Duplex and Deeplake Hivemind,
side by side.
Deeplake Hivemind gives coding agents shared memory and turns their sessions into skills. Duplex learns from every result, across your assistants and your own agents, and acts on it within your rules.
Comparison current as of 16 October 2026.
Every capability: Duplex and Deeplake Hivemind.
What each one does, group by group, from what it is to what it costs.
- Native
- Built into how the product works at its core.
- External
- You provide it, or assemble it, separately.
- Not native
- The product’s documentation doesn’t list it as a built-in feature. A custom build may still be possible.
| Capability | Duplex | Deeplake Hivemind |
|---|---|---|
| What each one is | ||
| What it is | A runtime intelligence beside your AI: it learns and acts for your agents and assistants, within your rules | Shared agent memory that turns traces into skills automatically |
| What it carries forward | Context, decisions, outcomes, rules, tasks, skills and what it has learned | Sessions, memories and skills scoped to a workspace |
| What gets better with experience | Its next decisions: confidence, routing, recommendations, tool use and reusable knowledge | The next session’s behaviour, through distilled skills |
| Knowing what happened, and learning from it | NativeIt knows what happened, and learns what to change next | Shared memory is turned into reusable skills |
| Learning | ||
| Continual learning without retraining | NativeIt keeps learning, outside your model’s weights | NativeLearning skills from traces |
| Turn-by-turn adaptation | NativeEach new result can change the next decision at once | Skills are distilled in the background or at the end of a session |
| Learning from corrections | NativeA correction changes what Duplex does next | Corrections and session traces can become skills |
| Learning from outcomes | NativeSuccess and failure feed straight back into what it learns | New skills are drawn from finished sessions |
| Learning from rewards | NativeA reward strengthens what worked and corrects what didn’t | Rewards aren’t its main way of learning |
| Credit for the steps behind a result | NativeIt works out which earlier actions led to a later result | Analysing traces can find reusable procedures |
| How actions move the work | NativeIt learns how each action moves the work from one state to the next | Can capture successful procedures as skills |
| Predicting what an action will do | NativeFrom what it observes, it predicts what an action will do, tries alternatives and plans | Not native |
| Better choices of action | NativeOutcomes improve which action it picks next | Distilled skills improve the next procedures |
| Reusable skills | NativeExperience becomes skills that are tested before use, and can be paused, brought back or withdrawn | NativeSessions become skill.md files for the workspace |
| Testing and managing skills | A skill is tested before it is relied on, and can be paused, brought back, withdrawn or removed for good | Skills stay files you can inspect and edit, linked to the sessions they came from |
| Generalising to related cases | It carries what it learned to related cases and new wording, and into skills it can reuse | Skills capture patterns that recur across sessions |
| Deciding and acting | ||
| Knowing how sure it is | NativeBefore every decision, it knows how familiar the case is and how sure it is | How relevant retrieval and skills are, not a measure of certainty |
| Answer, hand over or decline | NativeDuplex answers itself, calls your model, uses a tool or declines, within your rules | ExternalThe host agent decides how to act |
| Answering familiar work itself | NativeIt makes familiar decisions itself, without another frontier-model call | The model still reads the learned skill |
| Handing over what is new or uncertain | New, uncertain or sensitive work goes to the right model, tool or person | ExternalThe host agent’s job |
| Planning, simulation and recommendations | NativeIt predicts what happens next, tries alternatives, builds plans and recommends actions | ExternalThe host agent’s job |
| Many agents, working together | It sends work to the right agent, splits it within your rules, keeps each agent to its scope and brings the results together | Shares lessons across connected agents |
| Learning which tools work | It learns which tools work, when to use them and what each one leads to | Tool traces can become reusable procedures |
| Tool actions within your rules | Tools run only through your rules and permissions | Controlled by the host coding agent |
| Streaming | It streams model and tool output, with every step still within your rules | Depends on the host agent |
| Work that carries on | ||
| Learning from every step, automatically | Every step, through MCP + hooks or the plugins, feeds one shared learning loop | Agent sessions and tool activity are captured automatically |
| Context across sessions | NativeWork, decisions, tasks and the context that matters carry on across sessions | NativeShared memory outlives agent sessions |
| Long-running work | NativeLong work stays open across sessions and agents, with safe points to go back to, until it is finished or forgotten | Session memory helps pick work back up |
| Across models | NativeWhat it learns and your context stay outside the model, and follow the work across providers | Strong: skills and memory can be reused by different coding agents |
