ninetrix-sdk 0.1.1

Python SDK for Ninetrix — define agent tools in code

ninetrix-sdk

Python SDK for Ninetrix — build, compose, and deploy AI agents in pure Python.

pip install ninetrix-sdk

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What it does

The Ninetrix SDK lets you define agents, tools, workflows, and multi-agent teams entirely in Python. Agents are portable — serialize to YAML, run locally, or deploy to Ninetrix Cloud.

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Core Concepts

Agents

Define an agent with a role, tools, and an LLM provider. Run it synchronously, asynchronously, or as a streaming event source.

Agents support structured output (Pydantic models), token/cost budgets, and human-in-the-loop approval gates.

Tools

The @Tool decorator turns any Python function into an agent tool. The function signature is automatically converted to a JSON Schema — no manual wiring needed.

Group related tools with Toolkit for cleaner organization.

Tool Sources

Agents can use tools from multiple sources, all mixed together:

Source	Description
@Tool functions	Local Python functions
MCP servers	Via MCP Gateway (JSON-RPC)
OpenAPI specs	Any REST API with an OpenAPI 3.x spec
Composio	Composio SDK integrations
Community plugins	pip install ninetrix-source-* (auto-discovered)

The plugin system uses standard Python entry_points — anyone can publish a new tool source.

Workflows

The @Workflow decorator defines sequential pipelines that chain agents together. Workflows support:

• Durable execution — checkpoint every step, resume on crash
• Declarative steps — run_step(name, fn) for clean step definitions
• Fan-out — map(agent, items, concurrency) for parallel execution
• Early exit — terminate(reason) for explicit abort

Teams

Team provides LLM-based dynamic routing across multiple agents. The router agent picks the best specialist for each request — no manual if/else chains.

Ninetrix Context

The Ninetrix factory class sets provider and model defaults once. All agents, teams, and workflows created from the context inherit the configuration.

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Providers

Built-in LLM adapters with a unified interface:

• Anthropic (Claude)
• OpenAI (GPT)
• Google (Gemini)
• LiteLLM (100+ models)
• Fallback chains (try providers in order)

All provider exceptions are wrapped — raw third-party errors never surface.

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Observability

• EventBus — pub-sub event system for all agent lifecycle events
• OpenTelemetry — opt-in tracing (graceful no-op if OTEL SDK not installed)
• Debug mode — pretty-print all events to stderr
• Dashboard telemetry — RunnerReporter posts events for trace visualization
• Token tracking — per-turn and per-run budget monitoring with cost estimates

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Persistence

• InMemoryCheckpointer — for local dev and testing
• PostgresCheckpointer — production-grade with SELECT FOR UPDATE
• Durable workflows automatically resume from the last successful step on crash

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Testing

Built-in testing utilities for deterministic agent tests without real LLM calls:

• MockTool — fake tools with call tracking and assertions
• MockProvider — scripted LLM responses
• AgentSandbox — isolated test harness with full assertion API

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Deployment

Agents can be served, built, and deployed from code:

• agent.serve() — FastAPI HTTP server (/invoke, /stream, /health, /info)
• agent.build() — serialize to YAML and build a Docker image via ninetrix build
• agent.deploy() — push to Ninetrix Cloud

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YAML Serialization

Every agent is serializable from day one:

• agent.to_yaml() — export to agentfile.yaml format
• Agent.from_yaml() — load from YAML back into a live agent

This makes agents portable between code-first and YAML-first workflows.

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Optional Dependencies

Install only what you need:

pip install 'ninetrix-sdk[anthropic]'     # Anthropic provider
pip install 'ninetrix-sdk[openai]'        # OpenAI provider
pip install 'ninetrix-sdk[google]'        # Google provider
pip install 'ninetrix-sdk[providers]'     # All providers
pip install 'ninetrix-sdk[serve]'         # FastAPI server
pip install 'ninetrix-sdk[otel]'          # OpenTelemetry tracing

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Ecosystem

The SDK works with two community registries:

• Tools Hub — community tool registry. Use hub:// URIs in agentfile.yaml to pull tools at build time. Each tool has a TOOL.yaml manifest with dependencies, credentials, and companion skills.
• Skills Hub — community skills library. Skills are prompt-layer playbooks (SKILL.md) injected into your agent's system prompt at build time. They teach agents how to work — no code required.

Tools provide capabilities. Skills teach how to use them. The SDK connects both via the @Tool decorator, ToolSource plugins, and YAML serialization.

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License

Apache 2.0