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Jñāpakam

by Yabloko Labs Ltd.OfficialGitHubWebsiteUpdated May 22, 2026

Jñāpakam provides AI developers with a lightweight, local-first persistence layer for stateful agents. By leveraging a SQLite backend, it enables your agents to store, retrieve, and consolidate long-term context across sessions. Use store_memory, retrieve_memory, and consolidate_memory to maintain a lean, searchable knowledge base without the overhead of external vector database services.

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How to pay

Subscribe

Monthly billing

$5/month

Predictable monthly cost with included usage. Best for steady, high-volume traffic.

  • Unlimited tools within plan limits
  • One API key, billed once a month
  • Cancel any time

Overview

Jñāpakam gives AI agents persistent long-term memory, so they can retain context, preferences, and prior interactions across sessions, restarts, and deployment changes.
It helps your assistant continue with usable historical context even when you switch laptops, rebuild a VM, move systems, or restart your stack.

Key Capabilities

Jñāpakam provides three core memory operations: store_memory to save facts and interaction context, retrieve_memory to surface relevant memory through semantic or keyword search, and consolidate_memory to summarize and prune redundant data over time.
These tools keep agent memory durable, searchable, and efficient without the operational overhead of an external memory stack.

Use Cases

  • A coding assistant that preserves knowledge of your project structure, coding conventions, and prior decisions across sessions.
  • A research agent that retains findings, references, and topic history while long-running work continues over days or weeks.
  • A personal productivity or workflow agent that remembers user constraints, preferences, and recurring context even after local environment changes.

Who This Is For

Jñāpakam is built for AI engineers and developers creating stateful agents with frameworks such as LangChain, AutoGen, or CrewAI.
It is especially useful for teams that want a lightweight, local-first memory layer that survives session resets and environment churn without the complexity of external vector database infrastructure.