Metadata-Version: 2.4
Name: soma-wisdom
Version: 0.9.1
Summary: SOMA: Somatic Wisdom Architecture — Wisdom over Memory
Project-URL: Homepage, https://github.com/sunyan999999/soma
Project-URL: Repository, https://github.com/sunyan999999/soma.git
Project-URL: Issues, https://github.com/sunyan999999/soma/issues
Project-URL: Documentation, https://sunyan999999.github.io/soma/
Project-URL: PyPI, https://pypi.org/project/soma-wisdom/
Author: SOMA Project Team
License:                                  Apache License
                                   Version 2.0, January 2004
                                http://www.apache.org/licenses/
        
           TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
        
           1. Definitions.
        
              "License" shall mean the terms and conditions for use, reproduction,
              and distribution as defined by Sections 1 through 9 of this document.
        
              "Licensor" shall mean the copyright owner or entity authorized by
              the copyright owner that is granting the License.
        
              "Legal Entity" shall mean the union of the acting entity and all
              other entities that control, are controlled by, or are under common
              control with that entity. For the purposes of this definition,
              "control" means (i) the power, direct or indirect, to cause the
              direction or management of such entity, whether by contract or
              otherwise, or (ii) ownership of fifty percent (50%) or more of the
              outstanding shares, or (iii) beneficial ownership of such entity.
        
              "You" (or "Your") shall mean an individual or Legal Entity
              exercising permissions granted by this License.
        
              "Source" form shall mean the preferred form for making modifications,
              including but not limited to software source code, documentation
              source, and configuration files.
        
              "Object" form shall mean any form resulting from mechanical
              transformation or translation of a Source form, including but
              not limited to compiled object code, generated documentation,
              and conversions to other media types.
        
              "Work" shall mean the work of authorship, whether in Source or
              Object form, made available under the License, as indicated by a
              copyright notice that is included in or attached to the work
              (an example is provided in the Appendix below).
        
              "Derivative Works" shall mean any work, whether in Source or Object
              form, that is based on (or derived from) the Work and for which the
              editorial revisions, annotations, elaborations, or other modifications
              represent, as a whole, an original work of authorship. For the purposes
              of this License, Derivative Works shall not include works that remain
              separable from, or merely link (or bind by name) to the interfaces of,
              the Work and Derivative Works thereof.
        
              "Contribution" shall mean any work of authorship, including
              the original version of the Work and any modifications or additions
              to that Work or Derivative Works thereof, that is intentionally
              submitted to Licensor for inclusion in the Work by the copyright owner
              or by an individual or Legal Entity authorized to submit on behalf of
              the copyright owner. For the purposes of this definition, "submitted"
              means any form of electronic, verbal, or written communication sent
              to the Licensor or its representatives, including but not limited to
              communication on electronic mailing lists, source code control systems,
              and issue tracking systems that are managed by, or on behalf of, the
              Licensor for the purpose of discussing and improving the Work, but
              excluding communication that is conspicuously marked or otherwise
              designated in writing by the copyright owner as "Not a Contribution."
        
              "Contributor" shall mean Licensor and any individual or Legal Entity
              on behalf of whom a Contribution has been received by Licensor and
              subsequently incorporated within the Work.
        
           2. Grant of Copyright License. Subject to the terms and conditions of
              this License, each Contributor hereby grants to You a perpetual,
              worldwide, non-exclusive, no-charge, royalty-free, irrevocable
              copyright license to reproduce, prepare Derivative Works of,
              publicly display, publicly perform, sublicense, and distribute the
              Work and such Derivative Works in Source or Object form.
        
           3. Grant of Patent License. Subject to the terms and conditions of
              this License, each Contributor hereby grants to You a perpetual,
              worldwide, non-exclusive, no-charge, royalty-free, irrevocable
              (except as stated in this section) patent license to make, have made,
              use, offer to sell, sell, import, and otherwise transfer the Work,
              where such license applies only to those patent claims licensable
              by such Contributor that are necessarily infringed by their
              Contribution(s) alone or by combination of their Contribution(s)
              with the Work to which such Contribution(s) was submitted. If You
              institute patent litigation against any entity (including a
              cross-claim or counterclaim in a lawsuit) alleging that the Work
              or a Contribution incorporated within the Work constitutes direct
              or contributory patent infringement, then any patent licenses
              granted to You under this License for that Work shall terminate
              as of the date such litigation is filed.
        
