Split CLAUDE.md per official Claude Code recommendations
CLAUDE.md: lean — commands, key conventions, fast tool guide, @ARCHITECTURE.md import routecheck/CLAUDE.md: purpose, access paths, env vars, gotchas openmemory/CLAUDE.md: tools, two Ollama instances, prompts, notes Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
Alvis
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CLAUDE.md
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CLAUDE.md
@@ -4,175 +4,38 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co
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## Commands
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## Commands
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**Start all services:**
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```bash
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```bash
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# Start all services
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docker compose up --build
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docker compose up --build
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```
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**Interactive CLI (Docker container, requires gateway running):**
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# Interactive CLI (requires services running)
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```bash
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docker compose --profile tools run --rm -it cli
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docker compose --profile tools run --rm -it cli
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# or with options:
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docker compose --profile tools run --rm -it cli python3 cli.py --url http://deepagents:8000 --session cli-alvis
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# Integration tests (run from tests/integration/, requires all services)
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python3 test_health.py
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python3 test_memory.py [--name-only|--bench-only|--dedup-only]
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python3 test_routing.py [--easy-only|--medium-only|--hard-only]
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# Use case tests — read the .md file and follow its steps as Claude Code
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# e.g.: read tests/use_cases/weather_now.md and execute it
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```
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```
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**Run integration tests** (from `tests/integration/`, require all Docker services running):
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## Key Conventions
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```bash
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python3 test_health.py # service health: deepagents, bifrost, Ollama, Qdrant, SearXNG
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python3 test_memory.py # name store/recall + memory benchmark + dedup
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- **Models via Bifrost only** — all LLM calls use `base_url=BIFROST_URL` with `ollama/<model>` prefix. Never call Ollama directly for inference.
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python3 test_memory.py --name-only # only name store/recall pipeline
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- **One inference at a time** — `_reply_semaphore` serializes GPU use. Do not bypass it.
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python3 test_memory.py --bench-only # only 5-fact store + 10-question recall
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- **No tools in medium agent** — `_DirectModel` is a plain `ainvoke()` call. Context is injected via system prompt. `qwen3:4b` is unreliable with tool schemas.
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python3 test_memory.py --dedup-only # only deduplication test
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- **Fast tools are pre-flight** — `FastToolRunner` runs before routing and before any LLM call. Results are injected as context, not returned to the user directly.
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- **Memory outside agent loop** — `add_memory`/`search_memory` are called directly, never passed to agent tool lists.
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- **Complex tier is opt-in** — `/think ` prefix only. LLM classification of "complex" is always downgraded to medium.
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- **`.env` is required** — `TELEGRAM_BOT_TOKEN`, `ROUTECHECK_TOKEN`, `YANDEX_ROUTING_KEY`. Never commit it.
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python3 test_routing.py # all routing benchmarks (easy + medium + hard)
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## Adding a Fast Tool
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python3 test_routing.py --easy-only # light-tier routing benchmark
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python3 test_routing.py --medium-only # medium-tier routing benchmark
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python3 test_routing.py --hard-only # complex-tier + VRAM flush benchmark
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```
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Shared config and helpers are in `tests/integration/common.py`.
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1. Subclass `FastTool` in `fast_tools.py` — implement `name`, `matches(message) → bool`, `run(message) → str`
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2. Add instance to `_fast_tool_runner` list in `agent.py`
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**Use case tests** (`tests/use_cases/`) — markdown skill files executed by Claude Code, which acts as mock user and quality evaluator. Run by reading the `.md` file and following its steps with tools (Bash, WebFetch, etc.).
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3. The router will automatically force medium tier when `matches()` returns true
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## Architecture
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## Architecture
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Adolf is a multi-channel personal assistant. All LLM inference is routed through **Bifrost**, an open-source Go-based LLM gateway that adds retry logic, failover, and observability in front of Ollama.
