Persistent Agent Memory That Never Bloats Your Context
A native Rust memory layer for Claude Code, Cursor, and autonomous AI agents. Hydrates the exact knowledge your agent needs right now—and offloads the rest.
Engineered for Zero-Hallucination Precision
No Python warmups. No heavy vector containers. Just deterministic native speed.
Deterministic Native Engine
Queries complete in under 3ms directly in-process. Titan Mem indexes your project's architectural decisions, rules, and lessons to locate relevant context instantly without stalling your agent's thinking loop.
Progressive Disclosure (<150 Tokens)
Instead of cramming 60+ system skills (57,000+ tokens) into every prompt turn, Titan Mem uses progressive disclosure pointers capped under 150 tokens. Yields a verified 98.9% token reduction and keeps your active context window clean.
Instant Deduplication
Prevents memory drift before it happens. Titan Mem's write gate intercepts redundant rules and repeated lessons in under 1.5ms, updating usage frequency rather than polluting your database with duplicate entries.
Causal Decision Graphs (1,400+ Fixes)
Every stored lesson links causally to preceding design choices with 1,400+ indexed error-to-fix patterns in SQLite. Directed SUPERSEDES and CONTRADICTS edges stop agents from hallucinating or repeating previously fixed bugs.
Universal MCP Protocol
Integrates instantly via Model Context Protocol (MCP). Ready out of the box with Claude Code, Cursor, Windsurf, Zed, and custom autonomous agent workflows.
Zero Cloud Telemetry
Your codebase, proprietary keys, and architectural plans remain strictly on your physical NVMe drive. No data is ever sent to an external server without your explicit command.
The 4-Hour Bug That Never Happened
How Titan Mem turns one senior engineer's Monday morning fix into instant collective intelligence for the entire engineering organization.
Solves Obscure Kubernetes OOM Kill
Titan Mem indexes the novel rule in <1.5ms and syncs the authenticated delta to the team pool in 25ms. The senior dev gets back to building.
Generates New Microservice Manifest
The junior dev has no idea the bug ever existed. Their deployment works on the first try. Zero duplicate debugging, zero Slack interruptions.
Usage & Workflow: "Remember This Fix"
No complex JSON schemas. No git hook scripts. Just natural conversational directives for your AI agents.
1. Natural Chat Directives
During or after solving a tricky bug, simply tell Claude Code, Cursor, or Antigravity in plain conversational English:
The agent automatically resolves the active Git commit SHA and touched files, persisting the rule in <1.5ms without you typing JSON.
2. The 2-Line Project Rule
To capture lessons completely hands-free, add this 2-line directive to your repository's CLAUDE.md or .cursorrules:
Your agent will capture lessons autonomously after successful debugging turns, building institutional memory in the background.
3. Collective Team Memory
To broadcast a fix across your entire engineering team and display it in the Team Hub Dashboard:
Tags the memory with team scope. Replicates to the team pool in 25ms so your colleagues' IDE agents inherit the fix instantly.
Transparent, Predictable Pricing
Free for solo hackers. Enterprise power for engineering teams.
Full native power for a single local machine. 100% offline, zero cloud dependencies, locked to your local drive.
- Full Pure-Rust Engine (Sub-3ms Latency)
- Unlimited Local Memories & Graph Nodes
- Standard Open SQLite Database (Zero Lock-In)
- All MCP Integrations (Claude, Cursor, Warp, etc.)
- 100% Sovereign & Fully Offline
Never re-teach your AI agent again. Seamlessly carries your agent's learned lessons, rules, and fixes across all your machines.
- Everything in Community
- Zero-Knowledge E2EE Multi-Device Sync (Mac, Linux & Cloud Devboxes)
- Instant Brain Portability: Switch to your laptop and Claude already knows your past fixes
- Agent Time Machine: Point-in-time rollback if an agent learns flawed patterns
- Token Savings Telemetry: Real-time dashboard of avoided prompt bloat & saved API costs
- Priority Discord Channel & Direct Engineering Support
Instant collective intelligence. When one senior engineer solves a tricky bug, the whole team's AI knows the fix instantly.
- Everything in Pro
- Shared Team Memory Pools via high-speed Central Sync Relay
- Private vs. Team Scoping (prevents memory contamination)
- Causal Conflict Resolution with automatic SUPERSEDES edges
- Team Access Control (RBAC) & 1-click seat revocation
- Full Commercial Enterprise License
Frequently Asked Questions
Direct technical answers. Zero marketing fluff.
Does storing session context make the model generate tokens faster?
