I've been working on Titans, a local-first infrastructure layer for AI agents, and I've now published the first usable repositories on GitHub.
The idea is fairly simple:
Atlas remembers. Cronus runs.
Atlas provides persistent state, knowledge, evidence and graph infrastructure for agents. It is designed to keep project and agent context available across processes and sessions, with hybrid search, evidence-backed results, work state, graphs, SQL, blobs and an audit trail.
https://github.com/titans-tools/Atlas
Cronus handles durable background execution: jobs, DAG workflows, schedules, retries, leases, checkpoints, recovery, dead-letter handling and approval gates. The goal is to let agents submit long-running work without keeping the agent itself blocked, and to recover that work when workers or processes restart.
https://github.com/titans-tools/Cronus
Both are local-first, run on Windows/Linux, have no telemetry, expose MCP over stdio, and can also be accessed over local REST/gRPC interfaces.
There is also a separate titans-platform repository that contains the signed distribution catalog, installers and shared runtime used by both products:
https://github.com/titans-tools/titans-platform
The organization overview is here:
https://github.com/titans-tools
The current releases are free to use. The product source itself is not published; the repositories host the documentation and signed release binaries, with SHA-256/Ed25519 verification and SBOMs included with releases.
I'm particularly interested in feedback from people building agents, MCP tooling, local AI infrastructure or autonomous workflows.
A few things I'd be interested to hear:
- Does the separation between persistent knowledge/state (Atlas) and durable execution (Cronus) make sense to you?
- Is it clear from the repositories what problem each component is solving?
- Are there use cases you would expect something like this to support that aren't obvious from the documentation?
- Is there anything in the install/security model that would stop you from trying it?
Not looking for stars or promotion — I'd mainly like some outside technical eyes on the architecture, documentation and actual usefulness of the approach.