simplecontext-ai 4.3.0

Universal AI Brain for AI Agents — Tiered Memory, Context Scoring, Intent Planning. Zero dependencies.

🧠 SimpleContext

Universal AI Brain for AI Agents
Tiered Memory · Context Scoring · Intent Planning · Zero Dependencies

SimpleContext is not another vector database wrapper. It's a structured context brain — tiered memory, intent-aware retrieval, fact extraction, and importance scoring. Without a single external dependency.

Quick Start · Architecture · Agent System · API Reference · Ecosystem · Contribute

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🤔 Why SimpleContext?

Most AI agent frameworks treat memory as a flat list of messages. This breaks down fast:

❌ Flat memory:    [msg1, msg2, ... msg500]  → retrieval gets noisy
✅ Tiered memory:  working · episodic · semantic  → structured, scored, evolved

SimpleContext gives your agent a structured brain — not just a chat log.

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✨ Features

	Feature	Description
🧠	3-Tier Memory	working (active) · episodic (sessions) · semantic (long-term facts)
🎯	Intent Planning	Auto-detect intent → smart retrieval strategy per query type
📊	Context Scoring	relevance×0.55 + importance×0.25 + recency×0.10 + path_priority×0.10
🔍	Fact Extraction	Rule-based: "user uses Proxmox", "user project Mangafork"
♻️	Memory Evolution	Jaccard dedup · conflict resolution · importance decay
⚡	LRU Cache	30s TTL cache for repeated queries — reduces DB load
🤖	Agent YAML	Define agents in YAML · hot-reload without restart
🔗	Agent Chaining	Agent A handoff to Agent B based on user message
💾	Multi-Storage	SQLite (default, zero install) · Redis · PostgreSQL
🔌	Plugin System	Hooks + persistent state + dependency resolver
🔄	Backward Compat	All v3 API still works — zero breaking changes
📦	Zero Dependencies	Only Python built-ins: sqlite3, json, re, datetime

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🏗️ Architecture

User Message
      │
      ▼
 AgentRouter ──────── agents/*.yaml  (hot-reload, TF-IDF routing)
      │
      ▼
 ContextPlanner
      │  intent : coding | personal | task | knowledge | conversation
      │  budget : { working:5, episodic:2, semantic:4, skills:3 }
      ▼
 ContextEngine
      ├── Retriever   → collect candidates
      ├── Resolver    → TTL check, mark expired
      ├── Filter      → active nodes only
      ├── Scorer      → rank by relevance + importance + recency + path
      └── Selector    → enforce budget + max_nodes + max_chars
      │
      ▼
 PromptBuilder  (deterministic, bullet-format per tier)
      │
      ▼
    LLM  ←→  Gemini / OpenAI / Claude / Ollama / any
      │
      ▼
 MemoryProcessor
      ├── store messages  → working tier
      ├── extract facts   → semantic tier (rule-based, no LLM)
      ├── dedup           → Jaccard similarity ≥ 0.65
      ├── conflict resolve → confidence-based supersedes
      └── update importance scores

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🚀 Quick Start

No install needed. Copy simplecontext/ folder into your project.

from simplecontext import SimpleContext

sc = SimpleContext("config.yaml")

# Simple mode (v3 API — backward compatible)
result   = sc.router.route(user_id, message)
messages = sc.prepare_messages(user_id, message, result)
reply    = your_llm(messages)
reply    = sc.process_response(user_id, message, reply, result)

# Full mode (v4 API — one liner)
ctx   = sc.chat(user_id, message)
reply = your_llm(ctx.messages)
reply = ctx.save(reply)

Works with any LLM

# Gemini
import litellm
reply = litellm.completion(model="gemini/gemini-2.0-flash",
    messages=ctx.messages).choices[0].message.content

# OpenAI
from openai import OpenAI
reply = OpenAI().chat.completions.create(
    model="gpt-4o", messages=ctx.messages).choices[0].message.content

# Ollama (local)
import ollama
reply = ollama.chat(model="llama3", messages=ctx.messages)["message"]["content"]

# Anthropic Claude
import anthropic
sys_msg = next(m["content"] for m in ctx.messages if m["role"] == "system")
history = [m for m in ctx.messages if m["role"] != "system"]
reply = anthropic.Anthropic().messages.create(
    model="claude-3-5-sonnet-20241022",
    system=sys_msg, messages=history, max_tokens=1024).content[0].text

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🤖 Agent System

Define agents in YAML. Bot doesn't need to restart when you edit or add agents.

