- Replace SearXNG search with direct open-meteo.com API call (no key needed)
- WeatherTool now returns a ready-to-deliver reply string
- agent.py: short-circuit router+LLM when fast tools return a result (tier=fast)
- router.py: fast tool match no longer triggers light reply generation
Weather latency: 105-190s → ~1s
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
WeatherTool queries SearXNG with a fixed 'weather Balashikha Moscow now'
query instead of passing the user message as-is. SearXNG has external
internet access and returns snippets with actual current conditions.
Direct wttr.in fetch not possible — deepagents container has no external
internet routing.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
New fast_tools.py module:
- FastTool base class (matches + run interface)
- RealTimeSearchTool: SearXNG search for weather/news/prices/scores
- FastToolRunner: classifier that checks all tools, runs matching
ones concurrently and returns combined context
Router accepts FastToolRunner; any_matches() forces medium tier
before LLM classification (replaces _MEDIUM_FORCE_PATTERNS regex).
agent.py: _REALTIME_RE and _searxng_search_async removed; pre-flight
gather now includes fast_tool_runner.run_matching() alongside URL
fetch and memory retrieval.
To add a new fast tool: subclass FastTool, add to the list in agent.py.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- router.py: add _MEDIUM_FORCE_PATTERNS to block weather/news/price
queries from light tier regardless of LLM classification
- agent.py: add _REALTIME_RE and _searxng_search_async(); real-time
queries now run SearXNG search concurrently with URL fetch + memory
retrieval, injecting snippets into medium system prompt
- tests/use_cases/weather_now.md: use case test for weather queries
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Server (agent.py):
- _stream_queues: per-session asyncio.Queue for token chunks
- _push_stream_chunk() / _end_stream() helpers
- Medium tier: astream() with <think> block filtering — real token streaming
- Light tier: full reply pushed as single chunk then [DONE]
- Complex tier: full reply pushed after agent completes then [DONE]
- GET /stream/{session_id} SSE endpoint (data: <chunk>\n\n, data: [DONE]\n\n)
- medium_model promoted to module-level global for astream() access
CLI (cli.py):
- stream_reply(): reads /stream/ SSE, renders tokens live with Rich Live (transient)
- Final reply rendered as Markdown after stream completes
- os.getlogin() replaced with os.getenv("USER") for container compatibility
Dockerfile.cli + docker-compose cli service (profiles: tools):
- Run: docker compose --profile tools run --rm -it cli
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Pre-routing URL fetch: any message with URLs gets content fetched
async (httpx.AsyncClient) before routing via _fetch_urls_from_message()
- URL context and memories gathered concurrently with asyncio.gather
- Light tier upgraded to medium when URL content is present
- url_context injected into system prompt for medium and complex agents
- Complex agent retains web_search/fetch_url tools + receives pre-fetched content
- Medium model restored to qwen3:4b (was temporarily qwen2.5:1.5b)
- Unit tests added for _extract_urls
- ARCHITECTURE.md: added Tool Handling, Crawl4AI Integration, Memory Pipeline sections
- CLAUDE.md: updated request flow and Crawl4AI integration docs
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Add Bifrost (maximhq/bifrost) as LLM gateway: all inference routes through
bifrost:8080/v1 with retry logic and observability; VRAMManager keeps direct
Ollama access for VRAM flush/prewarm operations
- Switch medium model from qwen3:4b to qwen2.5:1.5b (direct call, no tools)
via _DirectModel wrapper; complex keeps create_deep_agent with qwen3:8b
- Implement out-of-agent memory pipeline: _retrieve_memories pre-fetches
relevant context (injected into all tiers), _store_memory runs as background
task after each reply writing to openmemory/Qdrant
- Add tests/unit/ with 133 tests covering router, channels, vram_manager,
agent helpers; move integration test to tests/integration/
- Add bifrost-config.json with GPU Ollama (qwen2.5:0.5b/1.5b, qwen3:4b/8b,
gemma3:4b) and CPU Ollama providers
- Integration test 28/29 pass (only grammy fails — no TELEGRAM_BOT_TOKEN)
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Tell agent that memory is saved automatically after every reply
- Instruct agent to never say it cannot store information
- Instruct agent to acknowledge and confirm when user asks to remember something
- Fix misleading startup log (gemma3:1b → qwen2.5:1.5b)
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
