Evan
d1cab3e33a
* feat: add 4 context engine plugins
- topic-memory: keyword clustering for topic-aware memory recall
- episodic-memory: conversation segmentation and cross-session recall
- user-profile: persistent user profiling from conversation patterns
- context-decay: time-based memory decay with reinforcement dynamics
All plugins use the ingest/after_turn hook protocol with stdin/stdout JSON.
* chore: add plugin scaffolding, update docs and templates
- Add plugin.toml template with {{NAME}} placeholder
- Add new-plugin Makefile target with hooks/ scaffolding
- Update plugins/README.md with all 10 plugins
- Update README.md stats (10 plugins, 220+ models)
- Add Plugin checkbox and checklist to PR template
- Add Plugin to issue template content type dropdown
- Fix CONTRIBUTING.md: last_verified is recommended, not required
* fix: correct model pricing and remove deprecated entries
- openrouter/gemma-2-9b-it: fix pricing from 0.0 to 0.03/0.09 per M tokens
(free variant correctly stays at 0.0)
- github-copilot: remove deprecated copilot/gpt-4 model entry
(GPT-4 retired in favor of GPT-4o for Copilot)
* docs: annotate kimi-coding as membership-gated
Kimi Code CLI uses quota-based membership model (not per-token billing).
Free tier has limited weekly requests; underlying model is K2.5.
Pricing kept at 0.0 consistent with other subscription providers
(chatgpt, github-copilot) but with explanatory comments.
* style: fix trailing newline in github-copilot.toml
* fix: correct Moonshot/Kimi model pricing from official sources
All 5 models had incorrect pricing:
- moonshot-v1-8k: 0.10/0.10 → 0.20/2.00
- moonshot-v1-32k: 0.30/0.30 → 1.00/3.00
- moonshot-v1-128k: 0.80/0.80 → 2.00/5.00
- kimi-k2: 2.00/8.00 → 0.60/2.50
- kimi-k2.5: 2.00/8.00 → 0.45/2.20
Sources: platform.moonshot.ai/docs/pricing/chat, costgoat.com, getmaxim.ai
* feat: add MiniMax M2.7 and M2.7-highspeed models
Released 2026-03-18, MiniMax's latest flagship text model.
10B activated params, 200K context, 128K output, tool use, streaming.
Pricing: $0.30/$1.20 per M tokens (input/output).
Added to both international (minimax.io) and China (minimaxi.com) providers.
1.4 KiB
episodic-memory
Episode-based conversation segmentation and cross-session recall. Automatically detects topic shifts to split conversations into discrete episodes, then recalls relevant past episodes when similar topics arise.
How it works
After each turn, the plugin tracks a "current episode" with accumulated keywords. When the Jaccard similarity between the current turn's keywords and the running episode keywords drops below 0.1 (and the episode has at least 4 messages), it marks the episode as completed with a summary and starts a new one.
On ingest, the plugin scores all completed episodes against the incoming message keywords and returns the top 2 matches (overlap > 0.2) as contextual memories including the episode timestamp and summary.
This gives agents episodic recall -- "last time we discussed Docker deployment, we configured nginx as a reverse proxy."
Hooks
| Hook | Script | Description |
|---|---|---|
| ingest | hooks/ingest.py |
Scores completed episodes against message keywords, returns top matches |
| after_turn | hooks/after_turn.py |
Tracks current episode, detects topic shifts, segments conversations |
Storage
Episodes are stored at ~/.librefang/plugins/episodic-memory/{agent_id}.json. Max 30 completed episodes per agent (oldest evicted when full).
Usage
Installed automatically when enabled in agent configuration.