AgentRecall Gives AI Agents Long-Term Retrieval Memory Without the RAG Plumbing
A developer-friendly retrieval layer that makes your AI agents remember what matters — without building a vector pipeline from scratch.
Detroit, MI, February 23, 2026 — MegatronLabs, LLC today announces AgentRecall, a retrieval-augmented generation (RAG) infrastructure layer for AI developers. AgentRecall handles the full retrieval stack — document ingestion, chunking, embedding, vector storage, and semantic search — behind a simple API that any AI agent can call through MCP.
Building RAG systems is one of the most common AI engineering tasks, and one of the most tedious. Developers spend days configuring vector databases, tuning chunking strategies, managing embedding model versions, and debugging retrieval quality — before their agent retrieves a single relevant document. AgentRecall collapses that work into a single integration.
"Every AI agent project I've seen spends 30% of its time re-solving the same retrieval problems. AgentRecall makes retrieval a solved problem so teams can focus on what their agent actually does." — Jeff Blakely, Founder
Frequently Asked Questions
What vector databases does AgentRecall support?
pgvector, Pinecone, Weaviate, Qdrant, and an embedded local option (no external DB required for development).
What embedding models are supported?
OpenAI text-embedding-3, Cohere, and local models via Ollama.
How is this different from AgentSynapse?
AgentSynapse is for long-term episodic memory (what happened in past sessions). AgentRecall is for document/knowledge retrieval (find the relevant chunk from a corpus). They complement each other.
Can I bring my own documents?
Yes — PDFs, markdown, web URLs, code files, and plain text are all supported at ingestion.
How is retrieval quality measured?
AgentRecall includes a built-in eval harness that scores retrieval precision/recall against your own golden test sets.
About MegatronLabs
MegatronLabs, LLC is a technology intellectual property holdings company founded in 2018, dedicated to managing and maintaining comprehensive IP portfolios for its subsidiary LLCs. Based in Detroit, MI, MegatronLabs provides strategic oversight, legal protection, and operational excellence for technology intellectual property across AI, mobile applications, web platforms, hardware integration, cybersecurity, and data analytics. megatronlabs.com
About AgentQuanta
AgentQuanta is a MegatronLabs intellectual property — a unified suite of AI-native developer tools purpose-built for the agentic era. AgentQuanta gives solo developers and small teams the infrastructure backbone to build, ship, and operate production AI agents, with tools spanning secrets management, persistent memory, observability, multi-agent terminal workspaces, kanban project management, voice input, and retrieval. agentquanta.ai