Show HN: Coding agent with algebraic memory (VSA) instead of RAG

A developer released Raidho, an open-source coding agent that uses a hybrid architecture with separate models for reasoning and execution, plus a durable algebraic memory (VSA) instead of RAG. The agent reduces costs by up to 70% on repeated tasks and supports provider-agnostic backends including Claude, DeepSeek, and OpenAI.

A coding agent that plans with one model, executes with another, and remembers what it learns. Most coding agents are one model in a tool loop. Raidho splits the work: use a smart, expensive model to reason and plan , a cheap, fast model to execute , and a durable memory that carries facts across runs — all provider-agnostic, with your own API key. The name is the rune Raidho ᚱ — "journey / movement". Status: alpha.Tested end-to-end live against both backends DeepSeek and Claude through the official Anthropic SDK, including the agentic tool-loop . A reproducible real-API benchmark ships in benchmarks/real task opus.py with full evidence evidence/2026-06-11 opus vs raidho/ : same task, same model — deterministic procedure $0.05 / context-first hybrid $0.116 / pure tool-loop $0.301; the hybrid matched the loop's report quality at ×2.6 less cost. APIs may change before 1.0. Reasoning ≠ execution. text mode reasoning, no tools and code mode agentic tool loop can run on different providers . Plan on Claude, grind on DeepSeek — you choose where the expensive thinking happens and where the cheap doing happens. Council mode. Have two providers debate a question and a neutral pass distill the consensus points of agreement, residual disagreements, recommendation — e.g. Claude vs DeepSeek. Depersonalized and provider-pluggable; no built-in personas. Durable, structural memory — persists across runs. The agent remembers subject, relation, object facts and recalls the relevant ones into its prompt each turn; it saves new ones itself via a remember tool, and council verdicts are distilled into facts automatically . Memory is written to disk per project <workdir /.raidho/memory and reloaded next run — so a decision reached today resurfaces tomorrow, recalled only when relevant cheap; no history bloat and across languages a Russian query finds an English fact . It's a Vector Symbolic Architecture VSA , not RAG: facts are composed algebraically, similarity is bit-packed 32× less RAM than float, identical ranking . You don't need to know any of that to use it — see docs/MEMORY.md /vitaliyfedotovpro-art/raidho/blob/main/docs/MEMORY.md if you want to. Gets cheaper with repetition opt-in . Turn on auto-distillation and a successful read-only tool-loop is captured as a deterministic procedure: the next similar task replaces the multi-iteration LLM loop with deterministic data-collection + one synthesis call. Heavily gated for safety read-only commands and pipelines only, a safety-verify pass, neutral fitness that sinks a bad procedure; writes always stay on the LLM path . Measured live deepseek-chat, evidence/2026-06-12 autodistill curve/ : the win scales with iteration overhead , not task size — a repeated multi-step task over small data dropped ×9.6 per repeat 70% over 5 runs , while a data-heavy audit cost dominated by file contents, few iterations to cut saved ~nothing. Honest rule: it removes repeated per-iteration context cost, not the cost of the data itself. Tiny and hackable. The memory core depends only on numpy ; the whole agent is a handful of files. Swap providers, tools, or the embedder without fighting a framework. Bring your own key. Claude default , DeepSeek, OpenAI, or any OpenAI-compatible endpoint. Guided recommended — one interactive script that explains every step, verifies your API key live, runs a real smoke test and shows how to use the agent concept: MavKa https://github.com/MozgAI/MavKa by MozgAI : bash install.sh Manual: pip install -e '. anthropic ' Claude backend official Anthropic SDK pip install -e '. openai-compat ' DeepSeek / OpenAI-compatible httpx pip install -e '. embed ' semantic memory sentence-transformers pip install -e '. dev ' + pytest Python ≥ 3.11. export CODER PROVIDER=deepseek export DEEPSEEK API KEY=sk-... coder "create a FastAPI hello-world app and run it" export CODER PROVIDER=deepseek execution code mode, tool loop export DEEPSEEK API KEY=sk-... export CODER REASON PROVIDER=anthropic reasoning text mode export ANTHROPIC API KEY=sk-ant-... coder REPL: /text plans on Claude, /code executes on DeepSeek The expensive model is used only where it earns its keep; the token-heavy tool loop runs on the cheap one. coder interactive REPL default mode: code coder "<task " headless: run one task, print result, exit In the REPL: /code agentic coding, /text reasoning chat, /ctx toggle context-first, /learn toggle auto-distill, /council <question two-provider debate → consensus, /quit to exit. Memory persists per project at <workdir /.raidho/memory — the REPL shows how many facts it loaded on start. python import asyncio from agent.providers import get provider from agent.loop import Session from agent.memory import AgentMemory reason = get provider {"provider": "anthropic", "api key": "sk-ant-..."