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algorithm SDK for coding agents — prompt externalization, Python REPL with symbolic recursion, code-driven navigation","maintainers":[{"name":"namastex888","email":"genie@namastex.ai"}],"readme":"# rlmx\n\nRLM algorithm CLI for coding agents — prompt externalization, Python REPL with symbolic recursion, code-driven navigation.\n\nBased on the [RLM paper](https://arxiv.org/abs/2501.12599) (REPL-based LLM Method). Uses [pi/ai](https://github.com/nickarora/pi-ai) as the multi-provider LLM client.\n\n## Production validation (2026-04-22)\n\nThe SDK (`rlmx.sdk.*`) is production-validated via its first consumer,\n`khal-os/brain`, a multi-agent pipeline over WhatsApp / long-form\narchives. Three agent bridges run through `sdk.runAgent()`:\n\n| bridge | role | slate | status |\n|---|---|---|---|\n| L1 triage | worth-processing filter | 30 windows | **SHIP 30/30** structural match vs legacy path |\n| L2 preservation | multi-step extraction + brain mutation | 24 windows | **SHIP at variance ceiling** (baseline×baseline ≈ baseline×bridge) |\n| L3 audit | sampled self-audit | slate in flight | pending verdict |\n\nEvidence depth (metadata only — no content):\n\n- Dogfood reports live in the brain repo under\n  `brain-lab/rlmx-sdk-bridge-report/` (L1) and\n  `brain-lab/rlmx-sdk-bridge-report-l2/` (L2). Each carries a\n  `SHIP-decision.md` with the baseline-vs-bridge delta table + stop-\n  reason distribution.\n- Event streams, permission hooks, validate-with-retry, and session\n  checkpoints are all exercised per-window. Cost and latency are\n  captured per iteration.\n- Brain's bridge pattern (an outer `IterationDriver` wrapping the\n  legacy pi-agent loop) is a reusable template for consumers that\n  want to migrate a working agent into the SDK without rewriting\n  its internals. See `src/agent/rlmx-bridge.ts` in `khal-os/brain`\n  for the reference implementation.\n\n**Stability stamp:** `schema_version: 1` + `tools_api: 1` are the\nfields every bridge has shipped against. See\n[`docs/agent-yaml-schema.md`](docs/agent-yaml-schema.md) for the\nschema itself.\n\n## Install\n\n```bash\nnpm install -g rlmx\n```\n\n## Quick Start\n\n```bash\n# Scaffold config files in current directory\nrlmx init\n\n# Run a query\nrlmx \"What is the meaning of life?\"\n\n# Query with context (directory of docs)\nrlmx \"How does IPC work?\" --context ./docs/\n\n# Query with a single file as context\nrlmx \"Summarize this paper\" --context paper.md --output json\n\n# Pipe data in\ncat data.csv | rlmx \"Analyze this dataset\"\n```\n\n## SDK (`rlmx.sdk.*`)\n\nrlmx also ships a programmatic SDK for consumers that need to drive\nagents from code — with per-iteration observability, permission\nhooks, validate-with-retry, session checkpointing, and a pluggable\ntool registry. The CLI path above is untouched; the SDK is purely\nadditive.\n\n```ts\nimport { sdk } from \"@automagik/rlmx\";\n\nconst spec = await sdk.loadAgentSpec(\"./my-agent\");\nconst registry = sdk.createToolRegistry();\nawait sdk.registerRtkTool(registry);\nawait sdk.loadPluginTools(spec, registry);\n\nfor await (const ev of sdk.runAgent({\n\tagentId: \"my-agent\",\n\tsessionId: \"s-1\",\n\tinput: \"what's new?\",\n\tdriver: sdk.rlmDriver({\n\t\tmodel: { provider: \"google\", model: \"gemini-2.5-flash\" },\n\t\tsystem: await Bun.file(\"./my-agent/SYSTEM.md\").text(),\n\t}),\n\ttoolRegistry: registry,\n})) {\n\tconsole.log(ev.type, ev.timestamp);\n}\n```\n\nDeeper dives:\n\n- [`docs/sdk-overview.md`](docs/sdk-overview.md) — layered architecture + design principles.\n- [`docs/events.md`](docs/events.md) — the 12-event catalogue + emitter contract.