Feature Comparison
Compare AI Memory Solutions
Most "AI memory" systems simply replay conversation history or rely on embeddings. CLAIV provides deterministic, structured memory designed for AI chat applications and long-running agents.
The Problem
Most AI systems don't actually have memory
Many applications claim to support AI memory, but what they typically implement is one of three patterns — each with fundamental limitations at scale.
Conversation Replay
Re-injecting previous messages into the prompt. Works for short demos, breaks under token limits and context drift.
Vector Search
Embedding messages and retrieving similar ones. Returns text fragments, not structured facts. No contradiction handling.
Custom Storage Logic
Developers building ad-hoc memory in their own code. Months of engineering, ongoing maintenance, no benchmarks.
Common issues as systems scale:
Memory drift — facts become stale silently
Contradictory information injected into prompts
Unpredictable retrieval under load
High token costs from full context injection
Difficulty debugging agent decisions
No GDPR-compliant deletion path
CLAIV was built to solve these problems by treating memory as infrastructure rather than prompt engineering.
Feature Matrix
Memory architecture comparison
A direct capability comparison across the four most common AI memory approaches.
| Capability | Conv. History | Vector DBs | Custom Systems | CLAIV Memory |
|---|---|---|---|---|
| Long-term persistence | ||||
| Deterministic recall | ||||
| Structured fact extraction | ||||
| Contradiction handling | ||||
| Token-efficient retrieval | ||||
| Evidence traceability | ||||
| Temporal change tracking | ||||
| Agent-ready architecture | ||||
| Debuggable memory state | ||||
| GDPR-compliant deletion | ||||
| Synthesized recall answers | ||||
| Infrastructure API |
CLAIV treats memory as a structured system of facts, events, and decisions rather than raw conversation history. This allows AI systems to reason over memory rather than simply retrieving similar text.
CLAIV vs Vector Databases
Similarity search is not the same as structured memory
Many developers use Pinecone, Weaviate, or Supabase pgvector for AI memory. But vector databases were designed for semantic search — not stateful AI memory.
Vector databases store similarity
They retrieve semantically similar chunks, which can be noisy for exact fact lookup — returning fragments that may or may not contain the precise information you need.
CLAIV stores structured facts
CLAIV extracts structured facts (entity, predicate, value, time) and retrieves using predicate/entity constraints plus semantic ranking, then synthesizes grounded answers with higher precision and less chunk fragmentation.
What vector databases lack:
No fact extraction — returns raw text chunks
No temporal awareness — can't track how facts change over time
No synthesis — you assemble context yourself
No contradiction handling — older facts accumulate unchecked
Deletion removes vectors, not structured knowledge
Concrete example
User says: "I moved from Sweden to London four years ago."
Query: "Where is the user from?"
Results (similarity-ranked):
→ "I moved from Sweden to London four years ago."
→ "My family is originally from Gothenburg."
→ "London has been great so far."
Ambiguous — LLM must interpret which is current.
Query: "Where is the user from?"
Extracted facts:
origin_city → Gothenburg, Sweden
current_city → London
relocation_date → ~4 years ago
Deterministic — exact facts, no ambiguity.
CLAIV vs Conversation Replay
Putting old messages back into the prompt is not memory
Most teams start with the simplest approach: inject previous messages into the system prompt. It works for demos. It does not scale.
Why prompt injection breaks down:
Token limits — long conversations exceed context windows
Context drift — outdated assumptions stay in the prompt
Uncontrolled growth — no mechanism to prune irrelevant history
No cross-session memory — starts fresh every conversation
Old contradictions re-appear — no conflict resolution
CLAIV solves this by:
Storing only important facts — noise is discarded at the Gate stage
Retrieving only relevant context — your prompts stay small and stable
Persisting memory across sessions — users are remembered forever
Synthesising a ready-to-use llm_context.text — one field to inject
POST /v6/recall
{
"user_id": "user-123",
"query": "What does this user prefer?"
}
=> {
"llm_context": {
"text": "The user prefers React and TypeScript.
They moved to London 4 years ago from Sweden.
