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fix(hanzo-tools-agent): add missing semaphore in deprecated swarm tool

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Fixes F821 undefined name error in swarm_tool_v1_deprecated.py
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zeekay
2025-12-26 03:58:20 -08:00
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commit 7144f12c22
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Metadata-Version: 2.4
Name: hanzo-aci
Version: 0.2.8
Summary: An Agent-Computer Interface (ACI) designed for software development agents Dev.
Author: Hanzo Industries Inc
License: MIT
Requires-Python: >=3.12
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: scipy
Requires-Dist: networkx
Requires-Dist: litellm
Requires-Dist: gitpython
Requires-Dist: tree-sitter>=0.24.0
Requires-Dist: tree-sitter-python>=0.23.6
Requires-Dist: tree-sitter-javascript>=0.23.1
Requires-Dist: tree-sitter-typescript>=0.23.2
Requires-Dist: tree-sitter-ruby>=0.23.1
Requires-Dist: grep-ast>=0.8.1
Requires-Dist: flake8
Requires-Dist: whatthepatch>=1.0.6
Requires-Dist: binaryornot>=0.4.4
Requires-Dist: cachetools>=5.5.2
Requires-Dist: charset-normalizer>=3.4.1
Provides-Extra: dev
Requires-Dist: ruff>=0.7.2; extra == "dev"
Requires-Dist: pre-commit>=4.0.1; extra == "dev"
Provides-Extra: test
Requires-Dist: pytest>=8.3.3; extra == "test"
Requires-Dist: psutil>=5.9.8; extra == "test"
Dynamic: license-file
# Agent-Computer Interface (ACI) for Dev
An Agent-Computer Interface (ACI) designed for software development agents [Dev](https://github.com/hanzoai/Dev). This package provides essential tools and interfaces for AI agents to interact with computer systems for software development tasks.
## Features
- **Code Editor Interface**: Sophisticated editing capabilities through the `editor` module
- File creation and modification
- Code editing
- Configuration management
- **Code Linting**: Built-in linting capabilities via the `linter` module
- Tree-sitter based code analysis
- Python-specific linting support
- **Utility Functions**: Helper modules for common operations
- Shell command execution utilities
- Diff generation and analysis
- Logging functionality
## Installation
```bash
pip install dev-aci
```
Or using Poetry:
```bash
poetry add dev-aci
```
## Project Structure
```
dev_aci/
├── editor/ # Code editing functionality
├── linter/ # Code linting capabilities
└── utils/ # Utility functions
```
## Development
1. Clone the repository:
```bash
git clone https://github.com/hanzoai/dev-aci.git
cd dev-aci
```
2. Install development dependencies:
```bash
poetry install
```
3. Configure pre-commit-hooks
```bash
make install-pre-commit-hooks
```
4. Run tests:
```bash
poetry run pytest
```
## License
This project is licensed under the MIT License.
pkg/hanzo-aci/hanzo_aci.egg-info/SOURCES.txt
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LICENSE
README.md
pyproject.toml
dev_aci/__init__.py
dev_aci/editor/__init__.py
dev_aci/editor/config.py
dev_aci/editor/editor.py
dev_aci/editor/encoding.py
dev_aci/editor/exceptions.py
dev_aci/editor/file_cache.py
dev_aci/editor/history.py
dev_aci/editor/prompts.py
dev_aci/editor/results.py
dev_aci/linter/__init__.py
dev_aci/linter/base.py
dev_aci/linter/linter.py
dev_aci/linter/impl/python.py
dev_aci/linter/impl/treesitter.py
dev_aci/linter/impl/treesitter_compat.py
dev_aci/utils/diff.py
dev_aci/utils/logger.py
dev_aci/utils/shell.py
hanzo_aci.egg-info/PKG-INFO
hanzo_aci.egg-info/SOURCES.txt
hanzo_aci.egg-info/dependency_links.txt
hanzo_aci.egg-info/requires.txt
hanzo_aci.egg-info/top_level.txt
pkg/hanzo-aci/hanzo_aci.egg-info/dependency_links.txt
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pkg/hanzo-aci/hanzo_aci.egg-info/requires.txt
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numpy
pandas
scipy
networkx
litellm
gitpython
tree-sitter>=0.24.0
tree-sitter-python>=0.23.6
tree-sitter-javascript>=0.23.1
tree-sitter-typescript>=0.23.2
tree-sitter-ruby>=0.23.1
grep-ast>=0.8.1
flake8
whatthepatch>=1.0.6
binaryornot>=0.4.4
