get-n00dles 0.2.0

Open-source multi-agent AI orchestration. Chain agents, manage state, handle failures — without 800 lines of boilerplate.

n00dles 🍜

Open-source multi-agent AI orchestration. Chain agents, manage state, handle failures — without 800 lines of boilerplate.

from n00dles import agent, pipeline, run

@agent(model="claude-sonnet-4-6")
def researcher(topic: str) -> str:
    """Research the topic. Return 3 key facts."""

@agent(model="claude-sonnet-4-6")
def writer(research: str) -> str:
    """Write a short article from the research."""

content_pipeline = pipeline(researcher >> writer, retry=3)
result = run(content_pipeline, topic="multi-agent orchestration")
print(result.output)

Install

pip install get-n00dles

(the PyPI distribution name is get-n00dles — PyPI rejected the bare n00dles as too visually similar to an existing package; the import is still import n00dles)

Set an API key for whichever provider you're using (n00dles wraps litellm, so any provider litellm supports works — Anthropic, OpenAI, Mistral, Gemini, Ollama, and more):

export ANTHROPIC_API_KEY="sk-ant-..."

Why n00dles

• No magic. Every behavior is traceable from your code to the LLM call in a handful of stack frames. No metaclasses, no hidden registries.
• Reliable by default. Retry with exponential backoff, per-node timeouts, and checkpointed state are built in — not opt-in extras.
• One provider layer. Wraps litellm instead of maintaining five parallel provider SDKs. Switch models with a string, not a rewrite.
• Zero cloud dependency. This package makes no network calls except to your LLM provider. No telemetry, no license checks, no phone-home.

Core concepts

Agents are plain functions decorated with @agent. The docstring becomes the system prompt; the type hints become the I/O contract:

@agent(model="claude-haiku-4-5")
def classify(ticket: str) -> str:
    """Classify the support ticket as billing, technical, or other."""

Pipelines chain agents with >> and wrap the chain with pipeline() to attach retry/timeout policy:

support_flow = pipeline(classify >> respond, retry=3, timeout=60)
result = run(support_flow, ticket="My invoice is wrong")

run() returns a RunResult:

result.output          # the final agent's return value
result.run_id           # unique id — pass to a future resume() call if interrupted
result.duration_ms      # wall-clock time for the whole run
result.total_tokens     # summed across every agent call, including retries
result.agent_traces     # per-agent timing, status, and token usage, in order

State is checkpointed to SQLite after every node by default — no config required. If your agent's return type is a Pydantic model instead of str, n00dles instructs the LLM to respond in JSON and validates the response against your schema, raising AgentOutputError if it doesn't fit.

from n00dles import configure

configure(state_store="sqlite:///my_app.db")   # custom path (default: ./n00dles_state.db)
configure(trace_exporter="otel")               # pip install get-n00dles[otel]

Parallel execution runs agents concurrently with | or parallel(). The next step receives a dict keyed by each member's function name — match your downstream agent's parameter names to those keys and there's no manual merging:

from n00dles import parallel

@agent(model="gpt-4o")
def scrape_news(query: str) -> str: """Scrape latest news."""

@agent(model="gpt-4o")
def scrape_twitter(query: str) -> str: """Pull recent posts."""

@agent(model="claude-sonnet-4-6")
def merge_signals(scrape_news: str, scrape_twitter: str) -> str:
    """Merge and rank both signals."""

intel = pipeline(parallel(scrape_news, scrape_twitter) >> merge_signals, timeout=20)
result = run(intel, query="AI regulation 2026")

parallel(*agents, max_concurrency=None) caps how many run at once; without it, every member runs simultaneously.

Conditional routing sends execution to exactly one agent with branch(), based on a key read off the previous step's output — the string itself if it's plain str, or the category field if it's a dict or Pydantic model:

from n00dles import branch

triage = pipeline(classify >> branch(billing=handle_billing, support=handle_support, default=handle_support))
result = run(triage, ticket="My invoice is wrong")

An unmatched key with no default raises BranchError.

What's in this release (v0.2.0)

• @agent, pipeline(), >>, run()/arun()
• parallel() / branch() / the | operator for fan-out and conditional routing
• litellm provider integration (every major LLM provider)
• Retry with exponential backoff + jitter, per-node timeouts, fallback agents
• SQLite state store (default) with checkpoint-and-resume (including partial-resume for parallel groups)
• Pydantic I/O validation for structured agent outputs
• Trace events + an optional OpenTelemetry exporter (pip install get-n00dles[otel])

Not yet implemented (tracked for the next release — see PUBLISHING.md for the full rollout plan):

• Circuit breaker
• Redis state backend
• Langfuse and Helicone exporters
• mock_agent() / MockLLM testing utilities
• The noodles CLI (run, serve, deploy)

Development

git clone https://github.com/n00dlehouse/n00dles-py
cd n00dles-py
pip install -e ".[dev]"
pytest
ruff check .

Tests run entirely against an in-repo fake provider — no API keys or network access required.

License

MIT