Every AI agent has the same problem. It listens, responds, and forgets. The next conversation starts from zero. Every time. Crosmos fixes that. It gives your agent a memory that grows with every interaction, so responses get better, not just bigger.Documentation Index
Fetch the complete documentation index at: https://docs.crosmos.dev/llms.txt
Use this file to discover all available pages before exploring further.
The problem with how agents remember today
Most tools chunk your documents, embed them in a vector database, and call it memory. That works for finding a paragraph about quarterly revenue. It falls apart the moment you need something like:- Temporal
- Relational
- Evolving
“What was I working on last summer?”Vector search finds text that mentions “summer.” It has no idea when the event happened or that a newer fact might supersede an old one.
How Crosmos is different
Crosmos does not flatten content into vector chunks. It builds a Monotonic Temporal Knowledge Graph: a living, time-aware graph of entities and relationships that only grows.Extract and connect
Facts, entities, and relationships are automatically pulled from raw content and woven into the knowledge graph. No manual tagging. No prompt engineering.
Retrieve
When your agent asks a question, four independent search signals fire in parallel and return exactly the context it needs. Read more about how retrieval works.
Why it works
The graph only grows
New facts append. Old facts stay. Both “I work at Google” and “I joined Anthropic” coexist, timestamped and ranked by recency. History is always recoverable. A smart forgetting layer fades low-importance noise while keeping what matters accessible. The result: your agent builds a richer picture with every conversation.Time is built in, not bolted on
Every fact carries two timestamps: when the event actually happened, and when the system learned it. Temporal queries are native. Ask “what changed since we last talked” and Crosmos uses the real event time, not the ingestion time.Four signals, one answer
No single search method catches everything. Crosmos runs four in parallel and fuses them:| Signal | What it catches |
|---|---|
| Semantic | Meaning-level matches through vector similarity |
| Keyword | Exact names and terms that semantics miss |
| Graph | Contextually connected entities beyond text overlap |
| Temporal | Facts inside the queried time window |
More than memory, a reasoning layer
Dozens of conversations about programming languages, editor preferences, and tooling choices. Individually, they are scattered facts. Together, they tell you something bigger: this user strongly prefers low-level systems work with keyboard-centric tools and avoids GUI-heavy workflows. Crosmos clusters related memories into synthesized summaries in the background. It is not just storing facts. It is understanding patterns. Your agent does not just remember what was said. It reasons about what it all means. Learn more about how clusters work in the Clusters deep dive.Structured, not stuffed
Raw text goes in. Structured knowledge comes out. Conversations, PDFs, and documents become entity-relationship graphs with confidence scores and provenance. The pipeline handles extraction automatically. See how memories are structured and stored.Built for the tools you use
MCP server
Plug into any MCP-compatible client. Your agent gets memory automatically.
Connectors
Ingest from the tools you already use. No custom pipelines needed.
Multi-tenant
Isolated memory spaces per organization. Zero cross-contamination.
Any content type
PDFs, plain text, conversation transcripts. The pipeline handles them all.
Memories as the source of truth
Anthropic’s research on contextual retrieval makes the case clearly: agents need richer, more connected context to reason well. Raw chunks are not enough. Crosmos is built around that principle. Memories carry entity relationships, temporal grounding, and importance scores. Clusters synthesize patterns across those memories. Together, they form the best corpus an LLM could ask for: structured, connected, and ranked by what actually matters. When your agent retrieves context from Crosmos, it is not getting a bag of text fragments. It is getting a reasoning-ready knowledge base that improves with every conversation.Next steps
Quickstart
Make your first API call in minutes.
How it works
Dive into the architecture behind the engine.