cachetools>=5.5.2
charset-normalizer>=3.4.1
[dev]
ruff>=0.7.2
pre-commit>=4.0.1
[test]
pytest>=8.3.3
psutil>=5.9.8
pkg/hanzo-aci/hanzo_aci.egg-info/top_level.txt
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dev_aci
pkg/hanzo-memory/src/hanzo_memory.egg-info/PKG-INFO
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Metadata-Version: 2.4
Name: hanzo-memory
Version: 1.0.1
Summary: AI memory service with FastAPI and MCP support
Author-email: "Hanzo Industries Inc." <[email protected]>
License: BSD
Project-URL: Homepage, https://github.com/hanzoai/memory
Project-URL: Documentation, https://docs.hanzo.ai/memory
Project-URL: Repository, https://github.com/hanzoai/memory
Project-URL: Issues, https://github.com/hanzoai/memory/issues
Keywords: ai,memory,mcp,fastapi,embeddings
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: fastapi>=0.115.0
Requires-Dist: uvicorn[standard]>=0.32.0
Requires-Dist: pydantic>=2.9.0
Requires-Dist: pydantic-settings>=2.6.0
Requires-Dist: fastembed>=0.4.0
Requires-Dist: litellm>=1.56.0
Requires-Dist: httpx>=0.28.0
Requires-Dist: python-multipart>=0.0.12
Requires-Dist: python-jose[cryptography]>=3.3.0
Requires-Dist: passlib[bcrypt]>=1.7.0
Requires-Dist: redis>=5.2.0
Requires-Dist: orjson>=3.10.0
Requires-Dist: mcp>=1.2.0
Requires-Dist: structlog>=24.4.0
Requires-Dist: rich>=13.7.1
Requires-Dist: tiktoken>=0.8.0
Requires-Dist: tenacity>=9.0.0
Requires-Dist: aiocache>=0.12.0
Requires-Dist: numpy>=1.26.0
Requires-Dist: scikit-learn>=1.5.0
Requires-Dist: polars>=1.15.0
Requires-Dist: chromadb>=0.5.0
Requires-Dist: lancedb>=0.8.0
Requires-Dist: sentence-transformers>=5.0.0
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Requires-Dist: twine>=4.0.0; extra == "dev"
Provides-Extra: test
Requires-Dist: pytest>=8.3.0; extra == "test"
Requires-Dist: pytest-asyncio>=0.24.0; extra == "test"
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Provides-Extra: docs
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Requires-Dist: mkdocstrings[python]>=0.27.0; extra == "docs"
Dynamic: license-file
# Hanzo Memory Service
## Add memory to any AI application!
A high-performance FastAPI service that provides memory and knowledge management capabilities for AI applications. Built with LanceDB vector database (works on all platforms including browsers via WASM), local embeddings, and LiteLLM for flexible LLM integration.
## Features
- **🧠 Intelligent Memory Management**: Store and retrieve contextual memories with semantic search
- **📚 Knowledge Base System**: Organize facts in hierarchical knowledge bases with parent-child relationships
- **💬 Chat History**: Store and search conversation history with de-duplication
- **🔍 Unified Search API**: Fast semantic search using FastEmbed embeddings
- **🤖 Flexible LLM Support**: Use any LLM via LiteLLM (OpenAI, Anthropic, Ollama, etc.)
- **🔐 Multi-tenancy**: Secure user and project-based data isolation
- **🚀 High Performance**: Local embeddings and efficient vector storage
- **🗄️ Cross-Platform Database**: LanceDB works everywhere - Linux, macOS, Windows, and even browsers
- **🔌 MCP Support**: Model Context Protocol server for AI tool integration
- **📦 Easy Deployment**: Docker support and uvx compatibility
## Architecture
```
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ FastAPI │────▶│ Vector DB │────▶│ Embeddings │
│ Server │ │ (LanceDB/ │ │ (FastEmbed/ │
└─────────────────┘ │ InfinityDB) │ │ LanceDB) │
│ └─────────────────┘ └─────────────────┘
▼ │
┌─────────────────┐ ┌─────────────────┐
│ LiteLLM │ │ Local Models │
│ (LLM Bridge) │ │ (BGE, etc.) │
└─────────────────┘ └─────────────────┘
```
## Quick Start
### Install with uvx
```bash
# Install and run directly with uvx
uvx hanzo-memory
# Or install globally
uvx install hanzo-memory
```
### Install from source
```bash
# Clone the repository
git clone https://github.com/hanzoai/memory
cd memory
# Install with uv
make setup
# Run the server
make dev
```
### Docker
```bash
# Using docker-compose
docker-compose up
# Or build and run manually
docker build -t hanzo-memory .