# agents/coding.yaml
name: coding
description: Expert programmer for all languages

triggers:
  keywords: [code, bug, error, python, javascript, debug, fix]
  priority: 10

personality:
  default: |
    You are a senior software engineer.
    Always use proper code blocks with language tags.
  beginner: |
    You are a patient programming teacher.
    Explain every step with simple examples.
  expert: |
    Principal engineer. Be concise and technical.

skills:
  - name: code_format
    content: Always use ```language for all code.
    priority: 10

chain:
  - condition: deploy OR server OR docker
    to: devops
    message: Routing to DevOps agent.

Add a new agent = create a new .yaml file in agents/. Done.

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📖 API Reference

Memory (v3 API)

mem = sc.memory(user_id)

mem.add_user("hello!")
mem.add_assistant("hi there!")
history = mem.get_for_llm(limit=10)   # ready for LLM

# Persistent user facts
mem.remember("name", "Alice")
mem.remember("stack", "Python + FastAPI")
mem.recall("name")                    # → "Alice"

# Compress old messages into episodic summary
mem.compress(keep_last=10)

TieredMemory (v4 API)

ctx = sc.context(user_id)

ctx.working.add("debug this error", NodeKind.MESSAGE)
ctx.episodic.add("session summary", NodeKind.SUMMARY)
ctx.semantic.add("user uses Proxmox", NodeKind.FACT, importance=0.8)

ctx.stats()   # → {"working": 5, "episodic": 1, "semantic": 3}
ctx.prune()   # remove expired + deleted nodes from DB

Intent → Retrieval Strategy

Intent	Working	Episodic	Semantic	Skills
conversation	✅	✅	❌	❌
personal	✅	❌	✅	❌
coding	✅	✅	✅	✅
knowledge	❌	❌	✅	❌
task	✅	✅	✅	✅

Debug & Utilities

sc.enable_debug(True)         # log retrieval pipeline details
sc.apply_decay(user_id)       # apply importance decay (call periodically)
sc.apply_decay()              # apply to all users

stats = sc.engine.get_stats(plan)
# → {"candidates": 37, "active": 28, "selected": 11, "total_chars": 3200}

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⚙️ Configuration

# config.yaml
storage:
  backend: sqlite          # sqlite | memory | redis | postgresql
  path: ./sc_data.db

memory:
  default_limit: 20
  ttl_hours:
    working: 2             # working nodes expire after 2 hours
    episodic: 720          # episodic nodes expire after 30 days
  compression:
    enabled: false
    threshold: 50
    keep_last: 10

agents:
  folder: ./agents
  hot_reload: true
  default: general

plugins:
  enabled: true
  folder: ./plugins

debug:
  retrieval: false

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🔌 Plugin System

from simplecontext.plugins.base import BasePlugin

class MyPlugin(BasePlugin):
    name       = "my_plugin"
    depends_on = []            # declare dependencies

    def setup(self):
        self.count = self.state.get("count", 0)  # persistent state

    # Hooks available:
    def on_message_saved(self, user_id, role, content, tags, metadata): ...
    def on_before_llm(self, user_id, agent_id, messages) -> list: ...
    def on_after_llm(self, user_id, agent_id, response) -> str: ...
    def on_agent_routed(self, user_id, agent_id, message): ...
    def on_prompt_build(self, agent_id, prompt) -> str: ...
    def on_export(self, data) -> dict: ...

sc.use(MyPlugin())
# or drop the file in ./plugins/ — auto-loaded on startup

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📁 Project Structure

SimpleContext/
├── simplecontext/
│   ├── core.py              ← SimpleContext + ChatContext (entry point)
│   ├── memory.py            ← Memory (v3) + TieredMemory (v4)
│   ├── skills.py            ← Skills: groups, conditions, inheritance
│   ├── enums.py             ← Tier, NodeKind, NodeStatus, Intent
│   ├── context/
│   │   ├── node.py          ← ContextNode + validator
│   │   ├── planner.py       ← ContextPlanner + RetrievalPlan
│   │   ├── engine.py        ← ContextEngine facade + LRU cache
│   │   ├── retriever.py     ← collect candidates
│   │   ├── resolver.py      ← TTL → mark expired
│   │   ├── scorer.py        ← scoring formula
│   │   ├── selector.py      ← budget enforcement
│   │   ├── builder.py       ← PromptBuilder
│   │   ├── processor.py     ← MemoryProcessor + decay
│   │   └── cache.py         ← LRU cache
│   ├── storage/
│   │   ├── sqlite.py        ← default, zero install
│   │   ├── redis.py         ← pip install redis
│   │   └── postgres.py      ← pip install psycopg2-binary
│   ├── agent/
│   │   ├── schema.py        ← parse YAML agent definitions
│   │   ├── registry.py      ← hot-reload agent files
│   │   └── router.py        ← TF-IDF routing + chaining
│   └── plugins/
│       ├── base.py          ← BasePlugin + hooks
│       ├── loader.py        ← dynamic loader + dependency resolver
│       └── state.py         ← persistent plugin state
├── agents/                  ← agent YAML definitions
├── plugins/                 ← drop custom plugins here
├── tests/
│   ├── test_all.py          ← 107 unit tests
│   └── test_benchmark.py    ← 28 accuracy + benchmark tests
└── config.yaml.example