           4. Redistribution. You may reproduce and distribute copies of the
              Work or Derivative Works thereof in any medium, with or without
              modifications, and in Source or Object form, provided that You
              meet the following conditions:
        
              (a) You must give any other recipients of the Work or
                  Derivative Works a copy of this License; and
        
              (b) You must cause any modified files to carry prominent notices
                  stating that You changed the files; and
        
              (c) You must retain, in the Source form of any Derivative Works
                  that You distribute, all copyright, patent, trademark, and
                  attribution notices from the Source form of the Work,
                  excluding those notices that do not pertain to any part of
                  the Derivative Works; and
        
              (d) If the Work includes a "NOTICE" text file as part of its
                  distribution, then any Derivative Works that You distribute must
                  include a readable copy of the attribution notices contained
                  within such NOTICE file, excluding those notices that do not
                  pertain to any part of the Derivative Works, in at least one
                  of the following places: within a NOTICE text file distributed
                  as part of the Derivative Works; within the Source form or
                  documentation, if provided along with the Derivative Works; or,
                  within a display generated by the Derivative Works, if and
                  wherever such third-party notices normally appear. The contents
                  of the NOTICE file are for informational purposes only and
                  do not modify the License. You may add Your own attribution
                  notices within Derivative Works that You distribute, alongside
                  or as an addendum to the NOTICE text from the Work, provided
                  that such additional attribution notices cannot be construed
                  as modifying the License.
        
              You may add Your own copyright statement to Your modifications and
              may provide additional or different license terms and conditions
              for use, reproduction, or distribution of Your modifications, or
              for any such Derivative Works as a whole, provided Your use,
              reproduction, and distribution of the Work otherwise complies with
              the conditions stated in this License.
        
           5. Submission of Contributions. Unless You explicitly state otherwise,
              any Contribution intentionally submitted for inclusion in the Work
              by You to the Licensor shall be under the terms and conditions of
              this License, without any additional terms or conditions.
              Notwithstanding the above, nothing herein shall supersede or modify
              the terms of any separate license agreement you may have executed
              with Licensor regarding such Contributions.
        
           6. Trademarks. This License does not grant permission to use the trade
              names, trademarks, service marks, or product names of the Licensor,
              except as required for reasonable and customary use in describing the
              origin of the Work and reproducing the content of the NOTICE file.
        
           7. Disclaimer of Warranty. Unless required by applicable law or
              agreed to in writing, Licensor provides the Work (and each
              Contributor provides its Contributions) on an "AS IS" BASIS,
              WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
              implied, including, without limitation, any warranties or conditions
              of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
              PARTICULAR PURPOSE. You are solely responsible for determining the
              appropriateness of using or redistributing the Work and assume any
              risks associated with Your exercise of permissions under this License.
        
           8. Limitation of Liability. In no event and under no legal theory,
              whether in tort (including negligence), contract, or otherwise,
              unless required by applicable law (such as deliberate and grossly
              negligent acts) or agreed to in writing, shall any Contributor be
              liable to You for damages, including any direct, indirect, special,
              incidental, or consequential damages of any character arising as a
              result of this License or out of the use or inability to use the
              Work (including but not limited to damages for loss of goodwill,
              work stoppage, computer failure or malfunction, or any and all
              other commercial damages or losses), even if such Contributor
              has been advised of the possibility of such damages.
        
           9. Accepting Warranty or Additional Liability. While redistributing
              the Work or Derivative Works thereof, You may choose to offer,
              and charge a fee for, acceptance of support, warranty, indemnity,
              or other liability obligations and/or rights consistent with this
              License. However, in accepting such obligations, You may act only
              on Your own behalf and on Your sole responsibility, not on behalf
              of any other Contributor, and only if You agree to indemnify,
              defend, and hold each Contributor harmless for any liability
              incurred by, or claims asserted against, such Contributor by reason
              of your accepting any such warranty or additional liability.
        
           END OF TERMS AND CONDITIONS
        
           APPENDIX: How to apply the Apache License to your work.
        
              To apply the Apache License to your work, attach the following
              boilerplate notice, with the fields enclosed by brackets "[]"
              replaced with your own identifying information. (Don't include
              the brackets!)  The text should be enclosed in the appropriate
              comment syntax for the file format. We also recommend that a
              file or class name and description of purpose be included on the
              same "printed page" as the copyright notice for easier
              identification within third-party archives.
        
           Copyright 2025 SOMA Project Team
        
           Licensed under the Apache License, Version 2.0 (the "License");
           you may not use this file except in compliance with the License.
           You may obtain a copy of the License at
        
               http://www.apache.org/licenses/LICENSE-2.0
        
           Unless required by applicable law or agreed to in writing, software
           distributed under the License is distributed on an "AS IS" BASIS,
           WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
           See the License for the specific language governing permissions and
           limitations under the License.
License-File: LICENSE
Keywords: agent,cognitive,llm,memory,rag,wisdom
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.10
Requires-Dist: faiss-cpu>=1.8.0
Requires-Dist: fastembed>=0.4.0
Requires-Dist: jieba>=0.42
Requires-Dist: litellm>=1.0.0
Requires-Dist: networkx>=3.0
Requires-Dist: numpy>=1.24
Requires-Dist: pydantic>=2.0
Requires-Dist: pyyaml>=6.0
Requires-Dist: scikit-learn>=1.3.0
Requires-Dist: tenacity>=8.0
Provides-Extra: alpha
Requires-Dist: langchain-core>=0.3.0; extra == 'alpha'
Requires-Dist: streamlit>=1.40.0; extra == 'alpha'
Provides-Extra: dev
Requires-Dist: mypy>=1.5; extra == 'dev'
Requires-Dist: pytest-asyncio>=0.21; extra == 'dev'
Requires-Dist: pytest-cov>=4.0; extra == 'dev'
Requires-Dist: pytest-timeout>=2.0; extra == 'dev'
Requires-Dist: pytest>=7.0; extra == 'dev'
Requires-Dist: ruff>=0.1; extra == 'dev'
Description-Content-Type: text/markdown

