MemLedger vs Myco Brain

MemLedger 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.

MemLedger

The vendor describes MemLedger as a memory framework with an audit trail: every stored fact carries provenance, so when an agent surfaces a stale or wrong preference you can trace the extraction decision that created it. The library includes a policy layer — a `memory.policy.yaml` file — that lets teams quarantine unverified facts before they reach permanent knowledge, which means bad data from one session doesn't silently corrupt the next. An evaluation suite ships alongside the core library, so you can benchmark how well a newer extraction model rebuilds memories from raw history before you migrate. The ceiling appears quickly for teams that need hosted infrastructure, multi-agent coordination, or anything beyond a Python library integration — there is no API, no managed service, and no UI.

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	MemLedger	Myco Brain
Pricing	Free	Free
Free trial	No	No
Open source	Yes	Yes
Has API	No	No
Self-hosted option	Yes	Yes
Platforms	Python	Postgres, Docker, MCP clients
Released	—	2026
Pros	Fact provenance is recorded at extraction time, so when an agent surfaces a wrong user preference you can trace which session and which extraction decision created it — instead of rebuilding that history manually from logs. A policy file (`memory.policy.yaml`) gates unverified facts into quarantine before they reach permanent storage, which means a bad inference from one session cannot silently overwrite trusted knowledge without clearing the policy condition. An evaluation harness ships with the library, so you can measure how accurately a newer extraction model rebuilds memories from raw conversation history before committing to a migration — rather than discovering regressions in production. MIT license and fully self-hosted, which means the memory store never leaves your infrastructure — relevant for any project where conversation history carries PII or is subject to data residency requirements. The repository includes prompt templates and example integrations, so the extraction logic is inspectable and replaceable rather than hidden behind a managed service you cannot audit.	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	No API surface exists: every system that needs to read or write memories must be a Python process or maintain its own wrapper, which blocks integration from non-Python services and rules out MemLedger entirely for polyglot architectures. The repository carries seven commits and six stars at curation time — when you hit an edge case in the extraction logic or the policy evaluation, there is no active community to file against and no track record of issues being resolved; teams with production SLAs typically switch to a maintained framework like Mem0 or a managed vector store with custom metadata fields. Persistence infrastructure is entirely the caller's responsibility: the library does not ship a storage backend, so before a single memory is written you are deciding and operating a database, which adds scope to any project that expected a drop-in solution. The quarantine-to-permanent promotion model requires someone to define and maintain the policy file — teams without a clear owner for that configuration tend to disable the gate, which removes the auditability feature the library was chosen for.	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

MemLedger 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 MemLedger and Myco Brain?

MemLedger 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 MemLedger 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.