astraeadb

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Python client for AstraeaDB, a graph database with vector search written in Rust. Talks to the AstraeaDB server over newline-delimited JSON-over-TCP (no dependencies) with an optional Apache Arrow Flight transport and pandas integration.

Installation

pip install astraeadb                 # core client, zero dependencies
pip install "astraeadb[arrow]"        # + Arrow Flight transport (pyarrow)
pip install "astraeadb[pandas]"       # + DataFrame helpers (pandas)
pip install "astraeadb[all]"          # everything

Requires Python 3.9+. A running AstraeaDB server is required — see the AstraeaDB documentation.

Quick start

from astraeadb import AstraeaClient

with AstraeaClient(host="127.0.0.1", port=7687) as client:
    alice = client.create_node(["Person"], {"name": "Alice", "age": 30})
    bob = client.create_node(["Person"], {"name": "Bob"})
    client.create_edge(alice, bob, "KNOWS", weight=0.9)

    # Traversals
    client.bfs(alice, max_depth=2)
    client.dfs(alice, max_depth=2)
    client.shortest_path(alice, bob, weighted=True)

    # Lookups
    client.find_by_label("Person")
    client.find_edge_by_type("KNOWS")

    # Vector similarity (results carry node_id + distance; smaller is closer)
    hits = client.vector_search([0.1, 0.9, 0.3], k=5)

    # Graph algorithms (computed server-side)
    client.run_pagerank()
    client.run_louvain()
    client.run_betweenness_centrality()

    # GQL
    client.query("MATCH (p:Person) RETURN p.name")

Features

  • CRUD — create/read/update/delete nodes and edges with arbitrary properties
  • Lookups — find_by_label, find_edge_by_type, bulk delete_by_label
  • Traversals — bfs, dfs, shortest_path, neighbors
  • Temporal queries — time-travel variants (bfs_at, dfs_at, neighbors_at, shortest_path_at) over edge validity windows
  • Graph algorithms — run_pagerank, run_louvain, run_connected_components, run_degree_centrality, run_betweenness_centrality
  • Vector search — vector_search (k-NN), hybrid_search (graph + vector), semantic_neighbors, semantic_walk
  • GraphRAG — extract_subgraph and graph_rag for LLM context assembly
  • Statistics — graph_stats and raw get_subgraph export
  • GQL — query for Cypher-style graph queries
  • Batch — create_nodes, create_edges, delete_nodes, delete_edges
  • DataFrames — import/export helpers in astraeadb.dataframe (pandas)
  • Arrow Flight — high-throughput columnar transport when pyarrow is installed

Clients

Class Transport Notes
JsonClient JSON/TCP Always available, zero dependencies
ArrowClient Arrow Flight Requires pyarrow; high-throughput bulk/query
AstraeaClient Auto-select Uses Arrow when available, falls back to JSON 
from astraeadb import JsonClient       # explicit JSON/TCP
from astraeadb import ArrowClient      # explicit Arrow Flight (needs pyarrow)
from astraeadb import AstraeaClient    # unified, auto-selecting

License

MIT © 2026 James Harris.

Project links

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Credits

Author: James Harris

GitHub Statistics

Data verified by PyPI on Aug 2, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

MIT
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Requires

Python >=3.9

Provides Extra

all arrow pandas

Tags

astraeadb database graph graph-database graphrag knn vector-search

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