docker run -p 4000:4000 -v $(pwd)/data:/app/data hanzo-memory
```
## Configuration
Create a `.env` file (see `.env.example`):
```env
# API Authentication
HANZO_API_KEY=your-api-key-here
HANZO_DISABLE_AUTH=false # Set to true for local development
# LLM Configuration (choose one)
# OpenAI
HANZO_LLM_MODEL=gpt-4o-mini
OPENAI_API_KEY=your-openai-key
# Anthropic
HANZO_LLM_MODEL=claude-3-haiku-20240307
ANTHROPIC_API_KEY=your-anthropic-key
# Local Models (Ollama)
HANZO_LLM_MODEL=ollama/llama3.2
HANZO_LLM_API_BASE=http://localhost:11434
# Embedding Model
HANZO_EMBEDDING_MODEL=BAAI/bge-small-en-v1.5
# Database Backend (optional, defaults to lancedb)
HANZO_DB_BACKEND=lancedb
HANZO_LANCEDB_PATH=data/lancedb
```
## Database Backends
Hanzo Memory supports multiple vector database backends:
### LanceDB (Default)
- Modern embedded vector database that works on ALL platforms
- Cross-platform: Linux, macOS, Windows, ARM, and even browsers (via WASM)
- Built-in support for FastEmbed and sentence-transformers
- Efficient columnar storage format (Apache Arrow/Parquet)
- Native vector similarity search
- Can be embedded in Python, JavaScript/TypeScript, Rust applications
### InfinityDB (Alternative, Linux/Windows only)
- High-performance embedded vector database
- Not available on macOS
- Optimized for production workloads
- Built-in vector indexing
To configure the database backend:
```env
# Use LanceDB
HANZO_DB_BACKEND=lancedb
HANZO_LANCEDB_PATH=data/lancedb
# Use InfinityDB
HANZO_DB_BACKEND=infinity
HANZO_INFINITY_DB_PATH=data/infinity_db
```
## API Documentation
For complete API documentation including all endpoints, request/response formats, and examples, see [docs/API.md](docs/API.md).
### Quick API Overview
- **Memory Management**: `/v1/remember`, `/v1/memories/*`
- **Knowledge Bases**: `/v1/kb/*`, `/v1/kb/facts/*`
- **Chat Sessions**: `/v1/chat/sessions/*`, `/v1/chat/messages/*`
- **Search**: Unified semantic search across all data types
- **MCP Server**: Model Context Protocol integration for AI tools
### LLM Features
The service can:
- **Summarize content** for knowledge extraction
- **Generate knowledge update instructions** in JSON format
- **Filter search results** for relevance
- **Strip PII** from stored content
Example summarization request:
```python
llm_service.summarize_for_knowledge(
content="Long document...",
skip_summarization=False, # Set to True to skip
provided_summary="Optional pre-made summary"
)
```
Returns:
```json
{
"summary": "Concise summary of content",
"knowledge_instructions": {
"action": "add_fact",
"facts": [{"content": "Extracted fact", "metadata": {...}}],
"reasoning": "Why these facts are important"
}
}
```
## Development
### Running Tests
```bash
# Run all tests
make test
# Run with coverage
make test-cov
# Run specific test
uvx pytest tests/test_memory_api.py -v
```
### Code Quality
```bash
# Format code
make format
# Run linter
make lint
# Type checking
make type-check
```
### Project Structure
```
memory/
├── src/hanzo_memory/
│ ├── api/ # API authentication
│ ├── db/ # InfinityDB client
│ ├── models/ # Pydantic models
│ ├── services/ # Business logic
│ ├── config.py # Settings
│ └── server.py # FastAPI app
├── tests/ # Pytest tests
├── Makefile # Build automation
└── pyproject.toml # Project config
```
## Deployment
### Production Checklist
1. Set strong `HANZO_API_KEY`
2. Configure appropriate LLM model and API keys
3. Set `HANZO_DISABLE_AUTH=false`
4. Configure data persistence volume
5. Set up monitoring and logging
6. Configure rate limiting if needed
### Scaling Considerations
- InfinityDB embedded runs in-process (no separate DB server)
- FastEmbed generates embeddings locally (no API calls)
- LLM calls can be directed to local models for full offline operation
- Use Redis for caching in high-traffic scenarios
## Contributing
Pull requests are welcome! Please:
1. Write tests for new features
2. Follow existing code style
3. Update documentation as needed
4. Run `make check` before submitting
## License
BSD License - see LICENSE file for details.
