| 1 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | — | $10/mo |
| Capabilities | 12 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Creates graph objects from diverse input formats including adjacency matrices, edge lists, GML, GraphML, JSON, and edge-weighted dictionaries. NetworkX uses a flexible node-edge abstraction where nodes can be any hashable Python object and edges store arbitrary attribute dictionaries, enabling heterogeneous graph representations without schema enforcement. The library automatically infers graph directionality and handles self-loops and multi-edges through specialized graph classes (DiGraph, MultiGraph, MultiDiGraph).
Unique: Uses a flexible node-edge abstraction where nodes are arbitrary hashable Python objects and edges store attribute dictionaries, enabling representation of heterogeneous graphs without rigid schema enforcement. Supports four distinct graph classes (Graph, DiGraph, MultiGraph, MultiDiGraph) to handle different topological requirements.
vs alternatives: More flexible than igraph for heterogeneous node/edge attributes and Python-native; more accessible than specialized graph databases for exploratory analysis without infrastructure overhead
Implements breadth-first search (BFS), depth-first search (DFS), and shortest path algorithms (Dijkstra, Bellman-Ford, A*) using iterator-based traversal patterns that yield nodes/edges on-the-fly rather than materializing full paths. The library uses deque-based queue management for BFS and recursive/stack-based DFS, with optional weight-aware variants for weighted graphs. Path algorithms return both the shortest distance and the actual path as a list of nodes.
Unique: Uses iterator-based traversal that yields nodes/edges on-the-fly rather than materializing full result sets, enabling memory-efficient exploration of large graphs. Supports multiple shortest-path algorithms (Dijkstra, Bellman-Ford, A*) with pluggable heuristics for A*.
vs alternatives: More memory-efficient than igraph for large sparse graphs due to iterator patterns; more algorithm variety than basic graph libraries but slower than specialized routing engines like OSRM for geographic networks
Analyzes bipartite graphs (graphs with two disjoint node sets where edges only connect nodes from different sets) using specialized algorithms for bipartite matching, projection, and property checking. Includes maximum bipartite matching (Hopcroft-Karp algorithm), bipartite projection (creating unipartite graphs from bipartite structure), and bipartiteness checking (2-coloring via BFS). Returns matching as edge set, projections as new Graph objects, or boolean for bipartiteness.
Unique: Provides specialized bipartite graph algorithms (matching, projection, bipartiteness checking) with explicit bipartite node partition support via node attributes. Hopcroft-Karp matching is O(E√V), faster than general matching for bipartite graphs.
vs alternatives: More accessible than specialized bipartite graph libraries; faster than general graph matching for bipartite structure; projection functionality unique among standard graph libraries
Exports graphs to multiple file formats including GML (Graph Modelling Language), GraphML (XML-based), JSON, edge lists (CSV/TSV), and adjacency matrices (NumPy/SciPy). Export functions serialize node/edge attributes as format-specific metadata; GML and GraphML preserve full graph structure and attributes, while edge lists and matrices lose attribute information. Supports both text-based (GML, GraphML, JSON) and binary (pickle) serialization.
Unique: Supports multiple export formats (GML, GraphML, JSON, edge lists, matrices) with attribute preservation in structured formats, enabling seamless integration with other graph tools. Adjacency matrix export supports both dense (NumPy) and sparse (SciPy) representations.
vs alternatives: More format variety than basic graph libraries; compatible with standard tools (Gephi, Cytoscape); less specialized than dedicated graph serialization libraries
Computes node importance scores using multiple centrality algorithms: degree centrality (node degree normalized by graph size), betweenness centrality (fraction of shortest paths passing through a node), closeness centrality (inverse average distance to all other nodes), eigenvector centrality (importance based on connections to important nodes), PageRank (iterative importance propagation), and harmonic centrality. Each algorithm returns a dictionary mapping nodes to numeric scores; algorithms use matrix operations (NumPy/SciPy) or iterative approximation for scalability.
Unique: Implements 10+ centrality algorithms with unified dictionary-based output interface, allowing direct comparison of different importance definitions on the same graph. Uses iterative approximation for PageRank and eigenvector centrality to handle larger graphs without full matrix decomposition.
vs alternatives: More comprehensive centrality algorithm coverage than most graph libraries; slower than specialized graph databases for real-time centrality updates but sufficient for batch analysis of networks <100k nodes
Detects communities (densely-connected subgraphs) using modularity optimization algorithms (Louvain, greedy modularity), spectral clustering, and label propagation. The Louvain algorithm uses hierarchical agglomeration with local modularity optimization to find high-quality partitions; label propagation assigns community labels through iterative neighbor voting. Returns a partition as a dictionary or set of sets mapping nodes to community IDs. Modularity score quantifies partition quality (higher = better separation).