# SOMA — Somatic Wisdom Architecture

<p align="center">
  <strong>Wisdom over Memory — 智慧超越记忆</strong><br>
  <em>AI agents shouldn't just remember. They should <strong>understand</strong>.</em>
</p>

```bash
pip install soma-wisdom     # 5 minutes from zero to thinking agent
```

```python
from soma import SOMA

soma = SOMA()
soma.remember("First-principles thinking: deconstruct to fundamentals...",
              context={"domain": "philosophy"}, importance=0.9)
answer = soma.respond("How to analyze our growth bottleneck?")
# → decomposes through 7 thinking laws → activates relevant memories → returns reasoned answer
```

**Why SOMA instead of a vector database?** Traditional memory (ChromaDB, Mem0) stores and retrieves. SOMA **thinks first**: a 7-law reasoning network decomposes problems *before* fetching memories. The result: agents that systematically analyze, not just pattern-match.

| | Vector DBs | Mem0 | **SOMA** |
|---|---|---|---|
| Stores & retrieves | ✓ | ✓ | ✓ |
| Reasoning framework | ✗ | ✗ | **✓ 7 thinking laws** |
| Self-evolution | ✗ | ✗ | **✓ weights auto-tune** |
| Consolidation + forgetting | ✗ | partial | **✓ Ebbinghaus decay** |
| Causal reasoning | ✗ | ✗ | **✓ graph chain inference** |
| Cross-domain analogy | ✗ | ✗ | **✓ structural pattern matching** |
| Conflict detection | ✗ | ✗ | **✓ contradiction flagging** |
| Multi-agent collaboration | ✗ | ✗ | **✓ expert routing + consensus** |
| Frame anchoring awareness | ✗ | ✗ | **✓ cognitive bias nudge** |
| Offline / zero infra | varies | ✗ (OpenAI) | **✓ ONNX, SQLite** |

<p align="center">
  <a href="https://github.com/sunyan999999/soma"><img src="https://img.shields.io/github/stars/sunyan999999/soma?style=social" alt="GitHub stars"></a>
  <a href="LICENSE"><img src="https://img.shields.io/badge/license-Apache%202.0-blue" alt="License"></a>
  <a href="#"><img src="https://img.shields.io/badge/version-0.9.1-blue" alt="Version"></a>
  <a href="#"><img src="https://img.shields.io/badge/python-3.10%2B-green" alt="Python"></a>
  <a href="#benchmarks"><img src="https://img.shields.io/badge/semantic_recall-100%25-brightgreen" alt="Semantic Recall"></a>
  <a href="#benchmarks"><img src="https://img.shields.io/badge/overall_score-80.5%2F100-blue" alt="Overall Score"></a>
  <a href="#"><img src="https://img.shields.io/badge/tests-485%2F486-brightgreen" alt="Tests"></a>
  <a href="CHANGELOG.md"><img src="https://img.shields.io/badge/changelog-v0.9.1-success" alt="Changelog"></a>
</p>

📖 **[中文文档](README_zh.md)** | **[Docs](https://sunyan999999.github.io/soma/)** | **[Demo](https://github.com/sunyan999999/soma-demo)** | **[Roadmap](ROADMAP.md)** | **[Changelog](CHANGELOG.md)** | **[Contributing](CONTRIBUTING.md)**

<p align="center">
  <img src="https://raw.githubusercontent.com/sunyan999999/soma/main/docs/images/demo-pipeline.gif" alt="SOMA Pipeline Demo" width="720">
</p>

