AutoLang vs Myco Brain

AutoLang 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.

AutoLang

Orbit wraps each agent run in a bounded loop: it pulls one task from a dependency-ordered backlog, hands it to whatever agent you've wired up, runs tests, lint, and type checks, and refuses to close the task until validation passes. Every run produces structured JSON — what the agent returned, how it scored against a rubric, whether a human should accept or re-queue. That audit trail is the point. The ceiling appears when your workflow needs anything beyond task-level sequencing: parallel agent execution, real-time dashboards, or integration with existing CI pipelines requires you to build the glue yourself.

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	AutoLang	Myco Brain
Pricing	Free	Free
Free trial	No	No
Open source	Yes	Yes
Has API	No	No
Self-hosted option	Yes	Yes
Platforms	Linux, macOS, Windows (Python)	Postgres, Docker, MCP clients
Released	—	2026
Pros	Validation gates block task closure until tests, lint, and type checks pass, so regressions that would have silently shipped surface inside the orbit instead of in production. Agent-neutral adapter contract means you can swap Claude for Codex behind the same harness and compare structured evaluation artifacts, so agent selection becomes a decision based on evidence rather than anecdote. Dependency-aware backlog sequencing ensures each agent run starts from a task whose prerequisites are already verified, which means the cascading failures that come from running tasks out of order stop accumulating. Four structured artifacts per run — result, evaluation, review recommendation, progress log — give compliance or audit teams a complete evidence trail without requiring post-hoc reconstruction. MIT licensed and self-hosted, so sensitive codebases never leave your infrastructure and there is no vendor dependency on a paid tier to retain audit history.	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	Orbit executes one task per orbit, sequentially. Teams that need agents working in parallel on independent tasks hit this ceiling immediately — there is no built-in concurrency model, and adding it means maintaining a scheduling layer outside the harness. Integration with existing CI pipelines — GitHub Actions, Jenkins, or similar — is not provided. Teams that need orbit results to gate pull requests or trigger deployments write the integration themselves, which becomes a second system to maintain alongside Orbit. The evaluation rubric scores task focus, completion, diff signal, and validation, but the rubric definitions are fixed to what the harness ships with. Teams whose quality criteria don't map to those dimensions either accept scores that don't reflect their standards or fork the evaluation logic — at which point they own a modified harness diverging from upstream. When a team's workflow grows beyond single-repo, dependency-ordered task queues — multi-team backlogs, cross-service agents, or real-time progress visibility — Orbit's intentional smallness becomes a hard constraint. That's the condition under which teams move to a broader agent orchestration platform and treat Orbit's artifact schema as a reference rather than a production harness.	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

AutoLang and Myco Brain are closely matched on pricing model, openness, and API availability — pick by feature set and platform support in the table above.

Frequently asked questions

What is the difference between AutoLang and Myco Brain?

AutoLang is Free 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 AutoLang 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.