ParseHawk

Open-source web crawler designed for LLMs and AI agents with structured extraction and browser automation.

Open-source local-first cognitive memory system implementing AGM-compatible belief revision that automatically re-evaluates downstream beliefs when facts change, with SHA-256 hash chain for data integrity.

Adaptive Recall is a hosted memory system for AI applications that goes far beyond simple vector search. It stores, recalls, and manages long-term memory for agents and apps over MCP or a plain REST API, and — unlike a static embeddings store — it actively learns. Four retrieval strategies run in parallel (vector similarity, temporal recency, full-text keyword, and knowledge-graph traversal), and the system learns which to prioritize for each query type. Results are ranked with ACT-R cognitive scoring from 30 years of cognitive-science research, factoring in recency, access frequency, entity connections, and validated confidence. A knowledge graph is built automatically from stored memories, memories move through a confidence-based lifecycle and fade when unused, and an ML pipeline trains on your usage patterns — validating every parameter change against real query history before adopting it. A simple eight-tool API (store, recall, update, forget, graph, status, snapshot, feedback) covers everything, with Bearer-token auth and JSON in/out. Free, Starter, Pro, and Business plans are available.

A deterministic SQL semantic inspector that catches silently-wrong AI-generated queries — double-counting, bad joins, exposed PII — in about 0.1 ms before they run. Works as a CI gate, an MCP server, or a library.