Independent package for agentic AI capabilities: tool selection, planning, workflows, and agent coordination
sage-agentic provides a comprehensive framework for building agentic AI systems with:
- Tool Selection: Multiple strategies (keyword, embedding, hybrid, DFS-DT, Gorilla)
- Planning Algorithms: ReAct, Tree of Thoughts (ToT), hierarchical planning
- Workflow Management: Workflow orchestration and optimization
- Agent Coordination: Multi-agent collaboration and registry
- SIAS: Sample-Importance-Aware Selection for tool/trajectory curation
- Reasoning: Advanced reasoning capabilities and timing decisions
# Basic installation
pip install isage-agentic
# With LLM support
pip install isage-agentic[llm]
# Development installation
pip install isage-agentic[dev]from sage_agentic.agents.action.tool_selection import HybridToolSelector
# Create selector
selector = HybridToolSelector(embedder=your_embedder)
# Select tools
tools = selector.select(
query="search for research papers",
available_tools=all_tools,
k=3
)from sage_agentic.agents.planning import ReActPlanner
# Create planner
planner = ReActPlanner(llm=your_llm_client)
# Generate plan
plan = planner.plan(
task="Analyze this document and summarize key findings",
context={"document": doc_content}
)from sage_agentic.workflow import WorkflowEngine
# Create workflow
workflow = WorkflowEngine()
# Register and execute workflows
workflow.register("data_pipeline", pipeline_config)
result = workflow.execute("data_pipeline", inputs=data)Planning algorithms and strategies:
- ToT (Tree of Thoughts): Multi-path reasoning with backtracking
- ReAct: Reasoning + Acting interleaved execution
- Hierarchical Planner: Hierarchical task decomposition
- Dependency Graph: Task dependency management
- Timing Decider: Execution timing optimization
Tool selection strategies:
- Keyword Selector: Rule-based keyword matching
- Embedding Selector: Semantic similarity-based selection
- Hybrid Selector: Combined keyword + embedding approach
- DFS-DT Selector: Decision tree-based selection
- Gorilla Adapter: Gorilla-style tool retrieval
Sample-Importance-Aware Selection for:
- Tool selection optimization
- Trajectory curation
- Continual learning with core-set selection
Agent evaluation capabilities:
- Metrics tracking
- Determinism testing
- Telemetry and monitoring
Unified interfaces and registration system for:
- Planners
- Tool selectors
- Workflows
- Agents
sage_agentic/
├── agents/ # Agent implementations
│ ├── action/ # Action and tool selection
│ ├── planning/ # Planning algorithms
│ └── intent/ # Intent detection
├── workflow/ # Workflow orchestration
├── sias/ # Sample-Importance-Aware Selection
├── reasoning/ # Reasoning capabilities
├── eval/ # Evaluation tools
├── interface/ # Protocol definitions
├── interfaces/ # Interface implementations
└── registry/ # Component registry
- Multi-Agent Systems: Build coordinated multi-agent workflows
- Tool-Augmented LLMs: Select and use external tools intelligently
- Hierarchical Planning: Decompose complex tasks into subtasks
- Adaptive Systems: Use SIAS for intelligent sample selection
- Research: Experiment with different planning and selection strategies
This package is part of the SAGE ecosystem but can be used independently:
# Standalone usage
from sage_agentic import ReActPlanner, HybridToolSelector
# With SAGE (if installed)
from sage.libs.agentic import ReActPlanner # Compatibility layer- Repository: https://github.com/intellistream/sage-agentic
- SAGE Documentation: https://intellistream.github.io/SAGE-Pub/
- Issues: https://github.com/intellistream/sage-agentic/issues
Contributions are welcome! Please feel free to submit a Pull Request.
MIT License - see LICENSE file for details.
Originally part of the SAGE framework, now maintained as an independent package for broader community use.
- Team: IntelliStream Team
- Email: [email protected]
- GitHub: https://github.com/intellistream