When a data engineer or AI practitioner jumps between a code editor, a chat assistant, and a collaborative workspace, the mental overhead of keeping context alive is a real cost. You finish a debugging session in Claude Code, switch to a brainstorming chat, and later open a new session -- to discover that the decisions you made an hour ago are sitting in a separate log you cannot easily find. Duplicated effort. Missed connections. Context that should have followed you, but did not.
LoreConvo was built to close that gap. By treating every interaction -- whether a code snippet, a design discussion, or a tool invocation -- as a single searchable memory item, it lets you move between surfaces while keeping the full story of your work intact. Three pillars make that possible: coordinated multi-agent handoff, cross-surface persistence, and true data portability.
Multi-Agent Coordination #
Modern AI workflows increasingly involve more than one agent. One generates a draft, another runs analysis, a third handles deployment checks. When those agents share no common memory, each one starts cold -- and the gaps between sessions become invisible tax.
LoreConvo's session linking and skill-history tracking turn handoffs into a continuous narrative. When a session ends, the auto-save hook records a concise summary, the tools called, the decisions made, and any open questions it detected -- all on a heuristic basis, without requiring any manual action. The next agent that picks up the work receives that context through the auto-load hook, which injects the most relevant prior sessions at the start of a new interaction.
Because each session is tagged by project and linked to related sessions, the system can surface a chain of work that spans multiple agents. A data pipeline that started with a schema design can be followed by a performance-tuning session, then a deployment checklist -- and all of it appears as an ordered timeline. The related-session discovery feature (Pro tier) enriches that chain by finding sessions that share keywords or similar embeddings, so loosely related work surfaces when you need it.
This coordination does not rely on a central cloud service. All links, tags, and histories live in a local SQLite file you own. Managed agent sessions are excluded from auto-load and search by default, preventing accidental contamination while still letting you opt in when those sessions become relevant.
Cross-Surface Persistence #
Losing context when switching tools is one of the most persistent frustrations in day-to-day AI work. LoreConvo addresses this with a single portable database that every supported surface reads and writes without manual configuration. Whether you are working in Claude Code, Claude chat, Cursor IDE, the OpenAI Codex desktop app, or Hermes Agent, the same session file is available, and the same MCP tools work across all of them.
Full-text search powered by SQLite's FTS5 engine lets you locate sessions by keyword. When you start a new session, the auto-load hook pulls in the most relevant prior context automatically -- no copy-pasting, no re-typing decisions you already made. The session export and import tools make backup and migration trivial, and exported JSON preserves session identifiers so re-imported sessions remain idempotent.
For teams that need to share knowledge without standing up a server, the Pro tier adds local-first async session sharing. You export selected sessions to JSON; teammates import them with the same tool. The workflow stays entirely offline and under your control. A usage-stats dashboard shows session counts by surface, project, and tag, along with storage size and token estimates -- enough to monitor the health of your memory store without instrumentation overhead.
True Data Portability #
Data ownership is where a lot of memory tooling falls short. When your session history lives in a cloud service you do not control, you are one API change away from losing access to work that belongs to you.
LoreConvo takes the opposite approach. The entire memory layer lives in a single SQLite file on your machine. You can move it to any other device, back it up like any other file, and open it with any SQLite client. There is no proprietary format, no hidden sync, and no vendor dependency. If you move to a different editor or a different AI assistant, you drop a .mcp.json
configuration file in the new environment and the memory file follows.
Session export in JSON and JSONL formats makes it straightforward to ingest your history into other analysis pipelines or archival systems. The memory inspection interface lets you list, filter, view, and delete sessions directly. If you need retention policies, the session-expiry tool sets a TTL on any session -- expired items stay in the file but drop out of default searches, and remain recoverable if you need them back.
For richer retrieval, the Pro tier adds a hybrid search index that combines vector embeddings with full-text scoring and a recency decay reranker. It handles queries like "how did we handle the schema migration last month?" even when the exact phrasing does not appear in any saved session. The index builds once and then serves results across every surface.
Why It Matters #
The practical case for LoreConvo is not that it has more features. It is that the three pillars above -- coordination, persistence, portability -- address the actual failure modes of day-to-day multi-tool AI work.
When you finish a sprint, you can look back at a searchable history that captures which decisions were made and why. When a teammate joins, they can import the same SQLite file and gain contextual awareness that would otherwise take weeks to rebuild. And because the data never leaves your control, you can meet internal data policies without negotiating with a third-party cloud provider.
If you want a workflow where context follows you instead of the other way around, the full LoreConvo capability list is at [/tools](/tools). Questions about how to integrate it into an existing pipeline are welcome at [/contact](/contact).
For a detailed look at how LoreConvo compares to other memory approaches, see [Claude's memory primitive vs LoreConvo vs Claude Mem vs Mem0](/blog/claude-memory-primitive-vs-loreconvo-vs-claude-mem-vs-mem0). And if you are using multiple AI coding tools, the [guide to keeping them all in sync](/blog/i-use-four-ai-coding-tools-heres-how-i-keep-them-all-in-sync) covers how LoreConvo fits into a multi-tool workflow.
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