\n- [`docs/tool-authoring.md`](docs/tool-authoring.md) — TS/MJS + Python plugin recipes, RTK integration.\n- [`docs/agent-yaml-schema.md`](docs/agent-yaml-schema.md) — `agent.yaml` field reference.\n- [`examples/`](examples/) — three runnable example agents (hello-world / research-agent / brain-triage) with smoke tests.\n\n## Live event stream (`rlmLoop({ emitter })`)\n\nA real recursive `rlmLoop` run emits an observable, in-memory event\nstream over the SDK's `createEmitter()` bus. This is the shared substrate\nthat the **rlmx-acp adapter** (web client) and **pi's native TUI** consume\nas renderers — rlmx itself ships the events, not a UI.\n\n### Subscribing (the contractual seam)\n\nPass a `createEmitter()` you have **already subscribed to** as\n`rlmLoop`'s `emitter` option, so you receive events from the first\nemission. Omit it and the run uses an internal emitter. The run closes\nthe emitter when it finishes (a `SessionClose` event, then the iterator\nreturns).\n\n```ts\nimport { createEmitter } from \"@automagik/rlmx/sdk\";\nimport { rlmLoop } from \"@automagik/rlmx\";\n\nconst emitter = createEmitter();\n\n// Subscribe BEFORE the run starts.\n(async () => {\n\tfor await (const ev of emitter) {\n\t\tconsole.log(ev.type, ev.correlationId, ev.parentRunId);\n\t}\n})();\n\nawait rlmLoop(query, context, config, { emitter }); // same signature the rlmx-acp adapter uses\n```\n\n### Event schema\n\nEvents reuse the existing SDK `AgentEvent` union (see\n[`docs/events.md`](docs/events.md)) — **no schema fork**. On the recursion\npath every event additionally carries two optional ancestry fields:\n\n| field | meaning |\n| --- | --- |\n| `correlationId` | Stable id of the node the event belongs to. For a spawned child it is the sortable `uuidv7()` minted at the spawn site; for a run's own iterations it is that run's self-correlation id. |\n| `parentRunId` | The `correlationId` of the parent node — the ancestry edge (maps to the child process's `RLMX_PARENT_RUN_ID`). Absent for the true root. |\n\n**Build the recursion tree by keying on `correlationId` / `parentRunId`,\nnot `depth`** — `depth` cannot disambiguate sibling branches at the same\nlevel, but two siblings of one parent always get distinct `correlationId`s.\n\nPer-run producers and what they emit:\n\n- `AgentStart`, `SessionOpen` — once at run start.\n- `IterationStart`, `IterationOutput` — per parent iteration. `IterationOutput.metrics` carries per-node `latencyMs` / `toolCalls` / `costUsd` / `tokens` (incl. `reasoning`) from the wired `MetricsRecorder`, plus `responseModel` when the provider reports it.\n- `ToolCallBefore` / `ToolCallAfter` — around each REPL execution.\n- `Recurse` — **one per recursive `rlm_query` spawn**, carrying the child's `correlationId` and `parentRunId` (this run) — the ancestry edge.\n- `IterationOutput` (child-completion) — bridged from `RlmChildResult.usage` when a child settles: the child node's cost / tokens / latency, keyed by its `correlationId`. A failed child additionally emits an `Error` (`phase: \"recurse\"`).\n- `EmitDone`, `SessionClose` — once at run end.\n\n### Headless subscriber (reference consumer)\n\n[`scripts/watch-headless.mjs`](scripts/watch-headless.mjs) is the reference\nconsumer: it logs one compact JSON line per event (so `\"type\"` is\ngreppable) and reconstructs the recursion tree by `correlationId`.\n\n```bash\n# Deterministic proof (no credentials needed): a >=2-level recursive run\n# with sibling branches at two levels.