No—and anyone claiming local memory accelerates cloud LLM generation speed is being dishonest. Model inference speed is physically constrained by cloud GPU cluster latency and Time-To-First-Token (TTFT). What Titan Mem eliminates is human wall-clock debugging hours and runaway token costs. Instead of pasting 20,000-token historical transcripts or having a new engineer spend 4 hours re-solving an obscure allocator bug, Titan Mem injects a <150 token distilled rule on prompt 1.
How does Titan Mem prevent hallucinations and bug regressions?
Causality over raw transcripts. Dumping raw chat logs into an agent pollutes the search index with terminal noise. Titan Mem extracts structured causal pairs into an indexed error_fix_index (over 1,400 verified bug-to-fix patterns) and links decisions using SUPERSEDES and CONTRADICTS directed edges. When a compiler or test fails, the model is physically anchored to your project's verified history rather than exploratory hallucination.
How does Titan Mem handle hundreds of enterprise MCP tools without blowing up my context window?
Enterprise agents often connect to hundreds of tools across cloud infrastructure, databases, and payment APIs—preloading these schemas conventionally burns 40,000+ tokens before you even type a prompt. Titan Mem features an autonomous native tool router. Instead of exposing every tool continuously, Titan Mem matches your intent against your organization's tool catalog in under 30 milliseconds, injecting only the exact 2 or 3 tools needed for the immediate step. You get the power of an entire enterprise toolchain with a verified 99.7% reduction in token overhead.
Why use Titan Mem instead of a cloud decision model like Jev or external tool routers?
Developers are raving about "System 1" decision models (like TypeSafe's Jev) because nobody wants to waste 45,000 prompt tokens on an LLM just to pick an API to call. But Jev is still a third-party cloud service that adds network hops and privacy exposure. Titan Mem performs local, in-process System 1 routing on your machine before the LLM ever sees the prompt:
| Execution Step | Conventional Naive Setup | The Jev Approach | The Titan Mem Architecture |
|---|---|---|---|
| System 1 (Routing & Selection) | Stuffs 250 tool schemas into prompt. Cost: ~48,000 tokens. Latency: 1,200ms+. | Queries an external cloud decision API (Jev) over HTTPS to pick a tool. | Runs in-process native search locally. Latency: 14ms–28ms. Tokens: 0. |
| System 2 (Execution & Reasoning) | LLM suffers severe attention drift across 250 tools. | LLM receives chosen tool and drafts execution action. | LLM receives only top 2 matched schemas (~140 tokens) with zero confusion. |
| Data Privacy & Boundaries | Cloud LLM vendor sees all internal tools & state. | State shared with third-party cloud routing service. | 100% Local Sovereign NVMe. Zero external egress. |
Do I or my team have to restart our IDE when new memories sync?
No. Restarting IDEs and losing open tabs is completely unacceptable. Titan Mem runs with SQLite Write-Ahead Logging (WAL) concurrency. When team rules or multi-device deltas sync in the background, active Claude Code, Cursor, and terminal sessions detect new rows on their very next query—with zero IDE restarts and zero prompt latency.
How do I use "Remember this fix"? Do I have to type complex JSON commands or install git hooks?
Never. You don't type JSON and you don't install git hooks. You simply tell your agent in natural English during any chat: "Remember this fix: [description]". The agent automatically resolves the active Git commit hash (via git rev-parse --short HEAD) and touched file paths, storing the rule in SQLite via MCP. To make it completely autonomous, add a 2-line directive to your repository's CLAUDE.md or .cursorrules, and your agent will capture lessons after successful debugging turns automatically.
How does Titan Mem prevent prompt bloat?
Conventional agents dump entire files, long rule lists, and chat history into every turn, causing prompt bloat and massive API bills. Titan Mem keeps your system prompt lean (~400 tokens) and hydrates matching rules just-in-time via sub-3ms MCP search only when relevant.
What happens to my data if I uninstall?
Nothing happens to your data. The uninstaller removes only the binary and client configurations. Your SQLite database at ~/.titan/mem/memory_graph.db is preserved untouched. You can inspect it anytime with standard SQLite tools.
Do you send my code or memories to an external server?
No. Titan Mem runs completely locally on your machine. In the free Community tier, network egress is literally non-existent. In the Pro tier, cloud sync is End-to-End Encrypted (E2EE) with zero-knowledge keys—we cannot read your memories even if subpoenaed.
Why native Rust instead of Python?
Python engines require heavy virtual environments, 150MB+ runtimes, and introduce 200ms–800ms cold start delays. Titan Mem is compiled into a single static native binary that launches in 2.9ms, consumes under 15MB of RAM, and runs with zero external dependencies.