Lobu vs Myco Brain

Lobu and Myco Brain are both agent frameworks tracked by AIDiveForge. Below is a side-by-side comparison of pricing, capabilities, platforms, and ownership — sourced from each tool's live website and verified before publishing.

Lobu

Lobu connects to over 50 data sources — HubSpot, Stripe, Zendesk, Snowflake, GitHub, and more — and builds a live memory layer that agents query on schedule rather than on demand. A 'watcher' definition tells the agent what to look for and when to pause for a human to sign off before anything ships. That approval-before-action model is what makes the autonomous scanning safe enough to actually run unsupervised. The ceiling shows up when your workflow needs logic that doesn't fit a watcher definition — at that point you're writing connector SDK code and maintaining it yourself. Teams with deeply custom data pipelines will feel that constraint before teams running standard SaaS stacks.

Myco Brain

The core mechanic is deterministic writes: the application code writes facts to Myco's Postgres store, not the LLM, so every stored fact carries a source document, a confidence score, and a full audit trail queryable via brain_why. One MCP server exposes that memory to Claude Code, Cursor, Codex, Windsurf, and any other MCP-compatible client simultaneously — write from Claude Desktop, retrieve from Cursor, no sync step required. The vendor publishes a 500-question LongMemEval result and a recall@5 figure using a recency reranker, both on the full benchmark set. The hard ceiling appears when your agents need to act on what they remember — Myco stores and retrieves facts; it does not plan, route, or execute tasks, so orchestration logic lives elsewhere.

Attribute	Lobu	Myco Brain
Pricing	Paid	Free
Free trial	No	No
Open source	Yes	Yes
Has API	Yes	No
Self-hosted option	Yes	Yes
Platforms	Local, Docker, Kubernetes, Lobu Cloud	Postgres, Docker, MCP clients
Released	—	2026
Pros	Persistent shared memory across all connected sources, so multiple team members querying the same agent see consistent, evidence-backed context rather than each starting from a fresh prompt. Approval-before-action steps baked into watcher definitions, so agents can run unsupervised on a schedule without the risk of sending a customer-facing message or filing a report without a human signing off first. Over 50 pre-built connectors plus a Connector SDK for arbitrary data sources, so teams with non-standard stacks aren't blocked waiting for a native integration. Three deployment modes — local CLI, Docker/Kubernetes self-hosted, and managed cloud — using the same project config, so a team can prototype on a laptop and promote to their cloud without rewriting the agent definition. Open-source codebase with 13 public example workflows covering sales, legal, finance, and market research, so teams inherit tested patterns rather than building agent memory architectures from first principles.	Deterministic write path means the LLM never authors the facts stored in memory, so every retrieved fact links to a source document and confidence score — which means regulated teams get an audit trail without building one themselves. One MCP server shared across all connected clients, so a fact written from Claude Desktop is immediately readable by a Cursor agent without a sync job or intermediate API call. Full-stack boot with docker compose and no required API keys, so teams evaluate and prototype without committing credentials or cloud spend before the architecture is validated. Content-hash deduplication on document ingestion, so re-importing the same ChatGPT or Claude export twice does not corrupt or inflate the memory store. Graph queries over entity relationships via the built-in tools, so agents can retrieve not just isolated facts but the web of connections between people, decisions, and documents in the store.
Cons	Watcher definitions are goal-and-approval constructs, not branching pipelines — there is no built-in way to say 'if the contract risk is high, route to legal; if medium, route to the account owner.' Teams that need that decision tree write it in the Connector SDK, which means owning and testing a custom code layer alongside the Lobu config. Teams whose core requirement is conditional routing between multiple agents — not monitoring and drafting, but complex multi-step task pipelines — will hit the watcher model's ceiling early and migrate to a dedicated agent orchestration framework. The memory and connector infrastructure doesn't transfer; the switch is a full rebuild. The managed cloud offering is a paid-only feature with no pricing details published on the vendor page, so teams trying to size budget before committing to a proof of concept must contact the vendor directly — a friction point that slows evaluation for organizations that require procurement approval before a pilot.	Myco stores and retrieves facts — it has no planner, no task router, and no execution loop. Teams building agents that need to act on retrieved memory must implement that logic themselves, which means maintaining a separate orchestration layer alongside the memory layer. The self-hosted path requires running Postgres 16 with pgvector and managing that infrastructure. Teams without existing Postgres ops experience hit configuration and maintenance overhead that the single docker compose up does not eliminate long-term. Semantic search requires a local Ollama instance or an external embedding provider; teams without GPU-capable self-host infrastructure who want semantic recall beyond full-text search are blocked until the cloud offering exits beta — at which point they are evaluating a hosted product with a waitlist rather than a drop-in replacement. No API surface is exposed outside the MCP protocol, so teams whose agents run outside MCP-compatible clients cannot integrate without building a custom MCP wrapper — teams with that constraint typically move to a vector database with a standard REST or gRPC API instead.

Bottom line

Lobu is paid while Myco Brain is free; only Lobu exposes a public API. Choose based on which difference matters most for your workflow.

Frequently asked questions

What is the difference between Lobu and Myco Brain?

Lobu is Paid and open source, while Myco Brain is Free and open source. Compare pricing, free trial, API, platforms, and pros/cons in the table above on AIDiveForge.

Is Lobu better than Myco Brain?

It depends on your workflow. Use the side-by-side attributes (pricing, open source, API, self-hosted, platforms) to decide. AIDiveForge does not rank a universal winner — we publish verified facts so you can choose.