Memharness vs OSymandias

Memharness and OSymandias 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.

Memharness

The core premise is storing facts, not strings, with two independent time axes: when something became true in the world and when the agent learned it — so querying past agent states is a real query, not archaeology through logs. Everything lives in a single SQLite file, which means the storage layer makes zero LLM or network calls and stays auditable. Recall combines hybrid vector search and full-text search with a source-staleness signal, so older or superseded sources rank down automatically. Where it breaks: the SQLite backend is a hard ceiling for teams expecting distributed writes or high-concurrency production deployments. Teams hitting that ceiling will need to treat memharness as a pattern to port, not a service to scale horizontally.

OSymandias

The project ships a self-hosted runtime built on FastAPI, Celery, PostgreSQL, Redis, RabbitMQ, and Qdrant, so you get job scheduling, DAG orchestration, shared memory, tool execution, and a real-time dashboard without stitching services together manually. A Python SDK lets you define agents, attach tools, and wire multi-agent plans through goal decomposition — the runtime handles the queuing and dependency resolution. That stack is genuinely useful for research pipelines or internal analysis workflows where you control the infra. The ceiling appears when you need a managed hosted option: there is none, which means your team owns every database migration, worker restart, and Redis failover.

Attribute	Memharness	OSymandias
Pricing	Free	Free
Free trial	No	No
Open source	Yes	Yes
Has API	Yes	Yes
Self-hosted option	Yes	Yes
Platforms	SQLite, MCP	—
Pros	Bi-temporal storage tracks both world-time and agent-learn-time independently, so you can reconstruct exactly what the agent believed at any past moment — which means post-incident reviews and compliance audits have an actual record to query instead of inferring from logs. Provenance-scoped deletion lets you remove all facts derived from a specific source in one operation, so GDPR takedown requests or source revocations do not require a full memory wipe that destroys unrelated facts. The storage layer makes zero LLM or network calls, so memory reads and writes have no latency dependency on external APIs and no token cost — which means memory operations do not blow your inference budget. Hybrid vector-plus-full-text recall with a built-in staleness signal means older or superseded sources rank lower automatically, so the agent surfaces the most current relevant facts without you writing custom re-ranking logic. MCP exposure and a self-hosted SQLite backend mean the tool drops into any agent stack that speaks MCP without requiring a separate managed service, so you retain full data ownership and avoid a vendor dependency in the memory layer.	Full backing stack (PostgreSQL, Redis, RabbitMQ, Qdrant, Celery workers) launches from a single command, so your team skips the two-day infrastructure assembly that normally precedes first agent run. DAG-based job scheduling with dependency resolution, which means multi-step agent workflows that must run in order don't require you to hand-roll sequencing logic or poll for completion. Shared vector memory via Qdrant across all agents, so agents in the same pipeline can read each other's outputs without passing state through environment variables or custom databases. LiteLLM in the call path for provider routing, so switching from one LLM provider to another when costs or rate limits change is a config edit rather than a refactor. MIT license with full self-host support, which means you can run this on air-gapped infrastructure or embed it in a commercial product without negotiating a license or sending data to a third-party host.
Cons	SQLite is a single-writer database: the moment two agent processes attempt concurrent writes — a parallelized pipeline, a multi-worker deployment, any architecture where more than one process holds the file — writes will collide or block. Teams with concurrent-write requirements either serialize all memory operations through a single process (adding a bottleneck) or abandon memharness for a Postgres- or Redis-backed alternative. The project has 2 stars and 1 fork on GitHub at time of curation, with 19 commits and no open issues, which means community-sourced debugging, third-party integrations, and production war stories are essentially nonexistent. Teams that hit an edge case are reading the source, not a Stack Overflow thread. There is no built-in access control or multi-tenant isolation: if multiple agents or users share the same SQLite file, provenance-scoped deletion could become a liability rather than a feature — one delete call wipes facts for every tenant who learned from that source. Teams building multi-user applications will need to implement per-user database files or a sharding layer before going to production.	You are operating five production services (PostgreSQL, Redis, RabbitMQ, Qdrant, Celery) from day one — when any one of them degrades under load, requests start queuing or agents stall mid-DAG, and there is no managed failover. Teams without dedicated infra engineers hit this wall during their first high-volume run and migrate to a hosted platform rather than debug distributed systems alongside their agent logic. The project has five GitHub stars and zero forks at the time of listing, which means community-sourced workarounds, third-party integrations, and tested upgrade paths are essentially nonexistent — when you hit an undocumented edge case, you are reading source code, not Stack Overflow. There is no commercial hosted tier, so any team that needs to hand off infrastructure responsibility entirely — a common requirement once a prototype moves toward a customer-facing deployment — must either build their own hosting layer or switch to a platform that offers one.

Bottom line

Memharness and OSymandias 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 Memharness and OSymandias?

Memharness is Free and open source, while OSymandias is Free and open source. Compare pricing, free trial, API, platforms, and pros/cons in the table above on AIDiveForge.

Is Memharness better than OSymandias?

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.