\nnode scripts/watch-headless.mjs -- \"decompose and recurse on each part\"\nnode scripts/watch-headless.mjs -- \"…\" | grep -c '\"type\":\"Recurse\"'   # one line per spawn\n\n# Against a real run (needs a working model endpoint / provider key):\nRLMX_HEADLESS_REAL=1 node scripts/watch-headless.mjs -- \"your prompt\"\n```\n\n### Scope note\n\nLevel-2 **live child-internal** streaming — a child's own iterations\nsurfaced to the parent bus over `src/ipc.ts` — is **not** included. A\nrecursive child is summarized as a single bridged completion node; the\nroot subscriber sees its direct spawns, and a collector aggregating the\nprocess tree reconstructs deeper levels by `correlationId`. Streaming\nchild-internal events into the parent stream is the documented next step.\n\n## ACP agent (`rlmx acp`) — wire rlmx into an ACP host\n\n> **⚠️ Experimental.** `rlmx acp` works and is tested end-to-end, but its\n> protocol surface may change without a deprecation cycle, and v1 **serializes\n> prompt turns within a single active session** — a concurrent\n> `session/prompt` is rejected with JSON-RPC `-32600` rather than queued. Every\n> other part of rlmx documented here is stable.\n\n`rlmx acp` is a stdio [Agent Client Protocol](https://agentclientprotocol.com)\nagent: it speaks newline-delimited JSON-RPC over **stdin/stdout**, so any ACP\nhost (Tidewave, Zed, Newio, …) can drive a real `rlmLoop` and render its live\nevent stream. No network port, no daemon — the host spawns the process and owns\nits lifetime.\n\n### One-time: find your absolute launch command\n\nEvery host entry needs the **absolute** path to the built CLI. Compute it once:\n\n```bash\n# From a clone of this repo:\nnpm ci && npm run build\nnode -e \"console.log(require('path').resolve('dist/src/cli.js'))\"\n# → /ABS/PATH/TO/rlmx/dist/src/cli.js   ← use THIS everywhere below\n```\n\nThe launch command is always: **`node /ABS/PATH/TO/rlmx/dist/src/cli.js acp`**\nrun **with `cwd` set to the project you want rlmx to operate in** (rlmx loads\n`.rlmx/rlmx.yaml` from `cwd`, exactly like the CLI). Substitute your real\nabsolute path for `/ABS/PATH/TO/rlmx` in every snippet — nothing else changes.\n\n> Sessions are durable. rlmx persists each ACP session (conversation history +\n> cwd + config snapshot + any host MCP config) to `~/.rlmx/acp-sessions/<id>.json`,\n> so `session/load` and a follow-up prompt keep working **after the host\n> restarts the agent** — no \"Invalid params\". Override the store location with\n> `RLMX_ACP_SESSIONS_DIR`.\n\n### Tidewave — External Agent (exec entry)\n\nAdd an external agent whose transport is **stdio / exec**:\n\n| Field | Value |\n| --- | --- |\n| Name | `rlmx` |\n| Command | `node` |\n| Args | `[\"/ABS/PATH/TO/rlmx/dist/src/cli.js\", \"acp\"]` |\n| Working directory (`cwd`) | `/ABS/PATH/TO/your-project` |\n| Env | `RLMX_ACP_RUN_TIMEOUT_MS=660000` (optional; lift the 300s run cap for recursive turns) |\n\n### Zed — `agent_servers` (settings.json)\n\n```jsonc\n// Zed settings.json\n{\n  \"agent_servers\": {\n    \"rlmx\": {\n      \"command\": \"node\",\n      \"args\": [\"/ABS/PATH/TO/rlmx/dist/src/cli.js\", \"acp\"],\n      \"cwd\": \"/ABS/PATH/TO/your-project\",\n      \"env\": {\n        \"RLMX_ACP_RUN_TIMEOUT_MS\": \"660000\"\n      }\n    }\n  }\n}\n```\n\n### Newio — custom agent type\n\n```jsonc\n// Newio agent config — a custom stdio ACP agent\n{\n  \"type\": \"acp\",\n  \"transport\": \"stdio\",\n  \"command\": \"node\",\n  \"args\": [\"/ABS/PATH/TO/rlmx/dist/src/cli.js\", \"acp\"],\n  \"cwd\": \"/ABS/PATH/TO/your-project\",\n  \"env\": { \"RLMX_ACP_RUN_TIMEOUT_MS\": \"660000\" }\n}\n```\n\n### acpx — dev loop\n\nDrive the agent by hand with [`acpx`](https://github.com/openclaw/acpx), a headless\nACP CLI client, while iterating. Its `--agent` escape hatch spawns any raw ACP\ncommand, so point it straight at rlmx's stdio entry (verified with acpx 0.12.0):\n\n```bash\n# From /ABS/PATH/TO/your-project (this becomes the session cwd):\nAGENT=\"node /ABS/PATH/TO/rlmx/dist/src/cli.js acp\"\n\n# One-time: register an acpx session for this cwd, driven by rlmx.