Stop building
amnesiac agents.
Because "similar" isn't the same as "remembered." Reeve is a temporal knowledge graph that understands what you store — not just what's similar.
A brain that remembers.
LLMs forget.
Every conversation
starts from zero.
Common workarounds — chat history, vector stores, RAG pipelines — all break down over time. They retrieve similar text. They can't handle contradictions. They have no concept of time or state evolution.
Ask any of them: "What changed about me since last year?"
Silence. Ask Reeve — it knows.
No cross-session memory
Every new conversation resets context entirely. Your agent is a stranger every time it wakes up.
No contradiction handling
"I moved to New York" and "I live in San Francisco" coexist. No resolution. No truth.
No sense of time
Ask "what changed since last year?" — silence. Memory systems store facts, not state evolution.
Three steps.
Lifetime memory.
Store anything
Call store() with any text. Reeve's LLM parses it into structured entities, states, actions, and locations — writing a living temporal knowledge graph to Neo4j. Not chunks. Not embeddings. Structured understanding.
from reeve import store
store("I just joined Google as a software engineer")
store("I love playing football")
store("I moved from San Francisco to New York")Graph evolves, history preserved
New facts don't overwrite old ones — they create SUPERSEDES chains. Entity resolution ensures "Google", "my company", and "work" all resolve to one canonical node.
(city: New York) ──SUPERSEDES──▶ (city: SF)
active: true active: false
"Google" = "my company" = "work" → one nodeQuery in natural language
Ask anything. The 3-lane retrieval engine surfaces the right memory — not just the most similar text, but the most relevant knowledge at this moment in time.
from reeve import query
query("Where do I live?")
# → "New York."
query("Should I play football with my friend?")
# → "Yes — you love football."
query("Did I ever live in SF?")
# → "Yes, before moving to New York."Built for permanence,
not prototypes.
3-Lane Retrieval
Most systems rank by vector similarity alone. Reeve combines three parallel lanes — semantic, temporal, and recency-weighted — into a single unified score. Important memories surface regardless of age.
score = 0.65×similarity + 0.30×importance + 0.05×recency State Supersession
Facts evolve. Reeve tracks this with explicit SUPERSEDES chains — current answers are always accurate, history is always preserved.
Landmark Memory
Major life events — promotions, moves, milestones — are protected with an importance floor. They bypass recency decay and surface instantly, no matter how old they are.
MCP-Native
Works with any MCP-compatible client — Claude Desktop, LM Studio, AnythingLLM, Cursor. Paste 4 lines of JSON. Done.
{"mcpServers": {"reeve": {"url": "..."}}} Temporal Knowledge Graph
Built on Neo4j with typed relationships — Episodes, Entities, Actions, States, Roles, Locations. Not an embedding dump. A living, evolving model of everything you've stored.
Entity Resolution
"Google", "my company", "work" — all resolve to one canonical node via 3-layer matching: exact, substring, and embedding similarity. One identity, many names.
Lifespan-Aware Scaling
Search depth scales dynamically with graph size — 2% of total episodes, clamped between 50 and 500. Efficient at day one. Deep at year ten. Ready for a lifetime.
Up in minutes.
{
"mcpServers": {
"reeve": {
"type": "sse",
"url": "https://api.reeve.co.in/mcp"
}
}
}Restart your client after saving. Your AI will remember everything from this point forward.
Give your agent
a lifetime.
Memory that persists, evolves, and never forgets what matters. Built on a temporal knowledge graph engineered to last decades.
Build with Reeve →