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@ARCHITECTURE.md
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### Request flow
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```
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Channel adapter → POST /message {text, session_id, channel, user_id}
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→ 202 Accepted (immediate)
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→ background: run_agent_task()
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→ asyncio.gather(
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_fetch_urls_from_message() ← Crawl4AI, concurrent
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_retrieve_memories() ← openmemory search, concurrent
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_fast_tool_runner.run_matching() ← FastTools (weather, commute), concurrent
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)
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→ router.route() → tier decision (light/medium/complex)
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fast tool match → force medium
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if URL content fetched → upgrade light→medium
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→ invoke agent for tier via Bifrost (url_context + memories in system prompt)
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deepagents:8000 → bifrost:8080/v1 → ollama:11436
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→ _push_stream_chunk() per token (medium streaming) / full reply (light, complex)
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→ _stream_queues[session_id] asyncio.Queue
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→ _end_stream() sends [DONE] sentinel
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→ channels.deliver(session_id, channel, reply)
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→ channel-specific callback (Telegram POST)
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→ _store_memory() background task (openmemory)
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CLI streaming → GET /stream/{session_id} (SSE, per-token for medium, single-chunk for others)
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```
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### Bifrost integration
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Bifrost (`bifrost-config.json`) is configured with the `ollama` provider pointing to the GPU Ollama instance on host port 11436. It exposes an OpenAI-compatible API at `http://bifrost:8080/v1`.
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`agent.py` uses `langchain_openai.ChatOpenAI` with `base_url=BIFROST_URL`. Model names use the `provider/model` format that Bifrost expects: `ollama/qwen3:4b`, `ollama/qwen3:8b`, `ollama/qwen2.5:1.5b`. Bifrost strips the `ollama/` prefix before forwarding to Ollama.
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`VRAMManager` bypasses Bifrost and talks directly to Ollama via `OLLAMA_BASE_URL` (host:11436) for flush/poll/prewarm operations — Bifrost cannot manage GPU VRAM.
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### Three-tier routing (`router.py`, `agent.py`)
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| Tier | Model (env var) | Trigger |
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|------|-----------------|---------|
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| light | `qwen2.5:1.5b` (`DEEPAGENTS_ROUTER_MODEL`) | Regex pre-match or LLM classifies "light" — answered by router model directly, no agent invoked |
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| medium | `qwen3:4b` (`DEEPAGENTS_MODEL`) | Default for tool-requiring queries |
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| complex | `qwen3:8b` (`DEEPAGENTS_COMPLEX_MODEL`) | `/think ` prefix only |
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The router does regex pre-classification first, then LLM classification. Complex tier is blocked unless the message starts with `/think ` — any LLM classification of "complex" is downgraded to medium.
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A global `asyncio.Semaphore(1)` (`_reply_semaphore`) serializes all LLM inference — one request at a time.
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### Thinking mode and streaming
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qwen3 models produce chain-of-thought `<think>...</think>` tokens. Handling differs by tier:
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- **Medium** (`qwen3:4b`): streams via `astream()`. A state machine (`in_think` flag) filters `<think>` blocks in real time — only non-think tokens are pushed to `_stream_queues` and displayed to the user.
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- **Complex** (`qwen3:8b`): `create_deep_agent` returns a complete reply; `_strip_think()` filters think blocks before the reply is pushed as a single chunk.
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- **Router/light** (`qwen2.5:1.5b`): no thinking support; `_strip_think()` used defensively.
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`_strip_think()` in `agent.py` and `router.py` strips any `<think>` blocks from non-streaming output.
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### VRAM management (`vram_manager.py`)
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Hardware: GTX 1070 (8 GB). Before running the 8b model, medium models are flushed via Ollama `keep_alive=0`, then `/api/ps` is polled (15s timeout) to confirm eviction. On timeout, falls back to medium tier. After complex reply, 8b is flushed and medium models are pre-warmed as a background task.
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### Channel adapters (`channels.py`)
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- **Telegram**: Grammy Node.js bot (`grammy/bot.mjs`) long-polls Telegram → `POST /message`; replies delivered via `POST grammy:3001/send`
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- **CLI**: `cli.py` (Docker container, `profiles: [tools]`) posts to `/message`, then streams from `GET /stream/{session_id}` SSE with Rich `Live` display and final Markdown render.
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Session IDs: `tg-<chat_id>` for Telegram, `cli-<username>` for CLI. Conversation history: 5-turn buffer per session.
