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.
32 lines
1.5 KiB
Markdown
32 lines
1.5 KiB
Markdown
# context-decay
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Time-based memory decay with relevance scoring. Memories lose confidence over time (5% per day) and are only recalled when they pass both a decay threshold and a relevance check against the current message. Implements "use it or lose it" -- recalled memories get their access timestamps refreshed.
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## How it works
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**After each turn**, the plugin extracts memorable statements from the conversation:
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- User preferences ("I prefer...", "I use...")
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- Decisions ("let's use...", "we decided...")
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- Important facts (names, versions, URLs)
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- Corrections ("no, actually...", "that's wrong...")
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Similar memories are reinforced (confidence +0.1, cap 1.0). All memories receive a decay pass, and those below 0.1 confidence are pruned.
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**On ingest**, each memory's confidence is decayed based on time elapsed, then scored for relevance to the current message via keyword overlap. The composite score `decayed_confidence * (0.5 + 0.5 * relevance)` must exceed 0.3 to be recalled. Recalled memories get their `last_accessed` timestamp updated.
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## Hooks
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| Hook | Script | Description |
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| ingest | `hooks/ingest.py` | Applies decay, scores relevance, returns top 5 memories above threshold |
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| after_turn | `hooks/after_turn.py` | Extracts new memories, reinforces similar ones, prunes decayed entries |
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## Storage
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Memories are stored at `~/.librefang/plugins/context-decay/{agent_id}.json`. Max 100 memories per agent. Decay formula: `confidence * 0.95^(hours / 24)`.
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## Usage
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Installed automatically when enabled in agent configuration.
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