Roampal

Roampal makes a narrow, defensible bet: the value of a memory is not how similar it sounds to the question, but whether it produced a useful result last time. That framing holds up in the vendor's own benchmark, where Roampal's end-to-end LoCoMo accuracy is 76.6% against 53.0% for raw ingestion, a 23.6-point gap reported at p<0.0001, with a non-adversarial ceiling of 85.8%. The mechanics are specific and inspectable. A sidecar LLM summarizes every exchange into a short record, extracts atomic facts as standalone memories, and tags nouns at store time. At query time TagCascade matches query words against the tag index, runs each matched tag through ChromaDB, counts tag overlap, and sorts candidates by tier — all tags matched first, then one fewer, cosine distance breaking ties — with straight cosine filling the pool if fewer than 40 candidates survive. A cross-encoder then reranks. Two lanes retrieve separately and merge: 4 summaries carry narrative, 4 atomic facts carry specifics. The scoring loop is the part most competitors do not have. Outcomes are detected from language like "that worked" or "that failed", and each result shifts a running score: worked +0.2, failed −0.3, partial +0.05. Score plus usage count drive promotion from working (24h) to history (30d) to patterns (permanent), with demotion when a pattern's score falls below threshold and timer-based expiry for un-promoted working and history memories. The weaknesses are as concrete as the strengths. Retrieval is capped at 4 summaries and 4 facts per turn, so a turn that needs more breadth than that won't get it. Scope is limited to Claude Code and OpenCode, with Ollama or LM Studio support sitting in the separate desktop app that also carries MCP tools. Everything is local in the free Core tier, which is the point, but it also means no cloud sync across machines. Quality of the memory store depends on the sidecar LLM's summarization and fact extraction, so a weak local model will degrade the thing you're trying to improve. And the headline numbers come from the vendor's own LoCoMo harness, not independent replication — treat 85.8% as a vendor-reported ceiling. Where it fits: developers who live in a coding agent across weeks-long projects, who want the assistant to stop re-suggesting approaches that already failed, and who would rather keep memory on disk than in someone's cloud. Where it doesn't: anyone who needs multi-device memory sharing, non-coding assistant memory, or a setup that avoids local model infrastructure entirely.