They are working toward a senior engineering role.
Their current project uses a microservices arch."
},
"answer_facts": [
{
"predicate": "prefers_framework",
"object_text": "React",
"source_text": "I switched to React last month"
}
]
}The Agent Problem
Why AI agents need real memory infrastructure
The biggest shift happening right now is the rise of AI agents — systems that run continuously, perform long-running tasks, and interact with users over time. Without memory infrastructure, agents become inconsistent, forgetful, and difficult to debug.
Without memory infrastructure
- Agents re-ask questions users already answered
- Long-running tasks lose context mid-execution
- Personalisation resets with every new session
- No history of past decisions or recommendations
- Debugging agent behaviour is nearly impossible
With CLAIV
- User identity and preferences persist indefinitely
- Long-term project history survives session boundaries
- Past decisions are recalled and reasoned over
- Contextual knowledge grows with every interaction
- Memory state is fully inspectable via API
CLAIV enables agents to maintain:
This allows agents to behave more like stateful systems rather than stateless chatbots.
How It Works
CLAIV memory architecture
Three endpoints. Structured facts. Deterministic recall.
POST /v6/ingestExplicit memory writes
Nothing is stored automatically. Developers choose exactly what gets remembered — messages, tool outputs, user statements. A five-stage LLM pipeline extracts structured facts asynchronously.
POST /v6/recallDeterministic recall
Instead of similarity search, CLAIV retrieves memory using predicate matching, vector similarity, temporal reasoning, and keyword search in parallel. Returns answer_facts plus llm_context.text — ready to inject.
POST /v6/forgetMemory correction + deletion
When new information contradicts old memory, CLAIV updates rather than accumulates. GDPR deletion returns a structured receipt with deleted_counts across events, facts, episodes, chunks, claims, and open loops.
Three-Tier Storage
Hot (always recalled), Warm (semantic relevance), Cold (archived superseded facts). Automatic tiering by importance score.
Evidence Traceability
Every fact is anchored to a verbatim source_text span. You always know exactly where a memory came from.
Agent-Ready
Multi-tenant isolation, cross-session persistence, predicate routing, and synthesised narratives — built for production agent deployments.
Best Fit
When should you use CLAIV?
If your AI system needs to remember users across sessions, CLAIV provides the infrastructure layer for that memory.
AI chat assistants
Personal assistants that learn user preferences, goals, and context over time.
AI customer support
Support bots that remember past tickets, user history, and product configurations.
Multi-agent systems
Agent networks that share a consistent memory graph and avoid contradictions.
Long-running automation
Agents that execute tasks over days or weeks and need persistent state.
Personalized AI products
Products where user-specific context is core to the value proposition.
Developer tools with AI
Code assistants that remember project context, preferences, and past decisions.
Not sure if CLAIV is the right fit?
Try the interactive playground — no account required.
Open PlaygroundFAQ
Frequently asked questions
Is CLAIV a vector database?
No. CLAIV uses structured memory rather than similarity search. Instead of returning chunks that are "similar" to a query, CLAIV retrieves exactly the facts that are relevant — using predicate matching, temporal reasoning, and LLM-synthesized answers. Vector databases are designed for semantic search; CLAIV is designed for stateful AI memory.
Can CLAIV work with any LLM?
Yes. CLAIV is fully model-agnostic. It works with OpenAI, Anthropic, Google, Meta, Mistral, and any other model. You pass the llm_context.text from a recall response directly into your system prompt — no SDK required.
Can CLAIV replace a custom memory system?
Yes, and that is what most teams use it for. Building fact extraction, conflict resolution, temporal tracking, tiered storage, GDPR deletion, and audit trails from scratch takes months of engineering. CLAIV provides all of this as a hosted API.
Does CLAIV store entire conversations?
No. CLAIV extracts structured facts from conversations via POST /v6/ingest and stores only the high-signal assertions. This keeps your memory graph clean, your token usage low, and your recall responses relevant.
Explore More
Related comparisons
Stop rebuilding AI memory in every project.
CLAIV gives you production-ready memory infrastructure so your agents and assistants can remember what matters.