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Memharness vs ProData AI

Which one should you try?

Memharness

Free

Best for Agents with months of evolving facts

Visit Memharness See full review

ProData AI

Free

Best for Teams automating code generation with multiple agents

Visit ProData AI See full review

Pricing, capabilities and platforms side by side. Prices are as of the "Pricing checked" date in the table.

Memharness

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 Recall combines hybrid vector search and full-text search with a source-staleness signal, so older or superseded sources rank down automatically.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.

ProData AI

ProData AI

Orbit is an open-source harness that wraps AI coding agent runs in a fixed loop: pick a task from a dependency-ordered backlog, run the agent, validate the output against tests, lint, and type checks, then record structured evidence before the task closes. Nothing advances without proof. Each run produces four artifact files — agent output, rubric scores, a recommendation, and a human-readable log — so you can inspect exactly what happened without replaying the whole session. The harness is agent-neutral; Claude, Codex, Cursor, or any JSON-speaking CLI plugs in behind the same contract. The ceiling appears quickly on teams who need anything beyond the validation-gate model — custom orchestration, parallel agent execution, or UI-driven workflow design are not in scope.

AttributeMemharnessProData AI
PricingFreeFree
Pricing checkedNot checked yetNot checked yet
Free trialNot statedNot stated
Open sourceYesYes
Has APIYesNo
Self-hosted optionYesYes
PlatformsSQLite, MCPPython (CLI), agent-agnostic
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.
  • Validation gates block task completion until tests, lint, and type checks pass, so an agent cannot silently mark work done on a broken diff — the kind of silent failure that compounds across a backlog.
  • Four structured artifact files per run (agent output, rubric scores, recommendation, progress log), which means you have a concrete audit trail when something goes wrong instead of reconstructing what the agent did from git history.
  • Agent-neutral JSON contract, so switching the underlying coding agent — from Claude to Codex or a local model — does not require rewiring the workflow, which means you can benchmark agents against the same task set and compare artifacts instead of impressions.
  • Dependency-ordered backlog selection keeps each run focused on one task at a time, which prevents agents from scope-creeping across unrelated files and makes the diff signal meaningful.
  • MIT-licensed and self-hostable with no external API required for the replay demo, so you can evaluate the full loop and inspect what it records without exposing credentials or production code to a third-party service.
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.
  • Parallel agent execution is not in scope — the harness runs one orbit at a time in sequence. Teams with large backlogs who need multiple agents working concurrently will find Orbit serializes what their workflow requires to parallelize, and they will either script around it or move to a purpose-built multi-agent orchestration layer.
  • There is no UI, no workflow canvas, and no non-engineer interface. Configuration is CLI and JSON. A product manager or QA lead who needs to inspect or adjust the backlog without engineering support cannot do so — teams in that situation add a wrapper or abandon the tool for something with a visual layer.
  • The artifact schema and rubric scoring are fixed by the harness design. Teams with domain-specific validation requirements beyond tests, lint, and type checks — for example, semantic correctness checks or business-rule assertions — must write custom adapter logic. The docs describe this as a contribution path, but it is engineering work that falls outside the core harness.

Made up your mind?

Memharness

Free

Best for Agents with months of evolving facts

Visit Memharness See full review

ProData AI

Free

Best for Teams automating code generation with multiple agents

Visit ProData AI See full review

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Specs come from each tool's own site and docs (how we test). AIDiveForge is editorially independent.

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