Botpress LLMz
https://botpress.com/features/knowledge-bases
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.
At a glance
- Type
- Vector DB + LLMz engine + KB
- Tier
- T1
- Section
- Framework-embedded memory
- Created
- 2017 (Botpress open-sourced on GitHub January 2017, founded by Sylvain Perron and Justin Watson)
- Latest release
- not applicable — not OSS
- License
- not applicable — not OSS
- GitHub
- not applicable — no GitHub repo
- Pricing
- Free 500 msgs/mo; Plus $89/mo 1GB Vector DB; Team $495/mo 2GB; Enterprise custom; AI Spend billed separately per LL…
- Funding
- $40M total; Series A $15M 2021; Series B $25M Jun 2025; Inovia Capital backed
Taxonomy
- storage
- vector
- retrieval
- similarity
- persistence
- cross-session
- update
- extraction
- unit
- document
- governance
- inspectable
- conflict
- llm-arbitrate
When to use
Optimised for: conversational AI + per-plan vector quota + KB
Anti-fit: searched not found
Pros & cons
Pros
Focused on conversational bot deployments at scale — memory is structured around conversation lifecycle (handoff, escalation, return).
Cons
Bot-builder market positioning narrows applicability; less relevant for general agentic apps.
Claims & capabilities
Free 500 msgs/mo; Plus $89/mo (1 GB Vector DB); Team $495/mo (2 GB).
Technical surface
- API surface
- searched not found
- Backend storage
- searched not found
- Deployment
- Both (OSS self-hosted; Botpress Cloud SaaS; private cloud option)
- Embedding model
- searched not found
- Multi-tenancy
- searched not found
- MCP
- via official adapter — Botpress MCP integration
- A2A
- searched not found
- OpenTelemetry
- searched not found
Compare Botpress LLMz with…
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.
- DSPy History T3
dspy.History primitive — typed field holding messages: list[dict] that slots into any Signature . No persistent memory of its own; purely a structured context-injection contract. DSPy's optimisation loop (MIPRO, BootstrapFewShot) treats historical turns as trainable few-shot structure.