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InfinityDB
Sub-millisecond neurosymbolic memory engine for AI agents
InfinityDB is an open-source neurosymbolic memory engine for AI agents that runs locally on SQLite WAL mode. Unlike vector memory that relies on costly LLM extraction, InfinityDB provides sub-5ms hybrid recall, deterministic $0 fact extraction, dynamic persona conflict resolution, and SHA-256 audit proofs. Cuts agent prompt token overhead by 81.4%. Compatible with Python (PyPI), Node.js (NPM), LangChain, CrewAI, and Model Context Protocol (MCP) for Claude Desktop and Cursor.
Hi Product Hunt community,
We built InfinityDB to solve the primary bottlenecks facing production AI agents: high token costs, slow recall latency, and lack of deterministic memory auditability.
Most current memory libraries dump conversation transcripts into vector databases. This requires constant auxiliary LLM calls for extraction, introduces vector hallucination on conflicting facts, and incurs 35ms to 140ms latency.
InfinityDB introduces a tiered L0–L6 neurosymbolic architecture operating locally on SQLite WAL mode:
- L0: Disk-backed raw interaction store that eliminates up to 81.4% of agent prompt token bloat.
- L1: Deterministic fact extraction with zero LLM API calls ($0 extraction cost).
- L2: Episodic causal graph tracking dependencies between memory events.
- L3: Dynamic persona with automatic chronological conflict resolution.
- L4: Enterprise policy engine injecting compliance guardrails directly into recall context.
- L6: Immutable SHA-256 cryptographic proof chain linking every recalled fact back to its exact raw source transcript.
Verified benchmarks from our 2,000-turn stress test:
- Recall latency (p95): 4.2ms
- Context token reduction: 81.4%
- Fact extraction cost: $0
Ecosystem support available today:
- Python: pip install infinitydb-ai
- Node.js & TypeScript: npm install infinitydb
- Native MCP Server: Out-of-the-box support for Claude Desktop and Cursor
- Turnkey adapters: Drop-in memory support for LangChain and CrewAI
Try the interactive browser demo without installing anything:
https://huggingface.co/spaces/Di...
Repository (100% open source under Apache 2.0):
https://github.com/infinitydb-ai...
We look forward to your feedback, questions, and feature requests.
About InfinityDB on Product Hunt
“Sub-millisecond neurosymbolic memory engine for AI agents”
InfinityDB was submitted on Product Hunt and earned 2 upvotes and 1 comments, placing #116 on the daily leaderboard. InfinityDB is an open-source neurosymbolic memory engine for AI agents that runs locally on SQLite WAL mode. Unlike vector memory that relies on costly LLM extraction, InfinityDB provides sub-5ms hybrid recall, deterministic $0 fact extraction, dynamic persona conflict resolution, and SHA-256 audit proofs. Cuts agent prompt token overhead by 81.4%. Compatible with Python (PyPI), Node.js (NPM), LangChain, CrewAI, and Model Context Protocol (MCP) for Claude Desktop and Cursor.
On the analytics side, InfinityDB competes within Open Source, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how InfinityDB performed against the three products that launched closest to it on the same day.
Who hunted InfinityDB?
InfinityDB was hunted by Diwakar Srd. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of InfinityDB including community comment highlights and product details, visit the product overview.
Diwakar Srd