-api-for-automating-codin","categories":["automation"],"tags":["hackernews","show-hn"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hn-46795584__cap_0","uri":"capability://tool.use.integration.unified.coding.agent.orchestration.across.multiple.llm.providers","name":"unified coding agent orchestration across multiple llm providers","description":"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.","intents":["I want to build an agent that can swap between Claude and GPT-4 without rewriting my orchestration logic","I need a unified interface to test my agent against multiple LLM providers simultaneously","I want to route different tasks to different models based on cost or latency constraints"],"best_for":["teams building multi-model AI agents","developers prototyping agents before committing to a single provider","cost-conscious builders wanting to optimize model selection per task"],"limitations":["Provider-specific features (e.g., vision capabilities, function calling schemas) may require adapter code","Token counting normalization adds ~5-10ms overhead per request","Rate limiting and quota management must be handled per-provider separately"],"requires":["Node.js 16+ or Python 3.8+","API keys for at least one supported LLM provider","Basic understanding of LLM request/response patterns"],"input_types":["text prompts","structured messages with role/content","tool/function definitions in JSON schema format"],"output_types":["text completions","structured JSON responses","tool call specifications"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46795584__cap_1","uri":"capability://automation.workflow.code.execution.sandboxing.with.isolated.runtime.environments","name":"code execution sandboxing with isolated runtime environments","description":"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.","intents":["I want my agent to write and execute Python scripts without risking my production environment","I need to safely run untrusted code generated by an LLM with strict resource limits","I want execution results fed back to the agent so it can debug and fix its own code"],"best_for":["developers building code-generation agents that need to validate output","platforms running user-submitted code in multi-tenant environments","teams implementing autonomous debugging workflows"],"limitations":["Docker/container overhead adds 500ms-2s per execution startup","Network access requires explicit allowlisting; no internet by default","Persistent state across executions requires explicit volume mounting","Debugging sandboxed code is harder than local execution"],"requires":["Docker daemon running (for container-based sandboxing)","Sufficient disk space for container images (~500MB per runtime)","Linux kernel with cgroup support for resource limiting"],"input_types":["code strings (Python, JavaScript, Bash, etc.)","file paths to scripts","environment variables as key-value pairs"],"output_types":["stdout/stderr as text","exit code as integer","structured execution metadata (duration, memory used, etc.)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46795584__cap_10","uri":"capability://safety.moderation.error.handling.and.self.correction.with.retry.strategies","name":"error handling and self-correction with retry strategies","description":"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.","intents":["I want my agent to automatically recover from transient errors without manual intervention","I need my agent to fix its own mistakes (e.g., malformed code, incorrect tool calls)","I want fine-grained control over retry behavior for different error types"],"best_for":["developers building resilient agents for production","teams implementing self-correcting agents","builders needing robust error handling across multiple failure modes"],"limitations":["Retry logic can significantly increase latency for flaky operations","Self-correction may fail if the agent can't understand the error","Max retry limits prevent infinite loops but may abandon valid tasks","Custom error handlers require domain-specific knowledge"],"requires":["Error classification strategy (transient vs permanent)","Retry configuration (max retries, backoff strategy)","Custom error handlers (optional)"],"input_types":["error objects/exceptions","error context (tool call, LLM response, etc.)","retry configuration"],"output_types":["retry decisions (retry, fail, escalate)","recovery actions (corrective prompts, alternative tools)","error metadata (error type, attempt count)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46795584__cap_11","uri":"capability://tool.use.integration.provider.agnostic.model.selection.and.routing","name":"provider-agnostic model selection and routing","description":"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.","intents":["I want to automatically route different tasks to the best model for that task","I need to optimize costs by using cheaper models when appropriate","I want automatic fallback to alternative models if my preferred model is unavailable"],"best_for":["teams running agents with heterogeneous task types","developers optimizing cost-to-performance tradeoffs","builders implementing multi-model agent systems"],"limitations":["Routing decisions add ~10-20ms latency","Model capabilities must be manually defined or inferred","Cost optimization requires accurate pricing data","Fallback chains can increase latency if primary model fails"],"requires":["Model capability definitions (reasoning, code, vision, etc.)","Routing rules (task type → model mapping)","Cost constraints and budget allocation"],"input_types":["task characteristics (type, complexity, requirements)","available models and their capabilities","cost constraints and budget"],"output_types":["selected model for task","routing decision metadata","fallback model chain"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46795584__cap_2","uri":"capability://tool.use.integration.agentic.tool.calling.with.schema.based.function.registry","name":"agentic tool calling with schema-based function registry","description":"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.","intents":["I want to define a set of tools my agent can use without manually parsing LLM output","I need my agent to call external APIs (databases, webhooks, file systems) in a structured way","I want automatic validation of tool arguments before execution to prevent runtime errors"],"best_for":["developers building agents that interact with external systems","teams implementing ReAct-style agents with tool use","builders