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### Services (`docker-compose.yml`)
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| Service | Port | Role |
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|---------|------|------|
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| `bifrost` | 8080 | LLM gateway — retries, failover, observability; config from `bifrost-config.json` |
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| `deepagents` | 8000 | FastAPI gateway + agent core |
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| `openmemory` | 8765 | FastMCP server + mem0 memory tools (Qdrant-backed) |
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| `grammy` | 3001 | grammY Telegram bot + `/send` HTTP endpoint |
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| `crawl4ai` | 11235 | JS-rendered page fetching |
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| `routecheck` | 8090 | Local routing web service — image captcha UI + Yandex Routing API backend |
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| `cli` | — | Interactive CLI container (`profiles: [tools]`), Rich streaming display |
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External (from `openai/` stack, host ports):
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- Ollama GPU: `11436` — all reply inference (via Bifrost) + VRAM management (direct)
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- Ollama CPU: `11435` — nomic-embed-text embeddings for openmemory
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- Qdrant: `6333` — vector store for memories
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- SearXNG: `11437` — web search
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### Bifrost config (`bifrost-config.json`)
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The file is mounted into the bifrost container at `/app/data/config.json`. It declares one Ollama provider key pointing to `host.docker.internal:11436` with 2 retries and 300s timeout. To add fallback providers or adjust weights, edit this file and restart the bifrost container.
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### Crawl4AI integration
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Crawl4AI is embedded at all levels of the pipeline:
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- **Pre-routing (all tiers)**: `_fetch_urls_from_message()` detects URLs in any message via `_URL_RE`, fetches up to 3 URLs concurrently with `_crawl4ai_fetch_async()` (async httpx). URL content is injected as a system context block into enriched history before routing, and into the system prompt for medium/complex agents.
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- **Tier upgrade**: if URL content is successfully fetched, light tier is upgraded to medium (light model cannot process page content).
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- **Complex agent tools**: `web_search` (SearXNG + Crawl4AI auto-fetch of top 2 results) and `fetch_url` (single-URL Crawl4AI fetch) remain available for the complex agent's agentic loop. Complex tier also receives the pre-fetched content in system prompt to avoid redundant re-fetching.
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MCP tools from openmemory (`add_memory`, `search_memory`, `get_all_memories`) are **excluded** from agent tools — memory management is handled outside the agent loop.
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### Fast Tools (`fast_tools.py`)
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Pre-flight tools that run before the LLM in the `asyncio.gather` alongside URL fetch and memory retrieval. Each tool has a regex `matches()` classifier and an async `run()` that returns a context string injected into the system prompt. The router uses `FastToolRunner.any_matches()` to force medium tier when a tool matches.
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| Tool | Trigger | Data source |
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|------|---------|-------------|
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| `WeatherTool` | weather/forecast/temperature keywords | SearXNG query `"погода Балашиха сейчас"` — Russian sources return °C |
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| `CommuteTool` | commute/traffic/arrival time keywords | `routecheck:8090/api/route` — Yandex Routing API, Balashikha→Moscow center |
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To add a new fast tool: subclass `FastTool` in `fast_tools.py`, add an instance to `_fast_tool_runner` in `agent.py`.
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### `routecheck` service (`routecheck/app.py`)
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Local web service that exposes Yandex Routing API behind an image captcha. Two access paths:
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- **Web UI** (`localhost:8090`): solve PIL-generated arithmetic captcha → query any two lat/lon points
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- **Internal API**: `GET /api/route?from=lat,lon&to=lat,lon&token=ROUTECHECK_TOKEN` — bypasses captcha, used by `CommuteTool`
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Requires `.env`: `YANDEX_ROUTING_KEY` (free tier from `developer.tech.yandex.ru`) and `ROUTECHECK_TOKEN`. The container routes Yandex API calls through the host HTTPS proxy (`host.docker.internal:56928`).
