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Evan d1cab3e33a feat: context engine plugins, scaffolding, and pricing fixes (#6 )

* 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.

2026-03-21 03:36:32 +09:00

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Plugins

Plugin packages for LibreFang. Plugins extend agent behavior through lifecycle hooks -- they can inject memories, modify context, or perform side effects during conversations.

Structure

plugins/
└── <plugin-name>/
    ├── plugin.toml          # Plugin manifest
    ├── hooks/
    │   ├── ingest.py        # Called on user message
    │   └── after_turn.py    # Called after each turn
    └── requirements.txt     # Python dependencies

plugin.toml Format

name = "plugin-name"             # Must match directory name
version = "0.1.0"
description = "What this plugin does"
author = "author-name"

[hooks]
ingest = "hooks/ingest.py"       # Receives user message, can return memories
after_turn = "hooks/after_turn.py" # Post-turn processing

Hook Protocol

Hooks communicate via stdin/stdout JSON:

ingest hook

stdin:  {"type": "ingest", "agent_id": "...", "message": "user message"}
stdout: {"type": "ingest_result", "memories": [{"content": "..."}]}

after_turn hook

stdin:  {"type": "after_turn", "agent_id": "...", "messages": [...]}
stdout: {"type": "ok"}

Current Plugins (10)

Plugin	Hooks	Description
auto-summarizer	ingest, after_turn	Running conversation summary for long context compression
context-decay	ingest, after_turn	Time-based memory decay with relevance scoring for natural forgetting
conversation-logger	after_turn	Logs conversations to JSONL files for auditing and analytics
episodic-memory	ingest, after_turn	Episode-based conversation segmentation and cross-session recall
guardrails	ingest	Safety filter detecting PII, prompt injection, and credential exposure
keyword-memory	ingest	Extracts keywords and named entities as contextual memories
sentiment-tracker	ingest	Analyzes user sentiment and injects emotional context
todo-tracker	ingest, after_turn	Detects, persists, and recalls action items from conversations
topic-memory	ingest, after_turn	Topic-aware keyword clustering with cross-conversation context recall
user-profile	ingest, after_turn	Persistent user profiling from conversation patterns for personalization

Adding a New Plugin

Create plugins/<name>/plugin.toml
Add hook scripts in hooks/
List dependencies in requirements.txt (prefer stdlib-only)
Run python scripts/validate.py
Submit a PR

See CONTRIBUTING.md for the full guide.

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