## Architecture

```
┌──────────────────────────────────────────────────────────────────────────────┐
│                         SOMA Agent (v0.9.1)                                    │
│                                                                                │
│  ┌──────────────────────────────────────────────────────────────────┐        │
│  │  v0.9.1 Sunyata Awareness Layer ⚡ — 零熵觉察层                    │        │
│  │  FrameAnchoringDetector · 框架锁定检测 · 觉察提示（脚注式低干扰） │        │
│  └──────────────────────────────────────────────────────────────────┘        │
│                                                                                │
│  ┌──────────────────────────────────────────────────────────────────┐        │
│  │  v0.9.0 Multi-Agent Collaboration ⚡ — 多智能体协作                │        │
│  │  AgentRegistry · ExpertRouter · ConsensusProtocol · DistributedEvolver │   │
│  └──────────────────────────────────────────────────────────────────┘        │
│                                                                                │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────────────────────────┐    │
│  │ WisdomEngine  │→│ActivationHub │→│           MemoryCore               │    │
│  │ · 关键词匹配  │  │ · 双向激活   │  │ · episodic/semantic/skill         │    │
│  │ · 规律链传播  │  │ · 反视角检索 │  │ · SQLite + vector + FTS5          │    │
│  │ · 组合模板    │  │ · 冲突检测⚡ │  │ · 加权RRF + 时间衰减              │    │
│  │ · 语义兜底    │  │ · 反向传播⚡ │  │ · 图谱扩展检索⚡                  │    │
│  │ · 语境排序    │  │ · MMR重排    │  │ · 跨域类比引擎⚡                  │    │
│  └──────────────┘  └──────────────┘  └──────────────────────────────────┘    │
│         │                  │                        │                        │
│         ▼                  ▼                        ▼                        │
│  ┌──────────────┐  ┌──────────────────────────────────────────────────┐      │
│  │ 复杂度评估    │  │              MetaEvolver                         │      │
│  └──────────────┘  │ · 偏差检测 → 自动调权 → 技能固化                  │      │
│         │          │ · 触发词扩展 · 思维模板挖掘 · 动态步长            │      │
│         ▼          └──────────────────────────────────────────────────┘      │
│  ┌──────────────────────────────────────────────────────────────────┐       │
│  │  v0.6 Reasoning Engine           │  v0.8 Causal + Conflict ⚡     │       │
│  │  · 17 reasoning templates        │  · CausalGraph 因果链推理      │       │
│  │  · Hypothesis + evidence matrix  │  · ConflictDetector 矛盾检测   │       │
│  │  · Auto-extract triples          │  · QualityEvaluator 反思评分   │       │
│  └──────────────────────────────────────────────────────────────────┘       │
└──────────────────────────────────────────────────────────────────────────────┘

Thirteen-Stage Wisdom Pipeline:
  Assess → Decompose → Chain → Combine → Semantic-fallback
         → Context-sort → Activate → Conflict-detect⚡ → Frame-anchoring⚡ → Anti-bias
         → Reason → Synthesize → Causal-extract → Backward-propagate⚡ → Evolve
```

## Screenshots

<p align="center">
  <strong>🧠 Wisdom Chat</strong> &nbsp;·&nbsp; <strong>📊 5D Benchmark</strong> &nbsp;·&nbsp; <strong>💻 IDE Integration</strong> &nbsp;·&nbsp; <strong>🔌 REST API</strong>
</p>

<table>
<tr>
<td width="50%"><a href="https://raw.githubusercontent.com/sunyan999999/soma/main/docs/images/screenshot-chat.jpeg"><img src="https://raw.githubusercontent.com/sunyan999999/soma/main/docs/images/screenshot-chat.jpeg" alt="SOMA ChatView — 智者对话" width="100%"></a></td>
<td width="50%"><a href="https://raw.githubusercontent.com/sunyan999999/soma/main/docs/images/screenshot-benchmark.jpeg"><img src="https://raw.githubusercontent.com/sunyan999999/soma/main/docs/images/screenshot-benchmark.jpeg" alt="SOMA BenchmarkView — 五维基准雷达图" width="100%"></a></td>
</tr>
<tr>
<td align="center"><b>Wisdom Chat</b> — 7条规律分解问题，双向记忆激活，LLM流式响应</td>
<td align="center"><b>5D Benchmark</b> — 记忆/智慧/进化/伸缩/综合，竞品实时对比</td>
</tr>
<tr>
<td width="50%"><a href="https://raw.githubusercontent.com/sunyan999999/soma/main/docs/images/screenshot-ide.jpeg"><img src="https://raw.githubusercontent.com/sunyan999999/soma/main/docs/images/screenshot-ide.jpeg" alt="SOMA IDE Integration — Claude Code 集成" width="100%"></a></td>
<td width="50%"><a href="https://raw.githubusercontent.com/sunyan999999/soma/main/docs/images/screenshot-api.jpeg"><img src="https://raw.githubusercontent.com/sunyan999999/soma/main/docs/images/screenshot-api.jpeg" alt="SOMA REST API — 完整接口文档" width="100%"></a></td>
</tr>
<tr>
<td align="center"><b>IDE Integration</b> — Claude Code / VS Code 一键接入，记忆自动持久化</td>
<td align="center"><b>REST API</b> — FastAPI + SSE 流式，多模型管理，API Key 认证</td>
</tr>
</table>

## Dashboard

Start the API server and open `http://localhost:8765`:

```bash
SOMA_API_KEY=test python dash/server.py
```

Vue 3 dashboard with i18n (English / 中文), 6 views: Chat · Framework · Memory · Analytics · Benchmark · Settings.

## Installation

```bash
pip install soma-wisdom
```

Requires **Python 3.10+**. The embedding engine uses ONNX Runtime for CPU inference — no CUDA, no Docker, no external services.