Unique: Implements multiple community detection algorithms (Louvain, greedy modularity, label propagation, spectral) with unified partition output format, enabling algorithm comparison on the same graph. Includes modularity scoring to quantify partition quality independent of algorithm choice.
vs alternatives: More algorithm variety than igraph; faster than spectral clustering on large sparse graphs due to Louvain's linear-time approximation; less sophisticated than specialized community detection libraries like Stanza for directed/attributed graphs
Detects graph isomorphism (structural equivalence) and finds maximum matchings (sets of non-adjacent edges) using backtracking-based isomorphism checking and augmenting path algorithms. Graph isomorphism uses VF2 algorithm with pruning heuristics to compare node/edge structure; maximum matching uses augmenting paths (Hopcroft-Karp for bipartite graphs, general matching for arbitrary graphs). Returns boolean for isomorphism or matching as a set of edge tuples.
Unique: Implements VF2 isomorphism algorithm with node/edge attribute matching support, enabling semantic graph comparison beyond pure topology. Provides both bipartite (Hopcroft-Karp) and general matching algorithms with unified edge-set output.
vs alternatives: More accessible than specialized graph isomorphism libraries (Bliss, Nauty) for Python users; slower on large dense graphs but sufficient for molecular structure comparison and moderate-sized network analysis
Analyzes graph connectivity by computing connected components (maximal connected subgraphs), strongly connected components (SCCs) in directed graphs, and bridge/articulation point detection. Uses union-find (disjoint set) for component identification and Tarjan's algorithm for SCC computation. Returns components as generators of node sets or dictionaries mapping nodes to component IDs. Bridge detection identifies edges whose removal disconnects the graph; articulation points identify nodes with the same property.
Unique: Combines multiple connectivity analysis algorithms (components, SCCs, bridges, articulation points) with generator-based output for memory efficiency on large graphs. Tarjan's algorithm for SCC computation is linear-time and handles directed graphs with cycles.
vs alternatives: More comprehensive connectivity analysis than basic graph libraries; faster than manual DFS-based approaches due to optimized implementations; less specialized than dedicated network resilience tools
+4 more capabilities
Generates single-line and multi-line code suggestions in real-time as developers type, using semantic indexing of the entire codebase to retrieve relevant type definitions, function signatures, and contextual patterns. The system maintains a 1M token context window (Pro/Team tiers) that enables suggestions informed by distant code definitions and cross-file dependencies, constructed via local codebase semantic search rather than simple token-based recency. Suggestions adapt to detected coding style on Pro/Team tiers through implicit pattern learning from recent edits.
Unique: 1M token context window with codebase-wide semantic indexing enables suggestions informed by distant code definitions and cross-file patterns, versus competitors (Copilot, Tabnine) that typically use fixed context windows (4K-32K tokens) or file-local context. Claimed 250ms latency suggests optimized retrieval pipeline, though indexing mechanism and performance at scale remain undisclosed.
vs alternatives: Larger context window than GitHub Copilot (8K-32K tokens) and faster latency than unnamed competitors (250ms vs 783ms claimed), enabling suggestions on large codebases with minimal typing delay; trade-off is cloud dependency and undisclosed free tier limitations.
Provides a separate chat interface supporting multiple LLM backends (GPT-4o, Claude 3.5 Sonnet, GPT-4, others) for conversational code assistance. Users attach files, reference recent edits, and trigger compiler diagnostic uploads; the system generates diffs and applies code changes directly to the editor. Model selection is per-conversation, and $5/month in credits (included in Pro/Team) covers external model API costs; overage pricing is undisclosed. Hotkey-driven workflow enables rapid context switching between inline completion and chat.
Unique: Multi-model chat interface with per-conversation model selection and integrated diff application, combined with compiler diagnostic auto-upload. Unlike Copilot Chat (single model per tier) or standalone ChatGPT, Supermaven Chat unifies multiple LLM backends in a single hotkey-driven workflow with direct editor integration for change application.
Supermaven scores higher at 71/100 vs networkx at 25/100. networkx leads on ecosystem, while Supermaven is stronger on adoption and quality.
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vs alternatives: Supports multiple LLM backends (GPT-4o, Claude 3.5 Sonnet) in one interface with included credits, whereas GitHub Copilot Chat is single-model per tier and requires separate ChatGPT subscription for model switching; trade-off is credit limits and unknown overage pricing.
