Inngest AgentKit — Network State
https://agentkit.inngest.com/concepts/memory
Memory as Network State — shared key-value store owned by the multi-agent network, readable by router / agents / tools. Persisted durably via Inngest infrastructure. Memory writes decoupled from response latency: agent responds immediately, background Inngest function handles DB write durably with retries.
At a glance
- Type
- Durable-network-state / event-driven
- Tier
- T3
- Section
- Framework-embedded memory
- Created
- searched not found
- Latest release
- not applicable — not OSS
- License
- not applicable — not OSS
- GitHub
- not applicable — no GitHub repo
- Pricing
- Free tier; Pro; Enterprise custom; exact pricing not published — contact for enterprise
- Funding
- $34M total; Series B $20.5M May 2025; Series A $6.1M Jan 2024 led by a16z; investors include Notable Capital, a16z,…
Taxonomy
- storage
- kv
- retrieval
- exact-match
- persistence
- session
- update
- overwrite
- unit
- kv-token
- governance
- inspectable
- conflict
- overwrite
When to use
Optimised for: durable event-driven multi-agent state
Anti-fit: not for non-Inngest stacks
Pros & cons
Pros
Durable workflow state for agents — resume cleanly after failure; production-grade orchestration backbone.
Cons
Tied to Inngest platform; memory is workflow-state-shaped, not free-form.
Claims & capabilities
Open source. TypeScript. Official Mem0 cookbook.
Technical surface
- API surface
- not applicable — research paper
- Backend storage
- not applicable — research paper
- Deployment
- Both (deploy AgentKit to preferred cloud provider; Inngest platform manages durable state)
- Embedding model
- not applicable — research paper
- Multi-tenancy
- not applicable — research paper
- MCP
- searched not found
- A2A
- searched not found
- OpenTelemetry
- first-class — Inngest dashboards + OTel exports
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.