mirror of
https://github.com/langchain-ai/langgraph.git
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b146b0dd7e574ade7511d2591e26fbdf7fd752ec
permchain
Get started
pip install permchain
Usage
from permchain import Pregel, channels
grow_value = (
Pregel.subscribe_to("value")
| (lambda x: x + x)
| Pregel.send_to(value=lambda x: x if len(x) < 10 else None)
)
app = Pregel(
grow_value,
channels={"value": channels.LastValue[str]()},
input="value",
output="value",
)
assert app.invoke("a") == "aaaaaaaa"
Check examples for more examples.
Near-term Roadmap
- Iterate on API
- do we want api to receive output from multiple channels in invoke()
- do we want api to send input to multiple channels in invoke()
- Finish updating tests to new API
- Implement input_schema and output_schema in Pregel
- Implement checkpointing
- Save checkpoints at end of each step
- Load checkpoint at start of invocation
- API to specify storage backend and save key
- Add more examples
- human in the loop
- combine documents
- agent executor
- run over dataset
- Fault tolerance
- Retry individual processes in a step
- Retry entire step?
Description
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agentsaiai-agentschatgptdeepagentsenterpriseframeworkgeminigenerative-ailangchainlanggraphllmmultiagentopen-sourceopenaipydanticpythonrag
Readme
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