needing provider-agnostic tool calling abstractions"],"limitations":["Schema validation adds ~10-20ms per tool call","Nested/complex schemas may require custom serialization logic","Tool execution errors must be explicitly caught and formatted for agent consumption","No built-in retry logic for failed tool calls"],"requires":["JSON schema knowledge for tool definitions","Async/await support in the runtime (Node.js 12+, Python 3.7+)","Understanding of provider-specific tool calling conventions"],"input_types":["JSON schema objects defining tool parameters","function/method references as tool handlers","LLM-generated tool call specifications"],"output_types":["tool execution results as JSON","error messages formatted for agent consumption","structured tool call metadata"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46795584__cap_3","uri":"capability://memory.knowledge.agent.state.persistence.and.context.management","name":"agent state persistence and context management","description":"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.","intents":["I want my agent to remember previous interactions and build on past reasoning","I need to persist agent state so it survives application restarts","I want to implement long-running agents that operate over days or weeks without losing context"],"best_for":["developers building multi-turn conversational agents","teams implementing persistent autonomous workflows","builders needing to scale agents across distributed systems"],"limitations":["In-memory storage doesn't survive process crashes","Context window management requires tuning summarization thresholds per model","Distributed state consistency requires external coordination (Redis/DB)","No built-in encryption for sensitive state at rest"],"requires":["Storage backend (Redis, PostgreSQL, or in-memory for development)","Serialization format agreement (JSON, MessagePack, etc.)","Understanding of token counting for context window management"],"input_types":["agent messages (role, content, metadata)","execution results from tool calls","user inputs and feedback"],"output_types":["conversation history as structured message arrays","agent state snapshots","context summaries for token optimization"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46795584__cap_4","uri":"capability://planning.reasoning.multi.step.agentic.reasoning.with.loop.control","name":"multi-step agentic reasoning with loop control","description":"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.","intents":["I want my agent to reason through multi-step problems without manual loop management","I need to implement custom reasoning strategies (e.g., tree-of-thought) without rewriting the core loop","I want visibility into the agent's reasoning process for debugging and optimization"],"best_for":["developers building autonomous agents for complex tasks","researchers experimenting with different reasoning strategies","teams implementing agents with strict step/cost budgets"],"limitations":["Each reasoning step adds LLM latency (typically 1-5 seconds per step)","Reasoning traces can grow large for long-running agents (100+ steps)","Custom reasoning strategies require understanding of the SDK's hook system","No built-in optimization for redundant reasoning steps"],"requires":["Understanding of agentic reasoning patterns (ReAct, CoT, etc.)","Configuration of step limits and termination conditions","Monitoring/logging infrastructure for reasoning traces"],"input_types":["initial task/prompt","tool definitions and availability","custom reasoning strategy implementations"],"output_types":["final agent response","reasoning trace (all steps, tool calls, observations)","execution metadata (steps taken, tokens used, duration)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46795584__cap_5","uri":"capability://data.processing.analysis.structured.output.extraction.with.schema.validation","name":"structured output extraction with schema validation","description":"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.","intents":["I want my agent to extract structured data (e.g., parsed code, metadata) from LLM responses reliably","I need guaranteed JSON output from my agent without manual parsing and error handling","I want automatic retry logic when the LLM produces malformed structured output"],"best_for":["developers building data extraction agents","teams needing reliable structured outputs for downstream processing","builders implementing agents that must produce machine-readable results"],"limitations":["Schema validation adds ~20-50ms per response","Retry logic can increase latency significantly if LLM struggles with schema","Complex nested schemas may require custom serialization","Provider-native structured output (OpenAI JSON mode) may have additional costs"],"requires":["JSON schema definition for expected output structure","Understanding of provider-specific structured output capabilities","Error handling for validation failures"],"input_types":["JSON schema objects","LLM responses (text, JSON, etc.)","custom validation rules"],"output_types":["validated JSON objects","structured data matching schema","validation error messages"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46795584__cap_6","uri":"capability://automation.workflow.agent.performance.monitoring.and.cost.tracking","name":"agent performance monitoring and cost tracking","description":"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).","intents":["I want to track how much my agents are costing to run and optimize for cost","I need visibility into agent performance (latency, success rates) for monitoring and debugging","I want to set cost budgets and automatically throttle agents when approaching limits"],"best_for":["teams running agents in production with cost constraints","developers optimizing agent performance and efficiency","builders implementing cost-aware routing and model selection"],"limitations":["Metric collection adds ~5-10ms overhead per step","Cost calculations depend on accurate provider pricing data (may lag)","Real-time cost tracking requires external observability platform","No built-in cost prediction for long-running agents"],"requires":["Observability platform integration (optional but recommended)","Provider pricing data (usually auto-populated from SDK)","Logging/monitoring infrastructure"],"input_types":["agent execution events","LLM API responses with token counts","tool execution metadata"],"output_types":["cost metrics (per-step, per-agent, total)","performance metrics (latency, success rates)","structured telemetry events"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46795584__cap_7","uri":"capability://automation.workflow.agent.testing.and.evaluation.framework","name":"agent testing and evaluation framework","description":"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.","intents":["I want to test my agent against a suite of test cases to ensure it works correctly","I need to evaluate agent performance improvements when changing reasoning strategies or models","I want to catch regressions in agent behavior before deploying to production"],"best_for":["teams implementing agents with quality gates","developers iterating on agent prompts and reasoning strategies","builders needing automated validation before production deployment"],"limitations":["Deterministic testing requires mocked LLM responses (may not reflect real behavior)","Stochastic evaluation requires multiple runs (expensive and slow)","Custom evaluation metrics require domain-specific implementation","Test coverage for edge cases is developer responsibility"],"requires":["Test case definitions (input/expected output pairs)","Evaluation metrics (built-in or custom)","CI/CD integration (optional but recommended)"],"input_types":["test cases (task, expected output)","evaluation metrics (functions or built-in)","agent configurations to test"],"output_types":["test results (pass/fail per case)","evaluation metrics (accuracy, latency, cost)","comparison reports across agent versions"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46795584__cap_8","uri":"capability://planning.reasoning.agent.composition.and.hierarchical.task.decomposition","name":"agent composition and hierarchical task decomposition","description":"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.","intents":["I want to build a complex agent by composing simpler, specialized sub-agents","I need my agent to break down complex tasks into subtasks and delegate to specialized agents","I want to reuse agents across different parent agents without duplication"],"best_for":["teams building complex autonomous systems with multiple specialized agents","developers implementing hierarchical reasoning and task decomposition","builders needing modular, reusable agent components"],"limitations":["Hierarchical composition adds latency due to multiple LLM calls","Coordination between sub-agents requires explicit state passing","Error handling in sub-agents must be explicitly defined","Debugging hierarchical agents is more complex than single-agent systems"],"requires":["Clear task decomposition strategy","Sub-agent definitions and capabilities","Coordination/orchestration logic between agents"],"input_types":["parent task/prompt","sub-agent definitions","task decomposition strategies"],"output_types":["aggregated results from sub-agents","hierarchical reasoning trace","execution metadata (sub-agent calls, latency, costs)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46795584__cap_9","uri":"capability://text.generation.language.dynamic.prompt.engineering.and.few.shot.learning","name":"dynamic prompt engineering and few-shot learning","description":"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.","intents":["I want to improve agent performance by providing relevant few-shot examples without manual prompt engineering","I need to dynamically adjust prompts based on task characteristics or agent performance","I want to reuse successful prompts and examples across different agents"],"best_for":["developers optimizing agent prompts iteratively","teams implementing few-shot learning for improved agent performance","builders needing adaptive prompting strategies"],"limitations":["Example selection adds ~50-100ms latency per prompt","Few-shot examples consume tokens (increases cost)","Prompt optimization requires feedback loops (slow iteration)","No automatic prompt generation; examples must be manually curated"],"requires":["Prompt templates with variable placeholders","Few-shot examples (manually curated or from past executions)","Similarity metrics for example selection"],"input_types":["prompt templates","few-shot examples","task context and variables"],"output_types":["constructed prompts with examples","prompt metadata (example count, token usage)","optimization metrics"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"high","permissions":["Node.js 16+ or Python 3.8+","API keys for at least one supported LLM provider","Basic understanding of LLM request/response patterns","Docker daemon running (for container-based sandboxing)","Sufficient disk space for container images (~500MB per runtime)","Linux kernel with cgroup support for resource limiting","Error classification strategy (transient vs permanent)","Retry configuration (max retries, backoff strategy)","Custom error handlers (optional)","Model capability definitions (reasoning, code, vision, etc.)"],"failure_modes":["Provider-specific features (e.g., vision capabilities, function calling schemas) may require adapter code","Token counting normalization adds ~5-10ms overhead per request","Rate limiting and quota management must be handled per-provider separately","Docker/container overhead adds 500ms-2s per execution startup","Network access requires explicit allowlisting; no internet by default","Persistent state across executions requires explicit volume mounting","Debugging sandboxed code is harder than local execution","Retry logic can significantly increase latency for flaky operations","Self-correction may fail if the agent can't understand the error","Max retry limits prevent infinite loops but may abandon valid tasks","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.46,"quality":0.34,"ecosystem":0.46,"match_graph":0.25,"freshness":0.6,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.691Z","last_scraped_at":"2026-05-04T08:09:59.925Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=sandbox-agent-sdk-unified-api-for-automating-codin","compare_url":"https://unfragile.ai/compare?artifact=sandbox-agent-sdk-unified-api-for-automating-codin"}},"signature":"h5/LYjDosetTkUttYBiFDn1nJ+giZjyCOHLoiHUAx/hZGuGGjdVgpscvv2UQLbttKlYaOa5BVJWar3ATk3TfDA==","signedAt":"2026-06-22T03:57:36.107Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/sandbox-agent-sdk-unified-api-for-automating-codin","artifact":"https://unfragile.ai/sandbox-agent-sdk-unified-api-for-automating-codin","verify":"https://unfragile.ai/api/v1/verify?slug=sandbox-agent-sdk-unified-api-for-automating-codin","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}