Deadlock-free LLM agent coordination
ZipperGen is a Python DSL and runtime for structured multi-agent LLM coordination. You write a single global protocol describing what messages flow between which agents and who owns each decision. ZipperGen projects it onto each agent and runs them concurrently — with deadlock-freedom guaranteed by construction.
Quick start
git clone https://github.com/zippergen-io/zippergen.git
cd zippergen
pip install -e .Python 3.11 or later required.
Hello, World!
Three agents collaborate: Writer drafts a tweet, Editor decides
whether it's good enough, and ZipperGen handles the coordination. No API key needed — the
built-in mock backend runs instantly.
from zippergen.syntax import Lifeline, Var
from zippergen.actions import llm
from zippergen.builder import workflow
User = Lifeline("User")
Writer = Lifeline("Writer")
Editor = Lifeline("Editor")
topic = Var("topic", str)
tweet = Var("tweet", str)
approved = Var("approved", bool)
@llm(system="Write a one-sentence tweet about the topic.",
user="{topic}", parse="text", outputs=(("tweet", str),))
def draft(topic: str) -> None: ...
@llm(system="Is this tweet engaging, original, and under 180 chars? Reply true or false.",
user="{tweet}", parse="bool", outputs=(("approved", bool),))
def approve(tweet: str) -> None: ...
@llm(system="Improve this tweet: shorter and punchier.",
user="{tweet}", parse="text", outputs=(("tweet", str),))
def revise(tweet: str) -> None: ...
@workflow
def write_tweet(topic: str @ User) -> str:
User(topic) >> Writer(topic)
Writer: tweet = draft(topic)
Writer(tweet) >> Editor(tweet)
Editor: approved = approve(tweet)
if approved @ Editor:
Editor(tweet) >> User(tweet)
else:
Editor(tweet) >> Writer(tweet)
Writer: tweet = revise(tweet)
Writer(tweet) >> User(tweet)
return tweet @ User
# No API key needed — runs with the built-in mock backend.
# Switch to a real LLM: write_tweet.configure(llms="openai")
result = write_tweet(topic="a git commit message that tells the truth")
print(result)Using real LLMs
Set your API key, pass a provider name, and run:
export OPENAI_API_KEY=...my_workflow.configure(llms="openai", ui=True)
result = my_workflow(notes="...", diagnosis="...")
Built-in providers: openai, mistral, claude.
Different agents can use different providers:
my_workflow.configure(llms={"LLM1": "mistral", "LLM2": "openai"})ZipperChat
Pass ui=True to open a live message-sequence-chart in your browser as the
agents run. Watch who sends what, who is thinking, and where the workflow currently is.