} smart execute = get provider {"provider": "deepseek", "api key": "sk-...", cheap "model": "deepseek-chat"} path=... makes memory persist across runs omit it for an in-RAM, ephemeral memory memory = AgentMemory path=".raidho/memory" session = Session execute, workdir=".", memory=memory, reason provider=reason asyncio.run session.chat "plan how to add auth to this app" → reason provider asyncio.run session.code "implement the plan and add a test" → execution provider facts the agent stored are now on disk; a new Session path=... reloads them Omit reason provider and both modes use the single provider. python from agent.council import Council council = Council reason, execute, name a="claude", name b="deepseek" result = await council.consensus "pin exact deps or use ranges?", rounds=2 print result "verdict" points of agreement / residual disagreements / recommendation result "transcript" holds the full exchange Via a Session with memory, the verdict is auto-distilled into facts and stored: res = await session.council "pin exact deps or use ranges?" print res "remembered" e.g. "dependencies", "pinned", "exact" — recalled later Or Session ... .council "..." , which seats reason provider vs provider . | Variable | Meaning | Default | |---|---|---| CODER PROVIDER | execution provider: anthropic | deepseek | openai | openai-compat | anthropic | CODER MODEL | override execution model | provider default | CODER REASON PROVIDER | optional separate provider for text /reasoning | = CODER PROVIDER | CODER REASON MODEL | reasoning model | provider default | CODER BASE URL | endpoint URL for openai-compat | — | CODER CONTEXT FIRST | 1 packs the workspace into the first call fewer tool iterations | off | CODER AUTODISTILL | 1 learns read-only procedures from successful runs gets cheaper with repetition | off | CODER MEMORY | memory file path; off disables persistence | <workdir /.raidho/memory | ANTHROPIC API KEY / DEEPSEEK API KEY / OPENAI API KEY / CODER API KEY | API keys provider-specific first, then CODER API KEY | — | See docs/PROVIDERS.md /vitaliyfedotovpro-art/raidho/blob/main/docs/PROVIDERS.md for adding a provider and the auth hook. docs/ARCHITECTURE.md /vitaliyfedotovpro-art/raidho/blob/main/docs/ARCHITECTURE.md — components and data flow. docs/MEMORY.md /vitaliyfedotovpro-art/raidho/blob/main/docs/MEMORY.md — the VSA memory model and bit-packing. docs/OPENWEBUI.md /vitaliyfedotovpro-art/raidho/blob/main/docs/OPENWEBUI.md — drive Raidho from Open WebUI chat / council / code as selectable models . - Broader benchmark coverage success rate on a task set vs. single-model baseline; SWE-bench-style eval — a first real-API cost benchmark with evidence is already in benchmarks/ + evidence/ . - Streaming responses in the Open WebUI plugin currently the reply lands at once . Recently shipped: persistent memory across runs · council verdicts saved as facts · context-first mode · auto-picked semantic embedder · automatic Open WebUI setup. The bash tool runs unsandboxed in the working directory; in code mode the model decides which commands to run. Use Raidho only on code and tasks you trust — ideally inside a container or a throwaway directory. See SECURITY.md /vitaliyfedotovpro-art/raidho/blob/main/SECURITY.md . Dual-licensed: AGPL-3.0-or-later for open-source / research / non-commercial use, or a commercial license — see COMMERCIAL.md /vitaliyfedotovpro-art/raidho/blob/main/COMMERCIAL.md . See CONTRIBUTING.md /vitaliyfedotovpro-art/raidho/blob/main/CONTRIBUTING.md . Issues and pull requests welcome. — the web interface Raidho plugs into. It's an excellent, polished chat UI and a perfect fit for this agent; rather than reinvent it, Raidho ships a Pipe plugin and the installer can wire itself in automatically. Thanks to the Open WebUI team. Open WebUI https://github.com/open-webui/open-webui — this project's critic throughout its path: his reviews shaped the retry layer, the embedder honesty, the history budget and more. The guided installer Oles Lytvyn MozgAI https://github.com/MozgAI install.sh follows the concept he pioneered in— an installer that explains everything out of the box "AI installs itself" . MavKa https://github.com/MozgAI/MavKa