Pydantic-AI + Hindsight
https://hindsight.vectorize.io/blog/2026/03/09/pydantic-ai-persistent-memory
Pydantic-AI ships no native persistent memory; agents start fresh each run. Long-term memory typically added via third-party integration (e.g. Hindsight) using tools + auto-injected memory instructions.
At a glance
- Type
- External memory bolted on
- Tier
- T2
- Section
- Framework-embedded memory
- Created
- 2024-06
- Latest release
- v1.90.0 2026-05-05
- License
- MIT
- GitHub
- 16.9k★ +714/mo Python
- Pricing
- OSS
- Funding
- Pydantic (company) raised $17.2M total; Series A $12.5M Oct 2024; investors include Sequoia Capital and Partech Par…
Taxonomy
- storage
- vector
- retrieval
- similarity
- persistence
- long-term
- update
- consolidation
- unit
- episode
- governance
- opaque
- conflict
- none
When to use
Optimised for: type-safe Pydantic-AI agents (memory bolted on)
Anti-fit: not for stateful agent flows out-of-the-box
Pros & cons
Pros
Type-safe agents with explicit memory backend — strongest TypeScript-style typing in Python AI.
Cons
Pydantic-AI is newer; ecosystem of pre-built integrations is smaller than LangChain's.
Claims & capabilities
Hindsight extracts entities and relationships, builds knowledge graph, and provides multi-strategy retrieval (semantic + BM25 + graph traversal + temporal ranking) — exposed to Pydantic-AI as retain/recall/reflect tools, model-agnostic; cleanest pattern uses memory_instructions() to inject relevant memories before each agent run
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
- native (first-party) — Pydantic-AI MCP server/client
- A2A
- supported — Pydantic-AI A2A
- OpenTelemetry
- first-class — Logfire OTel
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.
Related systems
References (1)
- Hindsight (Vectorize) builds on — Long-term memory typically added via third-party integration (e.g. Hindsight)