First run downloads a small ONNX model (~100 MB, Chinese-English bilingual).

```bash
python -m soma          # verify everything works in one command
soma-quickstart         # or use the CLI entry point
```

## Core Concepts

### 1. Wisdom Framework — 7 Thinking Laws

| Law | Description | Weight |
|-----|-------------|:---:|
| `first_principles` | Reduce to fundamentals, derive from base elements | 0.90 |
| `systems_thinking` | See interconnected wholes, identify feedback loops | 0.85 |
| `contradiction_analysis` | Find opposing forces, identify principal contradictions | 0.80 |
| `pareto_principle` | Focus on the vital 20% that drives 80% of outcomes | 0.75 |
| `inversion` | Think backwards — how could this fail? | 0.70 |
| `analogical_reasoning` | Map structures across domains | 0.65 |
| `evolutionary_lens` | Observe change over time, identify lifecycle stages | 0.60 |

Customize in `wisdom_laws.yaml` (bundled in the package — always available).

**New in v0.5**: Laws are no longer a flat list — they form a **reasoning network**. When a law triggers, its `relations` propagate activation to related laws (×0.35–0.50 bonus). When two laws fire together, synthesized perspectives emerge (e.g., "First Principles × Systems Thinking → Root-Cause System Analysis").

### 2. Bidirectional Activation — Hybrid RRF

Memories are matched through **weighted Reciprocal Rank Fusion**:
- Vector semantic similarity (×2 weight) via ONNX embeddings
- Keyword exact match (×1 weight)

Both directions compete and complement, producing true relevance scores.

### 3. Meta-Evolution — Self-Optimization

SOMA tracks success/failure of each thinking law across sessions. Every 5 sessions, `evolve()` automatically:
- **Memory consolidation**: similar memories automatically merged, reducing redundancy
- **Active forgetting**: low-value memories archived with Ebbinghaus decay curves
- **Bias detection**: laws used >40% of the time get penalized (-0.05) to prevent thinking ruts; underused high-success laws get boosted (+0.03)
- **Dynamic step sizing**: adjustment magnitude scales with sample count (0.01 → 0.02 → 0.03)
- **Skill solidification**: successful (law, domain, outcome) patterns become permanent skills after 3+ occurrences

### 4. Memory Types

| Type | Storage | Search |
|------|---------|--------|
| **Episodic** | SQLite + vector BLOB | Hybrid (semantic + keyword RRF) |
| **Semantic** | SQLite triple store | Keyword + graph traversal |
| **Skill** | SQLite pattern store | Keyword + domain matching |

### 5. v0.8.0 — Knowledge Graph & Reasoning Engine

Six new capabilities that upgrade SOMA from a memory store to a reasoning system:

**Graph Retrieval Expansion** — Keywords are no longer isolated. `_expand_via_semantic_graph()` traverses the knowledge graph (BFS depth=2) to discover neighbor concepts, breaking retrieval silos. O(1) hash lookup replaces full node scan.

**Causal Reasoning** — `CausalGraph` builds causal chains from semantic triples. `causal_analyze()` traces root causes and downstream effects, answering "why" questions with graph-backed evidence chains.

**Conflict Detection** — `ConflictDetector` identifies logically contradictory memories (e.g., "price drop → churn" vs "service quality → churn"). Conflicts are flagged with similarity-weighted scores and penalized in activation ranking. Batch ONNX encoding ensures sub-100ms detection.

**Bidirectional Activation v2** — High-activation memories now propagate backward to suggest new thinking foci (`_backward_propagate()`). The memory→focus feedback loop discovers perspectives the initial decomposition missed.

**Cross-Domain Analogy Engine** — `AnalogyEngine` maps structural patterns across domains. When two unrelated domains share identical graph topology (e.g., "supply chain bottleneck" ≈ "blood vessel blockage"), SOMA bridges them automatically. Structural signature caching avoids repeated graph scans.

**Quality Evaluation** — `QualityEvaluator` scores reasoning output on consistency (answer vs memory alignment), coherence (structure and logic), and actionability (concrete steps). Low-quality answers are flagged with `needs_reflection` for downstream handling.

> **Performance**: v0.8.0 query latency 209ms (v0.7.0: 33ms baseline, 1098ms pre-optimization). The 6x increase over v0.7.0 buys graph expansion + causal chains + conflict detection + cross-domain analogy — all in a single query path. For raw speed, use `query_memory()` which skips framework overhead.

### 6. v0.9.0 — Multi-Agent Collaboration

Four new modules that upgrade SOMA from a single thinking agent to a collaborative team:

**Agent Registry** — `AgentRegistry` formalizes agent expertise. Each agent registers with domain tags (e.g., "法律/合同/诉讼"), and `find_experts()` matches queries to specialists via exact (1.0) or fuzzy (0.7) tag matching. Zero external dependencies — pure in-memory dict + dataclass.

**Expert Router** — `ExpertRouter` uses a 3-tier routing strategy: L1 keyword match (8 domains × 80+ keywords, sub-ms), L2 semantic match (cosine similarity via ONNX), L3 default fallback. Zero LLM calls in routing decisions. Supports single-expert and multi-expert routing.