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📊 Comparison

	SimpleContext	OpenViking	LangChain	AutoGPT
Setup time	< 1 min	30+ min	~5 min	~10 min
Dependencies	Zero	Go + VLM	Many	Many
Tiered Memory	✅	❌	❌	❌
Intent Planning	✅	❌	❌	❌
Context Scoring	✅	❌	❌	❌
Fact Extraction	✅	❌	❌	❌
Conflict Handling	✅	❌	❌	❌
Agent YAML + Hot-reload	✅	❌	❌	❌
Agent Chaining	✅	❌	⚠️	⚠️
Plugin System	✅	❌	⚠️	❌
Multi-Storage	✅	VectorDB	VectorDB	VectorDB
Semantic Search	✅ via plugin	✅ vector	✅ vector	✅ vector

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🌐 Ecosystem

SimpleContext adalah core engine dari ekosistem yang terus berkembang. Gunakan bersama repositori lain untuk setup yang lebih lengkap:

Repositori	Deskripsi
SimpleContext	Core engine — Universal AI Brain (repo ini)
SimpleContext-Plugin	Official & community plugin registry — tambah kemampuan via drop-in plugins
SimpleContext-Bot	AI Telegram Bot powered by SimpleContext — one-command setup, auto-downloads engine + agents
SimpleContext-Agents	Ready-to-use agent definitions — koleksi YAML agent siap pakai

Contoh setup ekosistem penuh

SimpleContext          ← otak / engine
       │
       ├── SimpleContext-Agents   ← definisi agent (YAML)
       ├── SimpleContext-Plugin   ← plugin tambahan (vector search, dll)
       └── SimpleContext-Bot      ← interface ke user (Telegram)

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🤝 Call for Contributors

SimpleContext butuh plugin buatanmu.

Plugin system sudah siap — kamu tinggal buat satu file Python dan submit ke SimpleContext-Plugin. Tidak perlu fork core, tidak perlu setup rumit.

Plugin apa yang dibutuhkan?

Beberapa ide yang belum ada dan sangat berguna:

Ide Plugin	Deskripsi
plugin-auto-tagger	Tag otomatis setiap pesan berdasarkan keyword rules
plugin-summarizer	Auto-compress working memory ke episodic via LLM
plugin-sentiment	Deteksi sentimen user, simpan ke metadata
plugin-rate-limiter	Batasi frekuensi request per user
plugin-webhook	Kirim event ke endpoint eksternal via HTTP
plugin-translate	Auto-translate pesan ke bahasa tertentu
plugin-analytics	Dashboard statistik penggunaan per user/agent

Seberapa susah membuat plugin?

# Ini sudah cukup untuk jadi plugin yang valid:
from simplecontext.plugins.base import BasePlugin

class MyPlugin(BasePlugin):
    name    = "my_plugin"
    version = "1.0.0"

    def on_before_llm(self, user_id, agent_id, messages):
        # lakukan sesuatu sebelum LLM dipanggil
        return messages

Satu file. Drop ke plugins/. Selesai.

Cara kontribusi

1. Buka https://github.com/zacxyonly/SimpleContext-Plugin
2. Fork → buat plugin di community/plugin-namakalian/
3. Ikuti panduan di CONTRIBUTING.md
4. Submit Pull Request

💡 Punya ide plugin tapi tidak yakin cara implementasinya? Buka issue di SimpleContext-Plugin — diskusikan dulu, baru build.

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🧪 Tests

python -m unittest discover tests -v
# Ran 135 tests in 1.2s — OK

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📄 License

MIT — free to use, modify, and distribute.

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Built with ❤️ — zero dependencies, maximum brain.

⭐ Star this repo if you find it useful!

SimpleContext-Plugin · SimpleContext-Bot · SimpleContext-Agents