Feature Request: Python bindings
Most LLM agent frameworks (LangGraph, LlamaIndex, AutoGen) are Python-first. Adding Python bindings would let agents embed Dendrite without FFI or IPC overhead.
Scope
pyo3-based bindings for the core TreeState and KvCache API
dendrite-py crate (new crate in workspace)
pip install dendrite via PyPI (or local build)
- Basic example: fork, append tokens, query KV state from Python
API Target
import dendrite
state = dendrite.TreeState(num_layers=32, num_kv_heads=8, head_dim=128)
parent = state.root()
child1 = state.fork(parent)
child2 = state.fork(parent)
state.append_token(child1, token_id=42)
state.append_token(child2, token_id=99)
Why This Matters
Dendrite's O(1) fork advantage is most valuable to Python agent frameworks doing tree search. Right now there's no clean path to use Dendrite from Python without running it as a subprocess.
References
- pyo3 docs
crates/dendrite-ffi/ — existing FFI crate (currently a stub)
Feature Request: Python bindings
Most LLM agent frameworks (LangGraph, LlamaIndex, AutoGen) are Python-first. Adding Python bindings would let agents embed Dendrite without FFI or IPC overhead.
Scope
pyo3-based bindings for the coreTreeStateandKvCacheAPIdendrite-pycrate (new crate in workspace)pip install dendritevia PyPI (or local build)API Target
Why This Matters
Dendrite's O(1) fork advantage is most valuable to Python agent frameworks doing tree search. Right now there's no clean path to use Dendrite from Python without running it as a subprocess.
References
crates/dendrite-ffi/— existing FFI crate (currently a stub)