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LICENSE
README.md
pyproject.toml
src/hanzo_memory/__init__.py
src/hanzo_memory/cli.py
src/hanzo_memory/config.py
src/hanzo_memory/memory.py
src/hanzo_memory/server.py
src/hanzo_memory.egg-info/PKG-INFO
src/hanzo_memory.egg-info/SOURCES.txt
src/hanzo_memory.egg-info/dependency_links.txt
src/hanzo_memory.egg-info/entry_points.txt
src/hanzo_memory.egg-info/requires.txt
src/hanzo_memory.egg-info/top_level.txt
src/hanzo_memory/api/__init__.py
src/hanzo_memory/api/auth.py
src/hanzo_memory/db/__init__.py
src/hanzo_memory/db/base.py
src/hanzo_memory/db/client.py
src/hanzo_memory/db/factory.py
src/hanzo_memory/db/kuzudb_client.py
src/hanzo_memory/db/lancedb_client.py
src/hanzo_memory/db/local_client.py
src/hanzo_memory/db/markdown_reader.py
src/hanzo_memory/db/mock_infinity.py
src/hanzo_memory/db/backends/__init__.py
src/hanzo_memory/db/backends/backend_registry.py
src/hanzo_memory/mcp/__init__.py
src/hanzo_memory/mcp/__main__.py
src/hanzo_memory/mcp/server.py
src/hanzo_memory/models/__init__.py
src/hanzo_memory/models/base.py
src/hanzo_memory/models/chat.py
src/hanzo_memory/models/knowledge.py
src/hanzo_memory/models/memory.py
src/hanzo_memory/models/project.py
src/hanzo_memory/services/__init__.py
src/hanzo_memory/services/embeddings.py
src/hanzo_memory/services/embeddings_lancedb.py
src/hanzo_memory/services/llm.py
src/hanzo_memory/services/memory.py
tests/test_additional_coverage.py
tests/test_auth.py
tests/test_chat_api.py
tests/test_cli.py
tests/test_embeddings.py
tests/test_embeddings_edge_cases.py
tests/test_infinity_client.py
tests/test_knowledge_api.py
tests/test_llm_service.py
tests/test_mcp_server.py
tests/test_memory_api.py
pkg/hanzo-memory/src/hanzo_memory.egg-info/dependency_links.txt
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pkg/hanzo-memory/src/hanzo_memory.egg-info/entry_points.txt
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[console_scripts]
hanzo-memory = hanzo_memory.cli:main
hanzo-memory-mcp = hanzo_memory.mcp.server:main
hanzo-memory-server = hanzo_memory.server:run
pkg/hanzo-memory/src/hanzo_memory.egg-info/requires.txt
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fastapi>=0.115.0
uvicorn[standard]>=0.32.0
pydantic>=2.9.0
pydantic-settings>=2.6.0
fastembed>=0.4.0
litellm>=1.56.0
httpx>=0.28.0
python-multipart>=0.0.12
python-jose[cryptography]>=3.3.0
passlib[bcrypt]>=1.7.0
redis>=5.2.0
orjson>=3.10.0
mcp>=1.2.0
structlog>=24.4.0
rich>=13.7.1
tiktoken>=0.8.0
tenacity>=9.0.0
aiocache>=0.12.0
numpy>=1.26.0
scikit-learn>=1.5.0
polars>=1.15.0
chromadb>=0.5.0
lancedb>=0.8.0
sentence-transformers>=5.0.0
[dev]
pytest>=8.3.0
pytest-asyncio>=0.24.0
pytest-cov>=6.0.0
ruff>=0.8.0
mypy>=1.13.0
black>=24.10.0
pre-commit>=4.0.0
ipython>=8.29.0
ipdb>=0.13.0
twine>=4.0.0
[docs]
mkdocs>=1.6.0
mkdocs-material>=9.5.0
mkdocstrings[python]>=0.27.0
[test]
pytest>=8.3.0
pytest-asyncio>=0.24.0
pytest-cov>=6.0.0
pytest-mock>=3.14.0
faker>=30.0.0
factory-boy>=3.3.0
respx>=0.21.0
pkg/hanzo-memory/src/hanzo_memory.egg-info/top_level.txt
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hanzo_memory
pkg/hanzo-tools-agent/hanzo_tools/agent/swarm_tool_v1_deprecated.py
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reviewer_query = params.get("reviewer_query")
common_context = params.get("common_context", "")
max_concurrent = params.get("max_concurrent", 10)
semaphore = asyncio.Semaphore(max_concurrent)
if not agents:
await tool_ctx.error("No agents provided")