When conversation exceeds 24 items (~12 turns), Gemma summarizes the older
messages into a 2-4 sentence recap that replaces them, keeping the last 10
items verbatim. Also triggers after 5 minutes of idle time so returning
users get a compacted context rather than a bloated one.
Falls back to hard truncation if the summary call fails.
Compaction:
- GemmaLLM.chat() truncates ChatContext to last 30 items (~15 turns)
before sending to LLM, preventing context window overflow on long
conversations. Preserves system prompt and removes orphaned tool calls.
Skills:
- agent/skills_mcp.py: MCP server with skill_save, skill_recall,
skill_list, skill_update tools backed by .md files in /skills
- skills/ dir bind-mounted into container, git-trackable
- System prompt instructs Hope to save repeatable procedures as skills
and recall them before performing tasks she's done before
- Distinct from memory (facts) — skills are learned *procedures*
- agent/memory_mcp.py: MCP server with memory_recall, memory_save, memory_list
tools backed by .md files in /memory (simple keyword matching for v1)
- memory/ dir bind-mounted into container, persists across rebuilds,
easily backed up via git
- System prompt instructs Hope to recall on past references and save
personal info/preferences naturally without announcing it
- Dockerfile: copy memory_mcp.py; compose: ./memory:/memory volume
- preemptive_generation was incompatible with MCP tool calls: it starts
generating before the turn finalizes, breaking the tool execution loop
(agent said 'let me check' then hung forever)
- Add get_time(location?) to weather_mcp.py for date/time queries
- Update system prompt with Weather & Time section
- New agent/weather_mcp.py: stdio MCP server with get_weather(location)
that returns a short conversational summary (temp, conditions, high/low)
- Register weather toolset in build_mcp_toolsets() alongside web-access
- Update system prompt with Weather section
- agent/web_mcp.py: stdio MCP server exposing web_search and web_scrape,
backed by the self-hosted Firecrawl stack on xNAS (no API key needed)
- agent.py: Agent now attaches mcp_servers built from config; EXTRA_MCP_SERVERS
env var allows adding arbitrary HTTP/SSE MCP servers as JSON
- Dockerfile: installs livekit-agents[mcp], copies web_mcp.py
- .env.example: WEB_MCP_ENABLED, FIRECRAWL_BASE, EXTRA_MCP_SERVERS documented
Shane