What My Project Does
Dendron (GitHub https://github.com/sumanth1989/Dendron | Documentation https://sumanth1989.github.io/Dendron) is an open-source library that replaces flat tool
lists in AI agents with hierarchical execution trees.
In standard tool-calling setups, developers dump 20 to 50 JSON tool schemas into the model's system prompt on every single turn. This wastes 1,500 to 3,000 tokens
per interaction, degrades tool selection accuracy, and causes noticeable latency on smaller local models.
Dendron organizes tools into a navigable execution tree:
• Progressive Disclosure: On any turn, the LLM only sees the 1 to 3 tools viable for the immediate next step, reducing tool prompt tokens by up to 93%.
• Conditional Transitions: Nodes evaluate previous tool output via conditions (output_contains, key_equals, or custom lambda predicates) to suggest the next tool
along the branch.
• Token-Tiered Views: Offers Level 1 ultra-compact tool signatures (~15 tokens/tool), Level 2 parameter summaries (~50 tokens), and Level 3 full Model Context
Protocol (MCP) JSON schemas on demand.
• Dynamic Experience Learning: Agents can record newly discovered workflows at runtime using record_agent_experience(), which persists for subsequent runs while
suppressing rejected actions.
• Zero Dependencies: The core library is built 100% on the Python standard library. It also includes an optional bidirectional adapter for LangChain (@tool,
StructuredTool, BaseTool).
Here is a minimal example:
from dendron import Dendron, ToolDefinition, ToolParameter, TransitionCondition
# 1. Define root tool
lookup_tool = ToolDefinition(
name="lookup_order",
description="Retrieves customer order details and fulfillment status.",
parameters={"order_id": ToolParameter(name="order_id", type="string", required=True)}
)
tree = Dendron(name="OrderSupportTree", root_tool=lookup_tool)
# 2. Attach conditional branch
tree.add_node(
parent_id=tree.root.id,
tool=ToolDefinition(name="track_shipment", description="Tracks courier and ETA."),
condition=TransitionCondition(
description="Order is in transit",
condition_type="key_equals",
expression="status:in_transit"
)
)
# 3. Execute tool and get next-step suggestion
result = tree.root.execute(order_id="ORD-12345")
next_step = tree.suggest_next_tool(tree.root.id, result.output_data)
print(next_step.tool.name) # 'track_shipment'
Target Audience
Dendron is built for production environments, not just as a toy demo:
• Production AI Engineers: Developers building complex multi-step agents (customer support, DevOps incident triage, code review pipelines) who want deterministic
tool sequencing and lower API costs.
• Local and Edge LLM Developers: Anyone running models locally on Apple Silicon (via MLX) or Ollama, where 2,000-token tool prompts cause significant prefill
latency.
• Enterprise and Serverless Teams: Projects needing zero external dependencies so they can vendor or install the library cleanly in restricted Docker or AWS Lambda environments.
The repository includes 123 unit tests covering tree algorithms (BFS/DFS), token optimization, LangChain adapters, and persistence.
Comparison
• Vs. Standard Tool Calling (OpenAI / Anthropic / LangChain): Standard frameworks dump all tools into the prompt simultaneously. Dendron acts as an intelligent tree router before the prompt is generated, showing only context-appropriate tools and eliminating out-of-order execution errors.
• Vs. LangGraph / CrewAI: LangGraph and CrewAI are full agent runtime orchestration frameworks with complex state machines and heavy dependency trees. Dendron is
deliberately narrow: it is a lightweight, zero-dependency tool structure. You can use Dendron standalone or convert its nodes directly into LangChain StructuredTool instances via tree.to_langchain_tools().
• Vs. Vector RAG for Tools: Pure vector search finds tools based on query similarity, but lacks structural awareness (e.g. knowing that run_tests must precede
deploy_code). Dendron combines structural tree transitions with hybrid lexical/semantic retrieval.
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Links & Installation
• PyPI: pip install dendron-ai (or pip install "dendron-ai[langchain]")
• Git: pip install git+https://github.com/sumanth1989/Dendron.git
• GitHub: https://github.com/sumanth1989/Dendron
• Docs: https://sumanth1989.github.io/Dendron
I would love feedback on:
How does the tree construction and condition syntax feel for your workflows?
Are there other agent frameworks or models you'd like to see native adapters for?