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### Medium vs Complex agent
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| Agent | Builder | Speed | Use case |
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|-------|---------|-------|----------|
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| medium | `_DirectModel` (single LLM call, no tools) | ~3s | General questions, conversation |
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| complex | `create_deep_agent` (deepagents) | Slow — multi-step planner | Deep research via `/think` prefix |
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### Key files
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- `agent.py` — FastAPI app, lifespan wiring, `run_agent_task()`, Crawl4AI pre-fetch, fast tools, memory pipeline, all endpoints
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- `fast_tools.py` — `FastTool` base class, `FastToolRunner`, `WeatherTool`, `CommuteTool`
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- `routecheck/app.py` — captcha UI + `/api/route` Yandex proxy
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- `bifrost-config.json` — Bifrost provider config (Ollama GPU, retries, timeouts)
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- `channels.py` — channel registry and `deliver()` dispatcher
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- `router.py` — `Router` class: regex + LLM classification, light-tier reply generation
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- `vram_manager.py` — `VRAMManager`: flush/poll/prewarm Ollama VRAM directly
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- `agent_factory.py` — `build_medium_agent` (`_DirectModel`, single call) / `build_complex_agent` (`create_deep_agent`)
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- `openmemory/server.py` — FastMCP + mem0 config with custom extraction/dedup prompts
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- `wiki_research.py` — batch research pipeline using `/message` + SSE polling
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- `grammy/bot.mjs` — Telegram long-poll + HTTP `/send` endpoint
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openmemory/CLAUDE.md
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26
openmemory/CLAUDE.md
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@@ -0,0 +1,26 @@
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# openmemory
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FastMCP server wrapping mem0 for persistent per-session memory, backed by Qdrant + nomic-embed-text.
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## Tools exposed (MCP)
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- `add_memory(text, user_id)` — extract facts from a conversation turn and store in Qdrant
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- `search_memory(query, user_id)` — semantic search, returns results with score ≥ 0.5
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- `get_all_memories(user_id)` — dump all stored memories for a session
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These are called directly by `agent.py` (outside the agent loop), never exposed to the LLM as tools.
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## Two Ollama instances
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- **GPU** (`OLLAMA_GPU_URL`, port 11436) — extraction model (`qwen2.5:1.5b`): pulls facts from conversation text
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- **CPU** (`OLLAMA_CPU_URL`, port 11435) — embedding model (`nomic-embed-text`): 50–150 ms per query
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## Prompts
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Custom `EXTRACTION_PROMPT` starts with `/no_think` to suppress qwen3 chain-of-thought and get clean JSON output. Custom `UPDATE_MEMORY_PROMPT` handles deduplication — mem0 merges new facts with existing ones rather than creating duplicates.
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## Notes
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- Qdrant collection is created automatically on first use
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- Memory is keyed by `user_id` which equals `session_id` in `agent.py`
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- Extraction runs after the reply is sent (background task) — GPU contention with medium model is avoided since the semaphore is released before `_store_memory()` is scheduled
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25
routecheck/CLAUDE.md
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25
routecheck/CLAUDE.md
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# routecheck
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FastAPI service providing a Yandex Routing API proxy behind an image captcha.
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## Purpose
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Yandex Routing API free tier requires a website that uses the API. This service is that website.
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It also exposes an internal endpoint (`/api/route`) used by `CommuteTool` in `fast_tools.py`.
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## Two access paths
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- **Web UI** (`/`): solve PIL arithmetic captcha → get a token → query any two lat/lon points
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- **Internal API**: `GET /api/route?from=lat,lon&to=lat,lon&token=$ROUTECHECK_TOKEN` — no captcha
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## Key env vars
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- `YANDEX_ROUTING_KEY` — from developer.tech.yandex.ru, Router API, free tier
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- `INTERNAL_TOKEN` — equals `ROUTECHECK_TOKEN` from root `.env`; shared with deepagents
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- `HTTPS_PROXY` — set to `http://host.docker.internal:56928`; container has no direct external internet
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## Notes
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- Captchas expire after 5 min, route tokens after 1 hour, both stored in-memory (restart clears them)
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- Yandex API expects `lon,lat` order (not `lat,lon`) — `app.py` swaps automatically
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- Captcha image endpoint: `GET /captcha/image/{id}` — regenerates on each call with random noise
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Reference in New Issue
Block a user