\nnpx --yes acpx --agent \"$AGENT\" --cwd \"$PWD\" sessions new\n\n# Then drive prompts by hand, repeating as you iterate (--approve-all skips\n# permission prompts; drop it to answer them interactively):\nnpx --yes acpx --agent \"$AGENT\" --cwd \"$PWD\" --approve-all \"What does this repo do?\"\n\n# Or the repo's own scripted end-to-end smoke (spawns + drives the agent):\nnode /ABS/PATH/TO/rlmx/scripts/smoke-acp.mjs              # fast handshake + prompt\nnode /ABS/PATH/TO/rlmx/scripts/smoke-acp.mjs --multiturn  # survives an agent restart\nnode /ABS/PATH/TO/rlmx/scripts/smoke-acp.mjs --recursive  # live translated recursion stream\n```\n\n### Remote (stdio over SSH) — first-class\n\nBecause the transport is pure stdio, a host on your laptop can drive rlmx on a\nremote box with **no port, no tunnel, no `-t`** — SSH pipes stdin/stdout for\nyou. Point the host's `command` at `ssh` and pass the remote launch as args:\n\n```jsonc\n{\n  \"command\": \"ssh\",\n  \"args\": [\n    \"REMOTE_HOST\",\n    \"/ABS/PATH/ON/REMOTE/node\", \"/ABS/PATH/ON/REMOTE/rlmx/dist/src/cli.js\", \"acp\"\n  ],\n  \"cwd\": \"/ABS/PATH/TO/local-placeholder\"\n}\n```\n\n- **Use the remote node's absolute path — not bare `node`.** A non-interactive\n  SSH command (`ssh HOST node …`) does **not** source your login shell, so a\n  `node` installed via a version manager (nvm, fnm, asdf, volta) or under\n  `~/.local/bin` is **not on `PATH`** and you get `bash: node: command not\n  found`. Find the absolute path once from an interactive session on the remote\n  box — `ssh REMOTE_HOST` then `command -v node` (e.g. `/home/you/.nvm/versions/node/v20.11.0/bin/node`)\n  — and hard-code it. (If node is installed system-wide at `/usr/bin/node`,\n  bare `node` happens to work, but the absolute path is always safe.)\n- Do **not** pass `ssh -t` — a PTY injects terminal control bytes that corrupt\n  the JSON-RPC frame stream. Plain `ssh <host> /abs/node … acp` (no PTY) is\n  correct.\n- The session `cwd` that matters is the **remote** one; set it via the remote\n  project dir where you launch, or by prefixing `cd /remote/project &&`.\n- To pass env to the remote agent, set it in the remote command (SSH does not\n  forward local env by default), e.g.\n  `ssh REMOTE_HOST \"RLMX_REPL_TIMEOUT_MS=600000 /ABS/PATH/ON/REMOTE/node /abs/…/cli.js acp\"`.\n\n### Env legend\n\n| Env var | Effect |\n| --- | --- |\n| `RLMX_ACP_RUN_TIMEOUT_MS` | Override `rlmLoop`'s internal wall-clock cap for an ACP-hosted turn (default 300000). Raise it for recursive turns whose child spawns run long. |\n| `RLMX_REPL_TIMEOUT_MS` | Max time a single REPL cell may run before it is killed. Raise it when a recursive `rlm_query` cell must outlast a slow child. |\n| `RLMX_ACP_SESSIONS_DIR` | Override the durable session-store directory (default `~/.rlmx/acp-sessions`). Point it at scratch for hermetic tests. |\n| `STATION_BASE_URL` / `LEMONADE_BASE_URL` | Local station/Lemonade gateway base URL (default `http://localhost:13305/api/v1`). |\n\nAny provider API keys the chosen `.rlmx/rlmx.yaml` model needs are read from the\nenvironment / `~/.rlmx/settings.json` exactly as for the CLI.\n\n### MCP servers (store + advertise only)\n\n`initialize` advertises `mcpCapabilities` (`http` + `sse`), and rlmx accepts and\n**persists** the `mcpServers` a host passes on `session/new` / `session/load`.