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Deep Memory
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- Model
- Free
Summary
Agents hallucinate entity names, duplicate nodes, and drift off-schema the moment two agents write to the same graph — deep-memory exists to stop that.
The library pairs a GraphRAG implementation with a Vocabulary system: a shared, schema-enforced dictionary of node types, relationship labels, and property constraints that every agent queries before writing. The result is consistent graph data across sessions without prompting every agent with walls of example documents — the schema replaces the examples, trimming token overhead. Backends include Neo4j, SQL Server, Azure Cosmos DB, and an in-memory option, all wired up via Docker Compose quickstarts the docs describe. Where the ceiling appears: there is no hosted service, no GUI, and no API surface — this is a library you embed and operate, which means your team owns the infra from day one.
Bottom line: Reach for deep-memory when you need schema-enforced consistency across agents writing to the same knowledge graph; look elsewhere when your team has no capacity to self-host and operate a graph backend.
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Pros
Sign in to edit- Shared Vocabulary system enforces node and relationship schemas across every agent that writes to the graph, so two agents running in parallel cannot create conflicting entity types that fracture downstream queries.
- Schema-as-vocabulary replaces bulky in-prompt document examples, so each agent call carries less context overhead — relevant when token costs compound across high-frequency graph writes.
- Backend-agnostic design with Neo4j, SQL Server, Cosmos DB, and in-memory options means you can validate the pattern locally against the in-memory store and then swap to a production graph database with a config change, not a rewrite.
- Docker Compose quickstarts for each backend lower the time from clone to running graph, so evaluation does not require a pre-existing database cluster.
- Open-source codebase under a stated license, so teams that need to audit what gets written to their graph — or adapt the vocabulary logic to their domain — are not blocked by a closed SDK.
Cons
Sign in to edit- There is no hosted service, managed API, or GUI: your team provisions, monitors, and scales the graph backend from scratch. Teams without dedicated infra capacity hit this wall at the first production deployment and move to a managed GraphRAG service instead.
- Vocabulary governance is code-only — there is no visual schema editor or admin UI. When a domain analyst (not an engineer) needs to add a new entity type or review the current schema, they depend on a developer to make and deploy the change, which creates a bottleneck on any team where schema ownership spans roles.
- The project carries 4 stars and 1 fork at the time of the source scrape, which means community-sourced answers, third-party integrations, and battle-tested patterns are sparse. Teams running into edge cases in the vocabulary merge logic or backend connectors are largely on their own until the maintainer responds.
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About
- API Available
- No
- Self-Hosted
- Yes
- Last Updated
- 2026-06-11T05:51:17.187Z
Best For
Who it's for
- AI agent developers needing persistent graph memory
- Teams requiring schema-enforced consistency across agents
- GraphRAG implementations with vocabulary governance
What it does well
- Providing structured memory for AI agents working with knowledge graphs
- Enabling consistent multi-agent data creation via shared vocabulary
- Reducing token usage by replacing example documents with a compact schema
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Frequently Asked Questions
- Is Deep Memory free?
- Yes — Deep Memory is fully free to use. There is no paid tier.
- Is Deep Memory open source?
- Yes. Deep Memory is open source.
- Can I self-host Deep Memory?
- Yes. Deep Memory supports self-hosting on your own infrastructure.
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Curated lists that include this category
Most multi-agent graph pipelines accumulate drift — one agent writes ‘Person’, another writes ‘person’, a third invents a ‘Human’ node type, and the graph becomes unqueryable. deep-memory addresses this by coupling a GraphRAG retrieval layer with a Vocabulary system: a governed, shared schema that defines allowed node types, relationship kinds, and properties. Agents query the vocabulary before writing, so every node lands on a predictable schema rather than whatever the model inferred from context.
The Vocabulary system is the differentiating mechanism. Instead of stuffing example documents into every prompt to teach agents what the graph looks like, you define the vocabulary once and reference it at write-time. The docs describe this as replacing document examples with a compact schema, which cuts token usage on every agent call that would otherwise need that context repeated.
The monorepo covers multiple persistence backends — Neo4j, SQL Server, Azure Cosmos DB, and an in-memory store — each with its own Docker Compose quickstart file and dedicated quickstart guide. This means you can prototype against the in-memory backend and promote to Neo4j or Cosmos DB without rewriting agent logic. The library is open-source under a disclosed license, self-hosted only, and carries no API surface or hosted cloud option.
Integration follows a library-embed pattern: you pull the package, configure a backend via environment variables (an .env.example is provided), and wire your agents to call deep-memory’s vocabulary and memory interfaces. An MCP config example is included in the repo, and a Claude Desktop quickstart exists for teams evaluating the library with that client. There is no GUI for browsing the graph or managing vocabulary entries — all administration happens in code or directly against the backing database.