Easy-to-use tooling, whoever you are.

Make the most of your context layer, whether you're an executive, analyst, developer, data scientist or AI agent.

Talk to your data, and get answers fit for a regulator.

For analysts, executives, programme managers and relationship managers. Use Business Tools to bring contextual AI to life for non-technical users, through traceable, time-aware chat and powerful visualisation tooling.

Works with any LLM

ChatGPT, Claude, Gemini, Llama, or your internal model. Any model with tool-calling support works out of the box.

Temporal precision

Ask "as of Q3 2024" and get an answer from that exact historical state. Compare periods and replay decisions.

Full auditability

Answers trace back to nodes, edges, and timestamps. Regulators and compliance teams see full provenance for any output.

Visual exploration

Browse and navigate graph structure without writing a query. Expand from any entity, colour-code by type, scrub through time.

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Next: Developer Tools →

Easy to use, in multiple languages.

Engineers and integration teams can query Pometry's AI infrastructure in Rust, Python, GraphQL, via MCP, or streaming APIs - with temporal parameters built in to every call.

Systems-level access

Direct bindings to the Raphtory core. Zero-overhead integration for performance-critical applications and embedded deployments.

AI agent integration

Expose your context model as a tool for any model that supports the Model Context Protocol. Compatible with Claude, GPT-4, Llama, and more.

Data science native

Native Python bindings for analytics workflows. Integrates directly with pandas, NumPy, and your existing ML tooling.

Real-time event ingestion

Webhook and event-stream connectors for continuous graph updates. Changes are reflected immediately and preserved in the temporal history.

Flexible query language

Query exactly the fields you need. Supports deep graph traversal, relationship filtering, and temporal windowing in a single request.

Bulk data ingestion

Load any Arrow-compatible tabular data — CSV, Parquet, Pandas, DuckDB, and more. Completely unordered; Raphtory merges all history chronologically.

Documentation

Next: Data Science Tools →

Push the boundaries, with temporal algorithms.

For quantitative analysts, researchers, and data scientists. Run 50+ built-in algorithms against any point in time or window. Extend the library with your own Rust or Python implementations.

Point-in-time execution — run any algorithm against a historical snapshot of the graph.

Window-based analysis — restrict scope to any time window, down to the millisecond.

Change tracking — compare output across periods to detect structural shifts in your network.

Custom algorithms — extend the library with your own Rust or Python implementations.

Documentation

Temporal PageRank Community Detection Anomaly Detection Temporal Shortest Path k-Core Decomposition Betweenness Centrality Louvain (temporal) Weakly Connected Components Link Prediction Node Embedding Temporal Density Flow Analysis Temporal Motifs Clustering Coefficient HITS (temporal) Temporal PageRank Community Detection Anomaly Detection Temporal Shortest Path k-Core Decomposition Betweenness Centrality Louvain (temporal) Weakly Connected Components Link Prediction Node Embedding Temporal Density Flow Analysis Temporal Motifs Clustering Coefficient HITS (temporal)

Change Point Detection Motif Detection Label Propagation Foremost Path Temporal Ego Network Strongly Connected Components Graph Similarity Bipartite Projection Dependency Chain Reachability Triangle Count Fastest Path Graph Neural Network layer Temporal Degree Centrality Temporal Closeness Centrality Change Point Detection Motif Detection Label Propagation Foremost Path Temporal Ego Network Strongly Connected Components Graph Similarity Bipartite Projection Dependency Chain Reachability Triangle Count Fastest Path Graph Neural Network layer Temporal Degree Centrality Temporal Closeness Centrality

Next: Agent Tools →

Give your agents the power of context.

Expose your live context model as a first-class tool for any agent or model that supports the Model Context Protocol. No custom integration required.

Graph traversal as a tool call

Models call Pometry the same way they call any other tool. No bespoke integration — just register the MCP server and go.

Temporal parameters built in

Every tool call accepts at and between filters natively. Agents can reason about historical state without extra plumbing.

Compatible with any model

Claude, GPT, Gemini, Llama, or your internal deployment. Any model with MCP tool support works out of the box.

Learn more about Pometry's AI Infrastructure.

Book a call, view more information, or run a secure, two-week trial in your own environment.

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