Supermaven Chat can automatically upload compiler diagnostic messages (errors, warnings) alongside code context to provide error-aware suggestions and fixes. The mechanism is described as 'automatically uploading your code together with compiler diagnostic messages,' but specific language/compiler support and the upload trigger mechanism are undisclosed. This feature is Chat-only and not available in inline completion.
Unique: Automatic compiler diagnostic upload in Chat for error-aware suggestions, versus competitors (Copilot, Tabnine) that require manual error context or have limited diagnostic integration. Supermaven's approach reduces friction but with undisclosed language/compiler support.
vs alternatives: Automatic diagnostic upload reduces manual context-gathering compared to manual copy-paste; trade-off is undisclosed language support and unclear upload trigger mechanism.
Supermaven offers a 30-day free trial of the Pro tier ($10/month), providing full access to 1M token context window, largest model, style adaptation, and $5/month chat credits. No credit card is required to start the trial (implied), and trial conversion to paid is automatic after 30 days unless cancelled. Trial terms and auto-renewal policy are not explicitly detailed.
Unique: 30-day free trial of Pro tier with full feature access (1M context, largest model, chat credits), versus competitors (Copilot 2-month free trial, Tabnine free tier only) with different trial lengths and feature access. Supermaven's approach is generous but with undisclosed auto-renewal terms.
vs alternatives: Full Pro feature access during trial compared to limited free tier; trade-off is undisclosed auto-renewal policy and potential unexpected charges if not cancelled.
Supermaven requires internet connectivity and server-side inference; no offline mode or local inference capability is mentioned or available. All code completion requests are sent to Supermaven's backend servers for processing, and responses are returned over the network. This creates a hard dependency on network connectivity and Supermaven's service availability; if the service is down or network is unavailable, code completion is not available.
Unique: Supermaven has no offline mode or local inference capability; all processing is server-side. GitHub Copilot also requires server-side inference, but Tabnine offers local inference options for some use cases. Supermaven's lack of offline capability is a significant limitation for developers with connectivity constraints.
vs alternatives: Supermaven's server-side-only approach is comparable to GitHub Copilot; Tabnine offers local inference options, making Tabnine more suitable for offline work. Supermaven's lack of offline capability is a weakness vs. Tabnine.
Analyzes recent code edits and inferred coding patterns to adapt inline suggestions to match team conventions, naming patterns, and structural preferences. The mechanism is implicit (not explicit fine-tuning) and operates only on Pro/Team tiers, suggesting pattern learning from editor activity rather than explicit configuration. Free tier uses a single base model without personalization.
Unique: Implicit style adaptation via editor activity analysis without explicit configuration, versus competitors (Copilot, Tabnine) that require manual style guides or explicit fine-tuning. Supermaven's approach is transparent to the user but also non-configurable and undisclosed in mechanism.
vs alternatives: Requires no manual style configuration compared to tools requiring explicit style guides; trade-off is lack of transparency and inability to control or export learned styles.
Delivers code suggestions to the editor inline as the developer types, with a claimed baseline latency of 250ms from keystroke to suggestion display. The system uses a cloud inference backend and local editor plugin to minimize round-trip time. Latency claim is positioned against an unnamed competitor (783ms), but methodology is undisclosed and no independent verification is provided.
Unique: Claimed 250ms latency via optimized cloud inference pipeline and editor plugin architecture, versus competitors with higher latency (783ms unnamed baseline). Actual differentiation is undisclosed; mechanism may involve request batching, model quantization, or edge caching, but specifics are not public.
vs alternatives: Faster than unnamed competitor (250ms vs 783ms claimed); trade-off is cloud dependency and unverified latency claim with no SLA or performance guarantee.
Provides native editor extensions for VS Code, JetBrains IDEs (IntelliJ IDEA, PyCharm, WebStorm, etc.), and Neovim, enabling inline suggestion rendering, hotkey-driven chat access, and compiler diagnostic integration directly within the editor. Each plugin variant is maintained separately and integrates with the editor's native autocomplete UI, keybinding system, and file context APIs.
Unique: Native plugins for three major editor ecosystems (VS Code, JetBrains, Neovim) with integrated chat and diff application, versus competitors (Copilot, Tabnine) that support broader editor ecosystems but with less deep integration in some cases. Supermaven's approach prioritizes depth over breadth.
vs alternatives: Deep integration with VS Code and JetBrains (native autocomplete UI, hotkey system) compared to web-based tools or lighter integrations; trade-off is limited editor support (no Sublime, Vim, Emacs) and undisclosed Neovim support details.
+5 more capabilities