\nrlmx does **not** yet execute tools against those servers — there is no MCP\nclient wired in — so the config is stored/advertised for continuity, and MCP\ntool execution is a documented follow-on. A host is not misled: the advertised\ncapability carries `_meta[\"rlmx/mcp\"] = \"store-and-advertise-only; no MCP client\nexecution yet\"`.\n\n### Node-field legend — `rlm:` tool-call nodes\n\nEach recursive `rlm_query` spawn surfaces as a flat ACP tool-call node with\n`toolCallId = \"rlm:<childCorrelationId>\"`. Its completion `tool_call_update`\ncarries machine-readable per-node data under `_meta[\"rlmx/node\"]`:\n\n| `_meta[\"rlmx/node\"]` field | meaning |\n| --- | --- |\n| `correlationId` | The child node's stable id (sortable `uuidv7()` minted at the spawn site). Join key for the recursion tree. |\n| `parentRunId` | The spawning run's `correlationId` — the ancestry edge (absent for the root). |\n| `depth` | Recursion depth of this child (`[d{depth}]` also appears in the node title). |\n| `latencyMs` | Child wall-clock latency in ms. |\n| `costUsd` | Child cost in USD (may be absent on keyless/local providers). |\n| `tokens` | `{ input, output }` token counts for the child. |\n| `error` | `{ name, message }` when the child failed (node status `failed`). |\n\nA `tool_call_update` for an ordinary REPL execution instead carries\n`_meta[\"rlmx/durationMs\"]`. Client-facing payloads (args / output / titles) are\nwidth-bounded and secret-redacted at the translator boundary before they cross\nthe web boundary.\n\n## RTK Integration (token savings)\n\nrlmx auto-detects [RTK](https://github.com/rtk-ai/rtk) and routes CLI subprocess calls through it when available, for 60-90% token savings on tool outputs.\n\n### Install RTK (optional)\n\n```bash\nbrew install rtk                                                                             # macOS\ncurl -fsSL https://raw.githubusercontent.com/rtk-ai/rtk/refs/heads/master/install.sh | sh    # Linux/macOS\ncargo install --git https://github.com/rtk-ai/rtk                                            # Rust\n```\n\n### How it works\n\n- In your `TOOLS.md`, use `run_cli(cmd, *args)` instead of raw `subprocess.run(...)`\n- When RTK is installed, `run_cli` transparently prefixes with `rtk` → filtered output\n- When RTK is absent, `run_cli` passes through unchanged — no behavior break\n\n### Configuration\n\n```yaml\n# rlmx.yaml\nrtk:\n  enabled: auto   # auto | always | never (default: auto)\n```\n\n- `auto` — use RTK when detected on PATH, otherwise pass through (fail-open)\n- `always` — require RTK; `rlmx doctor` exits non-zero if missing\n- `never` — disable prefix even when RTK is installed\n\n### Verify\n\n```bash\nrlmx doctor         # shows RTK status (installed version + mode)\nrtk gain            # shows token savings from rlmx + other RTK integrations\n```\n\n### Before / after\n\n```python\n# Before — raw subprocess, full git output consumes tokens\nimport subprocess\nout = subprocess.run([\"git\", \"log\", \"-n\", \"10\"], capture_output=True, text=True).stdout\n\n# After — run_cli auto-routes through rtk when available\nr = run_cli(\"git\", \"log\", \"-n\", \"10\")\nout = r[\"stdout\"]   # filtered + compact; ~60-90% fewer tokens\n```\n\n## How It Works\n\nrlmx implements the RLM (REPL-LM) algorithm:\n\n1. **Prompt externalization** — Your context (files, directories) is loaded into a Python REPL as the `context` variable. Only metadata (type, size, chunk lengths) appears in the LLM message history. The LLM never sees the raw context in its messages.\n\n2. **Iterative REPL loop** — The LLM writes Python code in ` ```repl``` ` blocks. rlmx executes each block in a persistent Python subprocess, feeds results back, and the LLM iterates until it calls `FINAL()` or `FINAL_VAR()`.