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Atlas is local-first memory, state, knowledge and evidence infrastructure for AI agents. It keeps project knowledge persistent across agents, processes and sessions, with hybrid search, graph relationships, evidence, work state and an audit trail. Instead of rebuilding memory for every agent project, Atlas provides it as reusable infrastructure through MCP, REST and gRPC.
Hey Product Hunt 👋
I built Atlas as part of a broader idea behind Titans: foundational capabilities that AI agents repeatedly need shouldn't have to be redesigned and rebuilt inside every new project.
They should exist as reusable infrastructure that agents and applications can simply consume.
Atlas handles one of those recurring problems: persistent memory, state, knowledge and evidence.
Agent sessions are temporary, but the projects they work on are not. Decisions, relationships, evidence, project state and accumulated knowledge often need to survive across sessions, processes and even different agents.
Atlas provides a persistent local layer for that information, including:
• Persistent project memory and state
• Hybrid semantic and lexical search
• Knowledge graph relationships
• Evidence and provenance
• Work state
• Structured data and blobs
• Audit history
• MCP over stdio
• Local REST and gRPC interfaces
Atlas is local-first and agent-first, currently runs on Windows and Linux, has no telemetry, and is distributed through signed releases.
It is one of the first systems in the wider Titans ecosystem.
Its companion system, Cronus, provides durable execution: background jobs, workflows, scheduling, retries, checkpoints and recovery.
Atlas remembers. Cronus runs.
Both are designed to work independently or together. Existing applications, new agent systems and other parts of Titans can consume these capabilities instead of rebuilding them from scratch.
Other tools are already being implemented as part of the ecosystem around the same principle: identify recurring infrastructure problems in agentic systems, solve them once as reusable capabilities, and make them available to future projects.
I'd especially value feedback from people building agent systems:
• How are you currently maintaining knowledge and project state across agent sessions?
• Do multiple agents in your systems share the same persistent context?
• What would you need from a memory and evidence layer before trusting it with real agent workflows?
Critical technical feedback is very welcome.
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About Atlas on Product Hunt
“Local-first memory and evidence for AI agents”
Atlas was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #136 on the daily leaderboard. Atlas is local-first memory, state, knowledge and evidence infrastructure for AI agents. It keeps project knowledge persistent across agents, processes and sessions, with hybrid search, graph relationships, evidence, work state and an audit trail. Instead of rebuilding memory for every agent project, Atlas provides it as reusable infrastructure through MCP, REST and gRPC.
Atlas was featured in Developer Tools (519k followers), Artificial Intelligence (478.1k followers), GitHub (41.4k followers) and Database (2.2k followers) on Product Hunt. Together, these topics include over 232.4k products, making this a competitive space to launch in.
Who hunted Atlas?
Atlas was hunted by titans-tools. 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.
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