sdk-python
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.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
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.
⚡ Reliability
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.
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)
Interface
Authentication
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
Pricing for the SDK itself is not described in the provided README/manifest; costs depend on the selected model provider usage.
Agent Metadata
Known 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.
Alternatives
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Scores are editorial opinions as of 2026-03-29.