**Consensus Protocol** — `ConsensusProtocol` synthesizes multiple expert opinions through 3 strategies: L1 weighted voting (success-rate weighted), L2 LLM arbitration (for high-stakes decisions), L3 dialectic synthesis (thesis + antithesis → synthesis). Works without LLM in pure-rule mode.

**Distributed Evolution** — `DistributedEvolver` lets each agent evolve independently while periodically merging global weights (sample-count-weighted average). Conflict arbitration kicks in when weight divergence exceeds 0.2, preserving individual specialization while sharing collective experience.

**Memory Isolation** — Three-state memory isolation via `agent_id` + `group_id`: private (agent_id=self), group-shared (shared_group_id), and global (agent_id=""). All retrieval paths transparently respect isolation boundaries.

### 7. v0.9.1 — Sunyata Awareness Layer

A new dimension: **awareness**. SOMA now detects when you're over-anchored to a single cognitive frame and gently nudges — without blocking, forcing, or changing the pipeline.

**Frame Anchoring Detector** — `FrameAnchoringDetector` with 8 cognitive frame pairs (技术/商业/管理/法律/短期/长期/内求/外求). Pure keyword matching — zero LLM/embedder dependency. Detects when ≥60% of recent 5 turns fall into the same frame, then suggests neglected opposite frames as a blockquote footnote at the prompt's end.

**Backward Compatible by Design** — All new features controlled by `enable_frame_detection: bool = False`. Existing code upgrades with zero changes. The awareness nudge uses low-interference blockquote formatting at the prompt's end — it won't dominate the reasoning flow.

```python
soma = SOMA()
soma._agent.config.enable_frame_detection = True  # opt-in
# SOMA now notices when you're stuck in one perspective
# and adds a gentle footnote: "您已连续5轮从「技术视角」分析..."
```

## API Reference

### SOMA Facade (Python SDK)

```python
from soma import SOMA

soma = SOMA(
    framework_config=None,        # default: bundled wisdom_laws.yaml
    llm="deepseek-chat",          # LiteLLM model string
    use_vector_search=True,       # ONNX semantic search
    persist_dir="soma_data",      # persistence directory
    recall_threshold=0.01,        # minimum activation score
    top_k=5,                      # default recall count
    agent_id="",                  # v0.9.0: agent identity for multi-agent
    group_id="",                  # v0.9.0: group for shared memory
)

# Wisdom pipeline
soma.respond(problem: str) -> str
soma.chat(problem: str) -> dict          # structured: foci + memories + weights

# Memory operations
soma.remember(content, context, importance) -> str  # returns memory_id
soma.remember_semantic(subject, predicate, object_, confidence)
soma.query_memory(query: str, top_k: int) -> list

# Introspection & evolution
soma.decompose(problem: str) -> list     # show thinking dimensions
soma.reflect(task_id, outcome) -> None   # record outcome for evolution
soma.evolve() -> list                    # trigger automatic weight adjustment
soma.get_weights() -> dict               # current law weights
soma.adjust_weight(law_id, new_weight)   # manual override
soma.discover_laws() -> dict | None      # autonomous law discovery
soma.approve_law(candidate) -> bool      # approve a discovered law
soma.stats -> dict                       # memory store statistics

# v0.9.1: opt-in frame anchoring awareness
# soma._agent.config.enable_frame_detection = True
```

### REST API (Language-Agnostic)

```bash
# Start server
SOMA_API_KEY=your-key python dash/server.py    # → http://localhost:8765

# Standard chat
curl -X POST http://localhost:8765/api/chat \
  -H "X-API-Key: your-key" \
  -H "Content-Type: application/json" \
  -d '{"problem": "How to improve team productivity?"}'

# SSE streaming (decompose → activate → delta → done)
curl -X POST http://localhost:8765/api/chat/stream \
  -H "X-API-Key: your-key" \
  -H "Content-Type: application/json" \
  -d '{"problem": "Analyze our growth bottleneck"}'

# Memory search
curl -X POST http://localhost:8765/api/memory/search \
  -H "X-API-Key: your-key" \
  -H "Content-Type: application/json" \
  -d '{"query": "growth strategy", "top_k": 10}'
```

Set `SOMA_API_KEY` env var to enable authentication. Leave unset for local development.

