{"id":"strands-agents-sdk-python","name":"sdk-python","homepage":"https://strandsagents.com","repo_url":"https://github.com/strands-agents/sdk-python","category":"ai-ml","subcategories":[],"tags":["ai-ml","agents","llm","mcp","tooling","python","streaming","observability"],"what_it_does":"A Python SDK for building and running AI agents using a model-driven approach. It provides an agent loop, tool integrations (Python-decorated tools and optional built-in tools), support for multiple model providers (including OpenAI, Anthropic, Gemini, Bedrock, and others), streaming (including experimental bidirectional streaming), and native Model Context Protocol (MCP) client support for connecting to MCP servers.","use_cases":["Building conversational or autonomous AI agents in Python","Creating tool-using agents via Python decorators","Integrating external capabilities through MCP tool servers","Routing between multiple LLM providers (model-agnostic agent implementation)","Streaming agent outputs for responsive experiences","Local or production deployment of agent workflows with observability hooks (OpenTelemetry dependency present)","Real-time voice/text agent interactions using the experimental bidi feature"],"not_for":["High-security environments that require strict, documented guarantees about data handling by the SDK itself (e.g., guaranteed redaction, logging controls, or formal security review artifacts)","Teams that need a stable, documented REST/HTTP API surface for the SDK itself (this is a Python library, not a service)","Applications that require clearly specified retry/idempotency semantics for agent actions at the SDK boundary (not evidenced in the provided README/manifest data)"],"best_when":"You want a Python-native framework to orchestrate LLM calls and tool execution, optionally enhanced with MCP-connected tool servers and provider-specific model adapters.","avoid_when":"You need a turnkey hosted API (REST/GraphQL/SDK-as-a-service) rather than a library, or you require formally documented behaviors around retries, error contracts, and data privacy controls beyond standard provider policies.","alternatives":["LangChain / LangGraph (agent orchestration and tool integrations)","LlamaIndex (agent/tool workflows)","Microsoft Semantic Kernel (agent planning/execution with connectors)","OpenAI function calling / Assistants (provider-specific agent building)","Direct MCP clients with custom agent orchestration (if you only need MCP connectivity)"],"af_score":54.2,"security_score":53.2,"reliability_score":31.2,"package_type":"skill","discovery_source":["openclaw"],"priority":"high","status":"evaluated","version_evaluated":null,"last_evaluated":"2026-03-29T14:58:25.683717+00:00","interface":{"has_rest_api":false,"has_graphql":false,"has_grpc":false,"has_mcp_server":true,"mcp_server_url":null,"has_sdk":true,"sdk_languages":["python"],"openapi_spec_url":null,"webhooks":false},"auth":{"methods":["API keys / credentials configured per model provider (e.g., AWS credentials for Bedrock, API keys for Gemini/OpenAI)"],"oauth":false,"scopes":false,"notes":"The README indicates provider-specific authentication (e.g., AWS credentials for Bedrock; API key in client_args for Gemini; other providers likely use their own credential mechanisms). No SDK-level OAuth or scope model is described in the provided content."},"pricing":{"model":null,"free_tier_exists":false,"free_tier_limits":null,"paid_tiers":[],"requires_credit_card":false,"estimated_workload_costs":null,"notes":"Pricing for the SDK itself is not described in the provided README/manifest; costs depend on the selected model provider usage."},"requirements":{"requires_signup":false,"requires_credit_card":false,"domain_verification":false,"data_residency":[],"compliance":[],"min_contract":null},"agent_readiness":{"af_score":54.2,"security_score":53.2,"reliability_score":31.2,"mcp_server_quality":55.0,"documentation_accuracy":70.0,"error_message_quality":0.0,"error_message_notes":null,"auth_complexity":60.0,"rate_limit_clarity":10.0,"tls_enforcement":70.0,"auth_strength":55.0,"scope_granularity":20.0,"dependency_hygiene":70.0,"secret_handling":55.0,"security_notes":"As a client-side SDK, it relies on TLS to communicate with upstream providers (not explicitly documented here). Auth is delegated to provider credentials (AWS/API keys) without an SDK-level scope/permission model. Dependency list includes security/ML-adjacent packages; no vulnerability status is provided in the supplied data. No explicit guidance is shown regarding secret redaction/logging, secure storage, or data retention; users should ensure provider configuration and application logging do not leak sensitive inputs/prompts.","uptime_documented":0.0,"version_stability":55.0,"breaking_changes_history":40.0,"error_recovery":30.0,"idempotency_support":"false","idempotency_notes":null,"pagination_style":"none","retry_guidance_documented":false,"known_agent_gotchas":["The bidirectional streaming feature is explicitly labeled experimental and APIs may change.","Authentication and behavior depend on the selected model provider adapter; misconfiguration (e.g., AWS credentials/region/model access) can prevent successful calls.","MCP tool integration implies tool server process/runtime setup that can be fragile if the MCP server command/runtime is not controlled."]}}