\n\n3. **Recursive sub-calls** — Inside REPL code, the LLM can call:\n   - `llm_query(prompt)` — single LLM completion (fast, one-shot)\n   - `llm_query_batched(prompts)` — concurrent LLM calls\n   - `rlm_query(prompt)` — spawn a child RLM session (full iterative loop)\n   - `rlm_query_batched(prompts)` — parallel child RLM sessions\n\n4. **Termination** — The loop ends when the LLM calls `FINAL(\"answer\")` or `FINAL_VAR(\"variable_name\")`, or when max iterations (default 30) is reached.\n\n## CAG Mode (Cache-Augmented Generation)\n\nCAG mode bakes your full context into the system prompt and leverages provider-level caching so that subsequent queries against the same context are dramatically cheaper and faster.\n\n### When to use `--cache` vs default RLM\n\n| Mode | Best for | How it works |\n|------|----------|-------------|\n| **Default (RLM)** | Large corpora, exploratory analysis | Context loaded into REPL `context` variable; LLM navigates it programmatically |\n| **`--cache`** | Repeated questions on same docs, study sessions, batch Q&A | Full context injected into system prompt and cached at the provider |\n\nUse `--cache` when you plan to ask multiple questions about the same set of documents. Use default RLM when the context is too large for a single system prompt or you need programmatic navigation.\n\n### Cost comparison\n\n| Query | Cost |\n|-------|------|\n| First query (cache miss) | Full input token cost (context + prompt) |\n| Subsequent queries (cache hit) | **50-90% cheaper** -- only cache-read tokens are billed |\n\nThe exact savings depend on your provider. Google and Anthropic both offer significant discounts on cached input tokens.\n\n### Batch usage\n\nProcess a list of questions against cached context:\n\n```bash\nrlmx batch questions.txt --context ./docs/\nrlmx batch questions.txt --context ./docs/ --output json\n```\n\nEach question in the file is run sequentially, reusing the cached context. The first question pays full cost; subsequent questions benefit from the cache.\n\n### Cache warmup and estimation\n\nWarm the cache and estimate costs before running queries:\n\n```bash\nrlmx cache --context ./docs/ --estimate\n```\n\nThis loads your context, calculates token counts, and shows estimated costs for cached vs uncached queries without making any LLM calls.\n\n### YAML configuration\n\nEnable cache in your `rlmx.yaml`:\n\n```yaml\ncache:\n  enabled: true              # or use --cache flag per-invocation\n  retention: long            # short|long -- maps to provider cache retention\n  ttl: 3600                  # seconds -- provider-specific TTL\n  expire-time: \"\"            # ISO 8601 -- for Google explicit caching\n  session-prefix: \"myproject\" # prepended to content hash for sessionId\n```\n\nFor detailed provider-specific TTL behavior (Google, Anthropic, Bedrock, OpenAI), see [docs/TTL_CONTROL.md](docs/TTL_CONTROL.md).\n\n## Gemini 3 Native (v0.4)\n\nrlmx v0.4 integrates 14 Gemini 3 native features, making it the cheapest and most capable context agent available. All features are opt-in, additive, and silently ignored on non-Google providers.\n\n### Quick Start\n\n```yaml\n# rlmx.yaml\nmodel:\n  provider: google\n  model: gemini-3.1-flash-lite-preview\n\ngemini:\n  thinking-level: medium      # Control thinking depth\n  google-search: true          # Web search in REPL\n  url-context: true            # Fetch URLs in REPL\n  code-execution: true         # Server-side Python\n  media-resolution:\n    images: high               # ~1120 tokens/image\n    pdfs: medium               # ~560 tokens/page\n    video: low                 # ~70 tokens/frame\n```\n\n```bash\nrlmx \"Research latest AI developments\" --context ./notes/ --tools standard --thinking high\n```\n\n### Features\n\n| Feature | Config | CLI Flag | Description |\n|---------|--------|----------|-------------|\n| Thinking levels | `gemini.thinking-level` | `--thinking` | minimal/low/medium/high — controls reasoning depth |\n| Thought signatures | automatic | — | Multi-turn quality via pi/ai signature circulation |\n| Structured output | `output.schema` | — | JSON Schema enforcement via API (not text parsing) |\n| Google Search | `gemini.google-search` | — | `web_search()` battery in REPL |\n| URL Context | `gemini.url-context` | — | `fetch_url()` battery in REPL |\n| Code Execution | `gemini.code-execution` | — | Server-side Python alongside local REPL |\n| Image Generation | `gemini.image-gen` | — | `generate_image()` via Nano Banana |\n| Media Resolution | `gemini.media-resolution` | — | Per-type token cost control |\n| Batch API | — | `--batch-api` | 50% cost reduction for bulk operations |\n| Context Caching | `cache.enabled` | `--cache` | 90% discount on cached tokens |\n| Computer Use | `gemini.computer-use` | — | Planned for v0.5 |\n| Maps Grounding | `gemini.maps-grounding` | — | Planned for v0.5 |\n| File Search | `gemini.file-search` | — | Planned for v0.5 |\n| Function + Tools | automatic | — | Custom functions + built-in tools in one API call |\n\n### Cost Comparison\n\n| Mode | Cost (per 1M tokens) | Savings |\n|------|---------------------|---------|\n| Base (flash-lite) | $0.075 input / $0.30 output | — |\n| + Context caching | ~$0.0075 input (cached) | 90% on input |\n| + Batch API | ~$0.0375 input / $0.15 output | 50% on all |\n| Cache + Batch | ~$0.00375 input (cached+batch) | 95% on cached input |\n\n**100 queries over 500K context: < $2.00** with cache + batch stacking.\n\n### Provider Compatibility\n\n| Feature | Google | Anthropic | OpenAI | Others |\n|---------|--------|-----------|--------|--------|\n| Thinking levels | native | ignored | ignored | ignored |\n| Thought signatures | native | ignored | ignored | ignored |\n| Structured output | API-enforced | FINAL() fallback | FINAL() fallback | FINAL() fallback |\n| Web search/URL | native | error msg | error msg | error msg |\n| Code execution | native | local only | local only | local only |\n| Media resolution | native | ignored | ignored | ignored |\n| Batch API | native | standard batch | standard batch | standard batch |\n| Context caching | native | native | native | provider-dependent |\n\n### Gemini Batteries (REPL Functions)\n\nAvailable with `--tools standard` or `--tools full` when provider is Google:\n\n```python\n# In REPL code:\nresult = web_search(\"latest nodejs version\")\nprint(result)\n\npage = fetch_url(\"https://example.com/docs\")\nprint(page[:500])\n\nimg_path = generate_image(\"architecture diagram of microservices\")\nprint(img_path)\n```\n\nNon-Google providers get clear error messages: `\"web_search() requires provider: google\"`.\n\n### Examples\n\nSee `examples/` for complete configs:\n- `gemini-research/` — Web search + URL context research agent\n- `gemini-multimodal/` — Media resolution + image analysis\n- `gemini-cheap-batch/` — Maximum cost stacking example\n\n## Config Files\n\nDrop `.md` files in your working directory to customize behavior. Run `rlmx init` to scaffold defaults with inline comments.\n\n| File | Purpose |\n|------|---------|\n| `SYSTEM.md` | System prompt sent to the LLM. Default: exact RLM paper prompt. |\n| `CONTEXT.md` | Context loading documentation (informational). |\n| `TOOLS.md` | Custom Python functions injected into the REPL namespace. |\n| `CRITERIA.md` | Output format criteria appended to the system prompt. |\n| `MODEL.md` | LLM provider and model selection. |\n\n### TOOLS.md Format\n\nDefine custom REPL tools as `## heading` + `python` code block:\n\n```markdown\n## search_docs\n` ``python\ndef search_docs(keyword):\n    \"\"\"Search context for files matching keyword.