### LangChain Tool

```python
from soma.langchain_tool import create_soma_tool
from soma.agent import SOMA_Agent
from soma.config import SOMAConfig, load_config

agent = SOMA_Agent(SOMAConfig(framework=load_config()))
tool = create_soma_tool(agent)
result = tool.run("Analyze this problem...")
```

### AI Coding Agent Integration (Claude Code / VS Code / JetBrains)

SOMA runs alongside AI coding tools as a persistent wisdom backend — it learns from every debug session, code review, and architectural decision.

```bash
# 1. Start SOMA server (once)
SOMA_API_KEY=dev-key python dash/server.py
```

**Claude Code (Agent SDK)** — use as a custom MCP server or REST tool:

```python
# In your custom Claude Agent tool — persist insights across sessions
import requests
requests.post("http://localhost:8765/api/memory/remember",
    headers={"X-API-Key": "dev-key"},
    json={"content": "Bug: N+1 query in OrderService.getOrders, root cause: missing @BatchSize on items relation, fix: add Hibernate batch annotation + integration test", "importance": 0.9})
```

**VS Code Extension** — invoke via sidebar or command palette with a thin HTTP client wrapper.

**Any IDE / CLI tool** — just curl the REST API. No SDK required.

```bash
# Record a debug finding
curl -s -X POST http://localhost:8765/api/memory/remember \
  -H "X-API-Key: dev-key" -H "Content-Type: application/json" \
  -d '{"content":"Race condition in WebSocket handler: concurrent map writes on client.buf, root cause: missing mutex on write path, fix: sync.RWMutex around buffer ops","importance":0.9}'

# Recall relevant knowledge before starting a new task
curl -s -X POST http://localhost:8765/api/memory/search \
  -H "X-API-Key: dev-key" -H "Content-Type: application/json" \
  -d '{"query":"concurrency websocket golang","top_k":5}'
```

### Real-World Impact

SOMA has been used in production across two distinct codebases — a Go-based CLI agent and a Python data platform — with the following results:

- **Bug reoccurrence dropped significantly.** When a bug fix is recorded as a SOMA memory (root cause + fix pattern), later sessions on the same codebase automatically retrieve it. Developers no longer re-debug the same class of issues across sprints.
- **Architectural decisions became searchable.** Each trade-off ("chose SQLite WAL over multi-worker for simplicity") is persisted with rationale context. Six months later, new team members can query "why single worker?" and get the original reasoning — not folklore.
- **Cross-project knowledge transfer worked.** A concurrency pattern learned in one codebase (the Go project) activated during debugging in the other (the Python project), because SOMA's semantic search matched "race condition" across language boundaries.
- **Zero adoption friction.** The Python project integrated via `pip install soma-wisdom` + 3 lines of code. The Go project integrated via REST API with a thin HTTP client (~50 lines). Neither required schema design, vector database setup, or infrastructure changes.
- **Evolution is automatic.** After ~50 sessions, SOMA's auto-weighting surfaced that "Inversion" (thinking backwards from failure) was consistently the most useful lens for debugging tasks, while "Analogical Reasoning" shined during architecture discussions. The framework self-tuned — no manual knob-twisting needed.

## Benchmarks

SOMA v0.9.1 — benchmarked with production memories from 零熵智库:

### Overall Score: 80.5/100

| Dimension | Score | Grade |
|-----------|:---:|:---:|
| **Overall** | **80.5** | Excellent |
| **Memory** | **88.4** | Excellent — 100% recall, 209ms query (graph+conflict+causal) |
| **Wisdom** | **85.5** | Excellent — causal analysis + cross-domain analogy active |
| **Evolution** | **68.7** | Good — 1,000+ reflections, weight auto-adaptation |
| **Scalability** | **100.0** | Excellent — linear scaling at 1K |

> Score change from v0.7.0 (84.8→80.5): v0.8.0 adds causal reasoning, conflict detection, and analogy to the scoring rubric. Memory dimension reflects new graph-expanded search overhead (209ms vs 3.74ms in v0.7.0 bare keyword path).

### Key Metrics

| Metric | Value | Stability |
|--------|:----:|:---:|
| Semantic Recall Rate | 100% | ● Stable |
| Dedup Ratio | 100% | ● Stable |
| Avg Insert Latency | 3.44ms | ◐ Acceptable |
| Query Latency (framework) | 209ms | ● Stable |
| Query Latency (simple) | ~30ms | ● Stable |
| Causal Chain Accuracy | 100% | ● Stable |
| Conflict Detection | <100ms (batch) | ● Stable |
| Decomposition Coverage | 100% | ● Stable |
| Thinking Diversity Entropy | 0.87 | ● Stable |

### Live Competitor Comparison

| System | Recall@5 | Reasoning | Evolution | Causal | Analogy | Conflict |
|--------|:---:|:---:|:---:|:---:|:---:|:---:|
| **SOMA v0.8** | **100%** | **✓** | **✓** | **✓** | **✓** | **✓** |
| ChromaDB | 2.5% | ✗ | ✗ | ✗ | ✗ | ✗ |
| Mem0 | * | ✗ | ✗ | ✗ | ✗ | ✗ |
| Zep | * | ✗ | ✗ | ✗ | ✗ | ✗ |

> SOMA is the only system combining a reasoning framework, causal analysis, conflict detection, cross-domain analogy, evolutionary self-optimization, memory consolidation, and active forgetting — all without external services.

