Dijkstar
Dijkstra/A*
Decision gist · record as of 2026-08-14
Yes. Dijkstar is a mature, actively maintained implementation of a foundational algorithm with no known vulnerabilities, permissive licensing, and minimal dependencies. Install it if you need Dijkstra or A* pathfinding in a graph. The last release was 2021-03-30; verify that Python version support meets your requirements before adopting.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low friction install with a single lightweight dependency (six).
- The package is actively maintained with recent commits and has been stable since its early releases.
License · maintenance · safety
MIT (permissive) — MIT license permits unrestricted use, modification, and distribution in both open-source and proprietary projects with minimal obligations.
last release 2021-03-30 (1963 days) · last repo commit 2026-05-12 · 58 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 76,948 downloads/mo, #14,573 on PyPI
Alternatives
Verify before relying
from dijkstar import Graph, find_path
graph = Graph()
graph.add_edge(1, 2, 110)
graph.add_edge(2, 3, 125)
graph.add_edge(3, 4, 108)
result = find_path(graph, 1, 4)
print(result.total_cost)- Whether the package supports modern Python versions beyond 3.9 (classifiers list ends there; last release was 2021-03-30)
- Performance characteristics or scalability limits for large graphs
- Whether heuristic function API is documented for A* use cases
What it is and what it does
Dijkstar is a graph pathfinding library that implements Dijkstra's algorithm for finding shortest paths between nodes in a weighted graph. It can operate in single-destination mode (halting when a target is reached) or compute paths from a source to all reachable nodes. The library supports optional cost functions to compute edge weights dynamically during traversal and optional heuristic functions to convert the algorithm to A* search, steering it toward a destination rather than exploring uniformly.
The package provides a simple Graph class for building networks by adding weighted edges, and a find_path function that returns path information including the sequence of nodes, individual edge costs, and total cost. It also supports graph serialization via pickle for saving and loading. With only six as a runtime dependency and low install friction, it integrates easily into projects needing graph algorithms.
Use it for
- Route planning in navigation systems or street networks with dynamic cost adjustments for street names or traffic conditions
- Game pathfinding using A* heuristics to find efficient paths for NPCs or agents toward goals
- Network routing or resource allocation problems where you need optimal paths between nodes with weighted connections
- Analyzing connectivity in social networks or knowledge graphs to find shortest relationship chains
- Robotics motion planning to compute efficient paths through obstacle-aware weighted environments
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Dijkstar is a mature, actively maintained implementation of a foundational algorithm with no known vulnerabilities, permissive licensing, and minimal dependencies. Install it if you need Dijkstra or A* pathfinding in a graph. The last release was 2021-03-30; verify that Python version support meets your requirements before adopting.
Install
dijkstar on PyPI
Before you install
Low friction install with a single lightweight dependency (six). The package is actively maintained with recent commits and has been stable since its early releases.
License in practice
MIT license permits unrestricted use, modification, and distribution in both open-source and proprietary projects with minimal obligations.
Quickstart
from dijkstar import Graph, find_path
graph = Graph()
graph.add_edge(1, 2, 110)
graph.add_edge(2, 3, 125)
graph.add_edge(3, 4, 108)
result = find_path(graph, 1, 4)
print(result.total_cost)Verify before relying
- Whether the package supports modern Python versions beyond 3.9 (classifiers list ends there; last release was 2021-03-30)
- Performance characteristics or scalability limits for large graphs
- Whether heuristic function API is documented for A* use cases
Package facts
| License | MIT permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packagesix |
| Maintenance | Actively maintained 1,963 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 76,948 / month, #14,573 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersLicense :: OSI Approved :: MIT LicenseNatural Language :: EnglishProgramming Language :: Python :: 2Programming Language :: Python :: 2.7Programming Language :: Python :: 3Programming Language :: Python :: 3.4Programming Language :: Python :: 3.5Programming Language :: Python :: 3.6Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9 |
Evidence: Dijkstar-2.6.0-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “dijkstra shortest path algorithm”
- DijkstarDijkstar implements Dijkstra's shortest-path algorithm and A* search,…
- HeapDictHeapDict is a mutable mapping that acts as a priority queue,…
- rustworkxA high-performance graph library for Python, written in Rust,…
Give your agent the search over MCP, or paste the wish link into any chat.
More Mathematics packages
NetworkX provides data structures and algorithms for creating, analyzing, and manipulating graphs and networks, supporting everything from simple undirected graphs to complex directed and weighted networks.
kiwisolver is a Python binding to a fast C++ implementation of the Cassowary constraint solver, enabling you to solve systems of linear constraints and inequalities.
Install it if you need to solve constraint systems; skip it if you only need simple linear algebra.
SymPy is a Python library for symbolic mathematics, performing algebraic manipulation, calculus, equation solving, and mathematical expression simplification without numerical approximation.
ContourPy calculates contours of 2D quadrilateral grids using C++11 algorithms wrapped in Python, offering serial and multithreaded implementations without requiring Matplotlib as a dependency.
PyTorch provides GPU-accelerated tensor computation and automatic differentiation for building and training deep neural networks in Python.
onnxruntime loads and executes Open Neural Network Exchange (ONNX) models with a focus on inference performance across CPUs and accelerators.
Install it if you have ONNX models to run in production or development.
See also minorminer · searoute · altgraph · toposort · objectgraph · munkres · grandalf · HeapDict · roundrobin