DSPy History
https://dspy.ai/api/primitives/History/
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.
At a glance
- Type
- Signature-field / prompt-injection contract
- Tier
- T3
- Section
- Framework-embedded memory
- Created
- 2023-10 (arXiv:2310.03714 posted Oct 2023; grew from DSP framework research started Feb 2022)
- Latest release
- not applicable — not OSS
- License
- not applicable — not OSS
- GitHub
- not applicable — no GitHub repo
- Pricing
- OSS free (Apache-2.0); dspy.ai commercial services ~$110K ARR but pricing details not publicly listed
- Funding
- Stanford NLP academic project (Hazy Research group); Omar Khattab also affiliated with Databricks; dspy.ai commerci…
Taxonomy
- storage
- kv
- retrieval
- injection
- persistence
- session
- update
- read-only
- unit
- document
- governance
- inspectable
- conflict
- n/a
When to use
Optimised for: typed Signature-field history primitive
Anti-fit: not for non-DSPy programs
Pros & cons
Pros
Typed programming model means memory composes cleanly with prompts and optimizers.
Cons
DSPy's broader adoption is still emerging; more research-tilted than production-tilted.
Claims & capabilities
Open source. Official Mem0+DSPy tutorial.
Technical surface
- API surface
- not applicable — research paper
- Backend storage
- not applicable — research paper
- Deployment
- Both (OSS self-hosted; dspy.ai cloud services if applicable)
- Embedding model
- not applicable — research paper
- Multi-tenancy
- not applicable — research paper
- MCP
- searched not found
- A2A
- not supported
- OpenTelemetry
- via adapter — Arize Phoenix
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)
- Mem0 integrates with — Official Mem0+DSPy tutorial.