FAQ
Every answer leads with the answer, then the evidence where it exists, and the status when the question is about an external tool.
The ability of a multi-agent system to keep the context and work state needed to continue a task when it changes agent, session, model or workflow step. It is not about remembering everything, but about what the next agent needs before acting: decisions taken, constraints set, what was done, what is left. Venkai implements it as typed, versioned objects read and written over REST, a Python SDK or MCP.
Memory answers "what does the system know or remember?". Continuity answers "what must survive so the work can actually continue?". A memory layer typically keeps facts about a user or past conversations; continuity keeps the state of a piece of work in progress, so another agent can pick it up without rebuilding it. The two overlap — Venkai also stores facts and preferences — but the unit is the project's work state, not a user profile.
No. Mem0 is a memory layer for AI applications; Venkai keeps the work state that has to survive a handoff between agents, sessions or models. A team can run both. Venkai has no Mem0 adapter and no test against Mem0: the combination is compatible by architecture, not an official integration.
Compatible by architecture/en/integrations/
Yes, by architecture: Venkai is two HTTP calls (read before the prompt, write after a decision) and assumes nothing about any other store in your stack. No memory system has an official Venkai integration today, and none has been tested with it.
Compatible by architecture/en/integrations/
Compatible by architecture, not tested. Zep builds memory from conversations and business data; Venkai keeps typed work state shared between agents. An agent can query both in the same step. There is no Venkai–Zep connector.
Compatible by architecture/en/integrations/
Compatible by architecture, not tested. In practice: before the prompt, the agent reads user memory from Mem0 and project state from Venkai; after a decision, it writes the conclusion to Venkai. Nothing in Venkai conflicts with Mem0, and no official integration exists.
Compatible by architecture/en/integrations/
Compatible by architecture, documented, not tested. The documented pattern: a node before the model node loads Venkai context into the graph state; a node after it writes conclusions. LangGraph's own persistence keeps running the graph; Venkai holds the decisions and constraints other agents or later sessions need. No LangGraph package exists.
Compatible by architecture/en/integrations/
No. Venkai does not decide which agent runs or in what order, and calls no model. Your orchestrator — LangGraph, CrewAI, AutoGen or your own loop — stays in place; it is the one calling Venkai.
No. Venkai indexes no documents and is not a general-purpose similarity engine. It ranks its own context objects by relevance, recency and importance. To search a document corpus, keep your vector database; Venkai sits next to it.
Without a shared layer, the next agent gets whatever the previous one put in its last message, or re-reads the whole history. With Venkai, the outgoing agent has written its decisions, constraints and open items as typed objects; the incoming agent recalls the ones relevant to its task before its first prompt. A handoff can also be declared (POST /api/handoffs, experimental: a record, not a mechanism that enforces anything). Evidence: the A → B → C scenario passes 19 of 19 checks (python demos/agent_handoff_demo.py).
Context lives in Venkai's store, not in the model's window. A new session recalls the objects relevant to its task. Verified: context survives the agent and the process restarting. Limit: SQLite is the validated engine; PostgreSQL is not.
Nothing Venkai stores is model-specific: objects are typed text with an importance and a version, returned as JSON. In the handoff scenario, the third agent is declared on a different model and resumes the work. Limit: that scenario has not yet been replayed against two genuinely different model vendors.
What the next agent needs before acting, not the transcript: decisions and their reasons, hard constraints, established facts, preferences, what happened, how entities relate, and what is left to do. Leave out intermediate thoughts, tool output that can be re-read, and anything already in the repository.
When work crosses a boundary where context gets lost: several agents on one task, a long-running workflow spread over sessions, a change of model, or a human handing work to an agent. And when you want that state typed, versioned and isolated per organization rather than buried in chat logs.
One agent in one session: the context window is enough. A purely documentary need: use RAG. Semantic search required from day one without configuring an embedding model: default embeddings are hashing-based. Production scale as a prerequisite: PostgreSQL is not validated. A need for locking or conflict detection between agents: Venkai has neither today.
Memory for an AI agent is anything it can retrieve about past work that is not in its current context window. It splits into three families: conversation history (what was said), document retrieval (what is written somewhere), and operational state (what has been decided, done, constrained). The three solve different problems and most systems need more than one. Venkai covers the third.
Because a language model has no storage of its own: everything it knows about a task arrives in the prompt, and the prompt is rebuilt from scratch at every session. Nothing is lost by the model — nothing was ever written anywhere but the window. Fixing it means persisting the state of the work in an external store and re-injecting only the relevant part, which is what Venkai does.
By writing the durable parts of the work into an external store, then retrieving them by relevance at the start of each run instead of replaying the whole transcript. In practice: choose what deserves to persist (facts, decisions, constraints — not chatter), type it so it can be queried, and cap what you re-inject so the window stays small. Venkai implements that pattern over REST, a Python SDK and MCP; the same pattern can be built by hand on a database and an embedding index.
No. RAG indexes documents and answers "what information exists here?"; agent memory keeps the state of work in progress and answers "what has already been decided, and what is left to do?". A RAG corpus is written ahead of time by humans; a memory is written by agents as they go. The two layers coexist well: Venkai is the second one and does not replace a document index.
Far less than a full transcript, and much less than most systems send. What an agent needs in order to continue is the current state — open decisions, constraints, what has already been tried — not the dialogue that produced it, and the dialogue is where the volume is. No reduction figure is published here: the measurement has not been made under conditions we could hand to a reader.
A structured context store for agents. An agent writes typed objects into it — facts, decisions, constraints, preferences, events, relationships — and another agent retrieves them by relevance, in a different session or on a different model. Reachable over REST, Python SDK and MCP.
History keeps what was said; you have to re-read it to know what it contains, and it grows without bound. Venkai keeps what the conversation produced: the state of the work. That's a fraction of the volume, typed, versioned and queryable without re-reading.
80/80 core tests and 19/19 handoff checks, including a handoff to an agent on a different model, persistence across restart, multi-tenant isolation, and versioning with restore. No cost or performance figure is published: those measurements haven't been made.
A few lines per agent through the SDK, one HTTP call over REST, or one config entry for MCP. The limits are stated: SQLite is the validated engine, PostgreSQL and Docker are not, default embeddings are hashing-based, and production monitoring doesn't exist yet.