memQL vs. Other Harnesses
This is meant to be fair. memQL is not the right tool for every job, and the projects below are good at what they do. The point of this page is to make the category difference clear so you can choose well.
The category difference
Most "agent frameworks" are libraries / SDKs: you import them, wire chains/agents/tools in your own code, bring your own persistence, and add your own guardrails, multi-process coordination, and observability.
- Python: LangChain / LangGraph, LlamaIndex, and the surrounding glue. Huge ecosystems, every integration imaginable — and you assemble and operate the system.
- Go: the field is growing fast — Google ADK, Firebase Genkit, LangChainGo, ByteDance's Eino, and others. These are well-built Go-native libraries for composing model calls, tools, and flows.
memQL is a different category: a harness + memory substrate that runs. The loop, the persistent memory graph, the cost/safety enforcement, the multi-node mesh, the identity layer, and the observability are the product — not things you assemble on top.
A useful one-liner: the others give you the pieces to build an agent runtime; memQL is the agent runtime.
Comparison
| Capability | Library/SDK (Genkit, LangChainGo, Eino, LangChain) | memQL |
|---|---|---|
| Shape | Library you import + wire | Runtime + DSL you declare against |
| Persistent memory | BYO (pick a store, wire it) | Built in: append-only time-series graph, provenance, replay |
| Memory consolidation | DIY | Episodic → semantic, recency+semantic scoring |
| Cost/loop guardrails | DIY | Built in: global rate ceiling, per-plan budgets, loop breakers, approval gate, model tiering |
| Behavior definition | Code (chains/flows) | Declarative DSL (concepts, automations, tools, prompts, specs) |
| Multi-node coordination | DIY | Built in: node mesh, event bridge w/ dedup+TTL |
| Identity / authz | BYO | Built in: identity service, JWT/JWKS, per-row authz (test-enforced) |
| Observability | BYO | Built in: per-invocation hypertable + Cockpit topology |
| Footprint | Small, no database required | A real system: PostgreSQL + TimescaleDB |
| Language | Go (or Python) | Go engine + the MemQL DSL |
Where the others are a better fit (honestly)
- You want a small dependency, no database. A Go library (Genkit, Eino) drops into an existing service with a fraction of the surface. memQL is a system with a Postgres/TimescaleDB substrate — that is the point, and the cost.
- You need the widest provider/integration catalog today. The Python ecosystem still has the longest tail of connectors.
- You're prototyping a single, stateless flow. If there is no durable memory, no fleet, and no budget risk, a harness is overkill — reach for a library.
- Maturity. memQL is honestly pre-1.0 (versioning policy: git-tag semver, 1.0 at the beta); the established libraries have more miles on them.
When to choose memQL
Choose memQL when the hard parts are the point:
- agents that must remember across sessions and restarts, with provenance;
- workloads where an unbounded loop or a stuck model is a real cost or safety risk;
- multi-agent / multi-node systems you'd otherwise have to coordinate by hand;
- a product you intend to operate and inspect, not just demo;
- a team that wants behavior expressed declaratively and versioned, not buried in glue.
And the standing proof that those are solved problems and not roadmap items: memQL runs CoPresent today. See Why memQL Is a Harness, Not a Library for the code behind each claim.