| Across apps and agents | The same work carries on across Claude, Codex, Cursor, Hermes and your own agents | Shared memory connects Claude Code, Codex, Cursor and other agents |
| Handing work to another agent | A task and its context pass to another agent you allow, ready to continue | Workspace skills pass lessons between agents |
| Keeping context compact | Context stays compact, focused on the rules, tasks and decisions that matter | Sessions are distilled into compact skills |
| Learning from images, audio, video and PDFs | It learns from images, audio, video and PDFs, as it does from text | Focused mainly on sessions and tool traces |
| Across media | A question in words can draw on what was learned from images, audio and video, and media can shape later decisions | Outside Hivemind’s main focus |
| People, teams and sharing | ||
| For each person | NativeEach person gets their own private context, preferences and learning | Available through personal or workspace scope |
| For each team | NativeTeams share approved learning, and private work stays private | Shared workspace memory and skills |
| Across the organisation | NativeWhat works can move from one person to the team and the organisation, under your rules | Workspace lessons can spread across connected agents |
| Sharing between people, under control | You choose what is shared and with whom, and can withdraw it; anything shared shows who it came from | Workspace access controls |
| Protection for shared content | What someone shares arrives as information, never as instructions, so it can’t steer another person’s agent | Relies on how skills load, and on trust in the workspace |
| Control and trust | ||
| Your rules | Your rules are part of how Duplex works: they allow, decline or limit real actions | Skills guide the agent, but enforce nothing on their own |
| Rules applied before each action | NativeThe hooks check each action against your rules before it runs | Stays with the host agent |
| People in the loop | A person confirms the actions you choose, and what Duplex learns can be reviewed and undone | Skills can be inspected and edited before reuse |
| A history you can check | A history of every decision, where it came from and what came of it, that can’t be quietly changed | Skills linked to sessions, and stored traces |
| Receipts you can verify | Decisions, skills and deletions can come with receipts you can verify | Skill files linked to their source sessions |
| Undoing what was learned, exactly | NativeOne learned change can be undone exactly, without retraining anything | Skill files can be edited or removed |
| Forgetting that reaches everything built on it | Removing something also removes what came from it: context, anything shared from it and related learning | Means removing the affected sessions and generated skills |
| Keeping customers apart | Every user, team, organisation and customer is kept apart, in every integration and in everything it shares | Memory and skills scoped to a workspace |
| Where everything learned came from | For everything it learned, you can see its source, its scope and how far it can be trusted | Skills can link back to their source sessions |
| Where it runs and connects | ||
| Any model provider | One intelligence across OpenAI, Anthropic, Google, AWS and compatible models | Works across several coding agents and their models |
| MCP | NativeThrough MCP + hooks, Duplex joins every step your agents take | Connects with major coding agents; how varies by client |
| Hooks and plugins | NativePlugins for Claude, Codex, Cursor and Hermes, and hooks that bring Duplex in at each step and check sensitive tool actions | Automatic capture of coding-agent sessions |
| Your cloud, or self-hosted | Vareon Cloud, your cloud or no cloud: private cloud, your own account, self-hosted or air-gapped | Local agent integration; deployment options vary |
| No cloud: air-gapped | Built for deployments where nothing may leave, with local models, local dependencies and your own identity provider | Depends on the chosen deployment and model services |
| Cost, speed and fit | ||
| How it lowers model cost | Duplex answers familiar work itself, with no repeated frontier-model call | Better skills cut repeated mistakes, but the model still runs |
| How it lowers latency | Decisions it is sure of come back fast; uncertain work still goes to your frontier model | Skills shorten repeated discovery |
| Survives a model upgrade | YesLearning, context, tasks and rules stay outside any one model | YesSkills survive a change of coding agent or model |
| Fine-tuning needed | NoFine-tuning becomes an optional step up | No |
| Best for | Organisations that need their AI agents to learn, act within rules, carry work forward and improve across models, as one system | Teams that want coding agents to share sessions and reuse lessons automatically |
The other comparisons.
- Duplex and LettaStateful agents with memory and skills
- Duplex and CopilotKit CLiFReal workflows, turned into skills
- Duplex and memory platformsMem0, Zep, AgentCore Memory, Supermemory
- Duplex and fine-tuningFine-tuning and reinforcement fine-tuning
- Duplex and AI gatewaysPortkey, LiteLLM and similar
- The full comparisonDuplex beside all six, side by side
See Duplex on your own work.
Compare it where it counts: a pilot puts Duplex on your own work, with the measures agreed before it begins.