Full report: [CHANGELOG.md](CHANGELOG.md)

### Reproducibility

```bash
pip install soma-wisdom chromadb
python -m soma.benchmarks --full --runs 5 --output reports/    # statistical benchmark
python scripts/live_benchmark.py --full --output reports/       # live competitor test
```

## Development

```bash
git clone https://github.com/soma-project/soma-core.git
cd soma-core
pip install -e ".[dev]"

pytest -v --cov=soma --cov-report=term    # 485+ tests, ~97% coverage

python -m soma                              # quickstart verification

python dash/server.py                       # API server (http://localhost:8765)
```

### Project Structure

```
soma-core/
├── soma/                  # Core library
│   ├── __init__.py        # SOMA facade (zero-config entry)
│   ├── __main__.py        # python -m soma quickstart
│   ├── agent.py           # SOMA_Agent: pipeline orchestrator + awareness ⚡
│   ├── engine.py          # WisdomEngine: problem decomposition
│   ├── hub.py             # ActivationHub: bidirectional activation
│   ├── evolve.py          # MetaEvolver: reflection + auto-evolution
│   ├── embedder.py        # SOMAEmbedder: fastembed + ONNX encoding
│   ├── vector_store.py    # NumpyVectorIndex: faiss ANN search
│   ├── config.py          # Pydantic configuration models + frame detection ⚡
│   ├── base.py            # Data models (Focus, MemoryUnit, etc.)
│   ├── abc.py             # Abstract base classes
│   ├── langchain_tool.py  # LangChain BaseTool wrapper
│   ├── law_discovery.py   # Autonomous law discovery from clusters
│   ├── retry.py           # LLM retry with exponential backoff
│   ├── plugin.py          # Entry-points plugin auto-discovery
│   ├── quality.py         # QualityEvaluator: reasoning output scoring ⚡
│   ├── analogy.py         # AnalogyEngine: cross-domain structural matching ⚡
│   ├── competitors.py     # Live competitor benchmark adapters
│   ├── analytics.py       # Usage analytics & benchmark storage
│   ├── benchmarks.py      # 5D benchmark engine (memory/wisdom/evolution/scalability/overall)
│   ├── wisdom_laws.yaml   # Default thinking framework (bundled)
│   ├── hub/
│   │   ├── _core.py       # ActivationHub: bidirectional activation + frame detection ⚡
│   │   ├── _conflict.py   # ConflictDetector: contradiction detection ⚡
│   │   ├── _frame_detector.py  # FrameAnchoringDetector: cognitive bias nudge ⚡
│   │   ├── _retriever.py  # MemoryRetriever: multi-path recall
│   │   ├── _scorer.py     # RelevanceScorer: weighted scoring
│   │   └── _ranker.py     # MMRRanker: diversity re-ranking
│   ├── multi_agent/       # v0.9.0 Multi-Agent Collaboration ⚡
│   │   ├── registry.py    # AgentRegistry: expert registration + matching
│   │   ├── router.py      # ExpertRouter: 3-tier routing (keyword/semantic/fallback)
│   │   ├── consensus.py   # ConsensusProtocol: vote/LLM/dialectic synthesis
│   │   └── evolve.py      # DistributedEvolver: independent evolution + weight merge
│   └── memory/
│       ├── core.py        # MemoryCore: unified memory facade + 3-state isolation ⚡
│       ├── episodic.py    # EpisodicStore: SQLite + vector BLOB
│       ├── semantic.py    # SemanticStore: knowledge triples + causal graph ⚡
│       ├── skill.py       # SkillStore: learned patterns
│       ├── causal.py      # CausalGraph: causal chain reasoning ⚡
│       ├── consolidation.py  # ConsolidationEngine: memory dedup
│       ├── forgetting.py     # ForgettingEngine: Ebbinghaus decay
│       ├── external.py       # External knowledge import (Markdown/JSON/URL)
│       └── search_utils.py   # FTS5 shared search utilities
├── dash/                  # Dashboard & API server
│   ├── server.py          # FastAPI (REST + SSE streaming + auth, version auto-detect ⚡)
│   ├── providers.py       # LLM provider manager
│   └── frontend/          # Vue 3 dashboard UI (i18n: EN/ZH)
├── docs/                  # Documentation (EN + ZH bilingual)
│   ├── contribution-audit-standards.md  # Law contribution audit standards (D4) ⚡
│   ├── v0.9.0-capabilities.md           # v0.9.0 capability overview
│   └── v0.9.1-零熵整合方案.md            # v0.9.1 integration plan
├── tests/                 # 485+ tests, ~97% coverage
├── examples/              # Usage examples
└── pyproject.toml         # Build config (version auto-detect)
```

## Citation

```bibtex
@software{soma2025,
  title        = {SOMA: Somatic Wisdom Architecture},
  author       = {SOMA Project Team},
  year         = {2025},
  url          = {https://github.com/soma-project/soma-core},
  note         = {Apache 2.0},
}
```

## License

Apache License 2.0. See [LICENSE](LICENSE).

---

<p align="center">
  <sub>🧠 五分钟接入，给你的 Agent 一个会思考的灵魂</sub>
</p>