\"\"\"\n    matches = [item for item in context if keyword.lower() in item['content'].lower()]\n    return [m['path'] for m in matches]\n` ``\n\n## summarize_chunk\n` ``python\ndef summarize_chunk(text, max_words=100):\n    \"\"\"Summarize a chunk of text.\"\"\"\n    return llm_query(f\"Summarize in {max_words} words:\\n{text}\")\n` ``\n```\n\n### MODEL.md Format\n\n```markdown\nprovider: google\nmodel: gemini-3.1-flash-lite-preview\nsub-call-model: gemini-3.1-flash-lite-preview\n```\n\nSupports any provider available in [pi/ai](https://github.com/nickarora/pi-ai): `anthropic`, `openai`, `google`, etc.\n\n## CLI Reference\n\n```\nrlmx \"query\" [options]                Run an RLM query\nrlmx init [--dir <path>]             Scaffold config files\nrlmx batch <file> [options]           Run batch queries from a file\nrlmx cache [options]                  Cache management (warmup, estimate)\n\nOptions:\n  --context <path>        Path to context (directory or file)\n  --cache                 Enable CAG mode (cache context in system prompt)\n  --output <mode>         Output mode: text (default), json, stream\n  --verbose               Show iteration progress on stderr\n  --max-iterations <n>    Maximum RLM iterations (default: 30)\n  --timeout <ms>          Timeout in milliseconds (default: 300000)\n  --dir <path>            Directory for init command (default: cwd)\n  --help, -h              Show this help message\n  --version, -v           Show version\n\nGemini options:\n  --thinking <level>      Thinking level: minimal, low, medium, high\n  --batch-api             Use Gemini Batch API for 50% cost reduction\n\nCache options:\n  --estimate              Estimate cache costs without making LLM calls\n  --session-prefix <str>  Override session prefix for cache key\n```\n\n## Output Modes\n\n### Text (default)\n\nPrints the final answer to stdout.\n\n### JSON\n\n```bash\nrlmx \"query\" --output json\n```\n\nReturns:\n\n```json\n{\n  \"answer\": \"The answer to your query...\",\n  \"references\": [\"docs/start/create-project.md\", \"docs/concept/inter-process-communication.md\"],\n  \"usage\": { \"inputTokens\": 12500, \"outputTokens\": 3200, \"llmCalls\": 5 },\n  \"iterations\": 3,\n  \"model\": \"google/gemini-3.1-flash-lite-preview\"\n}\n```\n\n### Stream\n\n```bash\nrlmx \"query\" --output stream\n```\n\nEmits JSONL events per iteration, then a final event.\n\n## Context Loading\n\n| Input | Behavior |\n|-------|----------|\n| `--context dir/` | Recursively reads `*.md` files as `list[{path, content}]` |\n| `--context file.md` | Reads as single string |\n| `--context file.json` | Parses JSON as dict or list |\n| stdin pipe | Reads as single string |\n\n## Environment Variables\n\nrlmx uses pi/ai for LLM calls. Set the appropriate API key for your provider:\n\n- `GEMINI_API_KEY` — for Google Gemini models (default provider)\n- `ANTHROPIC_API_KEY` — for Anthropic models\n- `OPENAI_API_KEY` — for OpenAI models\n\n## Programmatic API\n\n```typescript\nimport { rlmLoop, loadConfig, loadContext } from \"rlmx\";\n\nconst config = await loadConfig(\"./\");\nconst context = await loadContext(\"./docs/\");\n\nconst result = await rlmLoop(\"How does IPC work?\", context, config, {\n  maxIterations: 10,\n  timeout: 60000,\n  verbose: false,\n  output: \"json\",\n});\n\nconsole.log(result.answer);\nconsole.log(result.references);\n```\n\n## Requirements\n\n- Node.js >= 22.19.0\n- Python 3.10+ (for the REPL subprocess)\n- An LLM API key (Anthropic, OpenAI, Google, etc.)\n\n## License\n\nMIT\n","readmeFilename":"README.md"}