tsunagi 0.1.1

SDK-first orchestration framework for AI pipelines and agents

Tsunagi

SDK-first orchestration framework for AI pipelines and agents. Tsunagi keeps SDK calls as plain user code while adding composition, retry, timeout, and tracing.

Installation

Install as a standard library (from PyPI or a local checkout):

pip install tsunagi
# or include optional extras, e.g. tracing
pip install "tsunagi[otel]"

Using uv for dependency management:

uv add tsunagi
uv add "tsunagi[otel]"

For local development with extras:

pip install -e .[dev]
uv pip install -e .[dev]

Quick Start

from tsunagi import Pipeline, step


@step
async def add_one(x: int) -> int:
    return x + 1


@step
async def double(x: int) -> int:
    return x * 2


pipe = Pipeline("basic")
result = await pipe.run(add_one >> double, input=3)
print(result)  # 8

Design Philosophy

• SDK-first: You call OpenAI/Anthropic/Qdrant directly. Tsunagi never wraps SDKs.
• Opt-in extras: Tracing, retry, and timeouts are explicit. No hidden behavior.
• Zero runtime deps: Core relies only on Python standard library.
• Small pieces: Decorators and adapters stay short and readable (<400 lines).

API Highlights

• @step: Wrap async functions for pipeline use; direct calls stay untouched.
• Pipeline: Run steps sequentially or in parallel with tracing and context.
• RetryConfig: Per-step retry/backoff configuration.
• Tracer: Protocol for observability; includes NullTracer and StdoutTracer.
• @tool and Agent: Register async tools and drive LLM tool-use loops via adapters.

Comparing with LangChain

• Tsunagi keeps SDK calls unwrapped; LangChain introduces wrappers and abstractions.
• No built-in chunking, parsing, or vector store layers — your code, your logic.
• Minimal surface area: a handful of decorators and classes instead of large class hierarchies.

Examples

See examples/ for pipelines, agents with tools, custom tracers, and RAG sketches. API-key dependent examples are annotated accordingly.