AI tool comparison
ds2api vs MemPalace
Which one should you ship with? Here is the side-by-side panel verdict, pricing read, reviewer split, and community vote comparison.
Developer Tools
ds2api
DeepSeek web sessions as drop-in OpenAI/Claude/Gemini APIs
50%
Panel ship
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Community
Paid
Entry
ds2api is a Go middleware that wraps DeepSeek's web chat interface and re-exposes it as fully compatible OpenAI, Claude, and Gemini API endpoints. Developers can point any existing SDK or tool that speaks these protocols at a local ds2api instance and get DeepSeek responses without rewriting a line of integration code. It handles multi-account pooling, per-account rate limiting, proof-of-work computation (which DeepSeek's web layer requires), and context management for long conversations. The architecture is surprisingly complete for a solo project: a Go backend for concurrency and protocol translation, a React management dashboard, Docker/Vercel deployment support, and compiled binaries for Linux, macOS, and Windows. It even adapts tool-calling semantics across different provider formats — a notoriously tricky edge case. The project has attracted nearly 3,000 GitHub stars and 461 in a single day, suggesting real demand for free or cheap DeepSeek access routed through familiar APIs. The catch: DeepSeek's ToS doesn't allow automated web scraping, and the README explicitly limits use to "learning and internal verification." That said, the technical execution is impressive and the architecture is worth studying regardless.
Developer Tools
MemPalace
Persistent cross-session memory for any LLM — local, free, 96% LongMemEval
75%
Panel ship
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Community
Free
Entry
MemPalace is a free, open-source AI memory system that gives large language models persistent, cross-session memory. It accumulated over 43,000 GitHub stars within a week of launch — one of the fastest open-source AI project takeoffs of 2026. Unlike systems that use AI to summarize memories (lossy by design), MemPalace stores all conversation data verbatim and uses vector search via ChromaDB and SQLite to retrieve relevant memories. The storage metaphor is architecturally literal: people and projects become 'wings', topics become 'rooms', and original content lives in 'drawers' — enabling scoped search rather than flat corpus retrieval. Memory retrieval costs just ~170 tokens, making it practical even in cost-sensitive deployments. On the LongMemEval benchmark it scores 96.6% raw (100% in hybrid mode, though the hybrid methodology has faced some independent scrutiny). It runs entirely locally at zero API cost, meaning no cloud dependency and no privacy leakage. The project has been independently validated on production agentic workflows and is already being integrated into agent frameworks.
Reviewer scorecard
“If you have a DeepSeek account and want to use it through your existing OpenAI-compatible stack, this is the cleanest solution I've seen. The multi-account pooling and automatic rate-limit handling are genuinely thoughtful engineering.”
“Verbatim storage avoids the lossy-summary trap that plagues most memory systems. ChromaDB + SQLite locally is a practical stack with minimal operational overhead, and the 170-token retrieval cost is genuinely low. Worth evaluating before paying for any memory-as-a-service layer.”
“This is web scraping dressed up as an API — and DeepSeek's ToS explicitly forbids it. You're one UI update away from your middleware breaking entirely. For production use, just pay for the official API; it's already cheap.”
“The 100% hybrid LongMemEval score was achieved through targeted fixes for specific failing test cases, and independent reviewers have flagged methodology concerns. 43K GitHub stars in a week is hype velocity, not production validation. Wait for real-world deployments before betting critical workflows on this.”
“This pattern — wrapping web interfaces as protocol-compatible APIs — is going to proliferate as AI providers fragment. ds2api is an early proof-of-concept for a class of tools that lets developers treat the web as an API surface.”
“Persistent local AI memory is the missing infrastructure layer in most agent architectures. MemPalace's hierarchical 'palace' structure — wings, rooms, drawers — is a more principled approach to memory organization than flat vector search, and it points toward how agents will eventually manage long-horizon knowledge.”
“As someone who builds content pipelines, the ToS uncertainty makes this a hard pass for anything customer-facing. The Go architecture is slick but the legal exposure isn't worth it for a production tool.”
“Being able to pick up a creative project where you left it — with full context intact across sessions — fundamentally changes how AI fits into long-duration creative work. Local storage means zero privacy leakage. This is the boring infrastructure that unlocks actually useful creative AI workflows.”
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