Vapi (voice agents)
Voice agents. Externalises memory from LLM into structured call state — variables rather than model recall — reducing hallucinations. Squad mode passes context across multi-agent assistant hand-offs in same call.
At a glance
- Type
- Structured workflow state during call
- Tier
- T2
- Section
- Framework-embedded memory
- Created
- 2023-01
- Latest release
- not applicable — not OSS
- License
- not applicable — not OSS
- GitHub
- not applicable — no GitHub repo
- Pricing
- Pay-per-use
- Funding
- $22M total $130M val Series A · 2024-12
Taxonomy
- storage
- kv
- retrieval
- injection
- persistence
- session
- update
- overwrite
- unit
- fact
- governance
- inspectable
- conflict
- overwrite
When to use
Optimised for: low-latency voice + structured workflow state
Anti-fit: not for non-voice / non-realtime use cases
Pros & cons
Pros
Voice-first agent platform with memory tuned for telephony / IVR; mature voice infrastructure.
Cons
Voice-only positioning; not useful for non-voice agents.
Claims & capabilities
Usage-based (per call minute). Call-scoped only.
Technical surface
- API surface
- searched not found
- Backend storage
- searched not found
- Deployment
- Both
- Embedding model
- searched not found
- Multi-tenancy
- searched not found
- MCP
- via official adapter — Vapi MCP tools
- A2A
- searched not found
- OpenTelemetry
- searched not found
Similar systems
Other framework-embedded memory in the catalog, ranked by inbound references.
- LangGraph Persistence T2
Distinct from LangMem. Built-in checkpointer saves graph state per super-step (short-term, thread-scoped). Store System adds long-term hierarchical key-value memory across threads with optional vector search + TTL. Postgres / Mongo / Redis stores for production.
- AutoGen Memory T2
ListMemory chronological context + teachable agents that vectorise corrections. Integrates with Mem0/Zep rather than building deep memory natively.
- CrewAI Memory T2
Memory subsystem inside the CrewAI orchestration framework; integrates with Mem0 for the long-term tier.
- AGiXT Adaptive Memory T2
Open-source AI automation platform. Routes between short-term and long-term memory adaptively across any LLM provider; plugin system for storage backends. Memory managed at the instruction-management layer — task context, instruction state, conversation history as unified agent state.
- Agno (Phidata) Memory T2
Agno (formerly Phidata). AgentStorage persists sessions to a DB; AgentMemory auto-classifies/store user preferences and conversation summaries. Single-line integrations with LanceDB, Pinecone, Weaviate, Qdrant.
- Botpress LLMz T1
Per-plan vector-DB storage quota + LLMz autonomous engine (in-session working memory) + Knowledge Base (semantic search over uploaded docs). Long-term user memory persists across sessions.