A reference implementation for the tulip agent, an LLM-backed agent with access to a large number of tools via a tool library. This approach reduces costs, enables the use of tool sets that exceed API limits or context windows, and increases flexibility with regard to the tool set used.

🔬 Function analysis
Generates OpenAI API compatible tool descriptions for Python functions via introspection

🌷 Tool library
Combines a vector store for semantic search among tools and tool execution

🤖 Agents
Specifying instructions for an agent completely overrides the base system prompts to avoid contradictions. You can append custom instructions to the default prompts in tulip_agent.agents.prompts.

• Baseline, without tool library
  • BaseAgent: LLM agent without tool access
  • NaiveToolAgent: Includes tool descriptions for all tools available
  • CotToolAgent: Extends the NaiveToolAgent with a planning step that decomposes the user input into subtasks
• Tulip variations with access to a tool library
  • MinimalTulipAgent: Minimal implementation; searches for tools based on the user input directly
  • NaiveTulipAgent: Naive implementation; searches for tools with a separate tool call
  • CotTulipAgent: COT implementation; derives a plan for the necessary steps and searches for suitable tools
  • InformedCotTulipAgent: Same as CotTulipAgent, but with a brief description of the tool library's contents
  • PrimedCotTulipAgent: Same as CotTulipAgent, but primed with tool names based on an initial search with the user request
  • OneShotCotTulipAgent: Same as CotTulipAgent, but the system prompt included a brief example
  • AutoTulipAgent: Fully autonomous variant; can use the search tool at any time and modify its tool library with CRUD operations
  • DfsTulipAgent: DFS inspired variant that leverages a DAG for keeping track of tasks and suitable tools, can create new tools

📊 Evaluation

• math_eval: Math evaluation
• robo_eval: Robotics evaluation using tools created for AttentiveSupport

📝 Examples
See ./examples

• Make sure to set the environment variables required by the API of your choice. Currently supported:
  • OpenAI: OPENAI_API_KEY, see the official instructions
  • Azure: AZURE_OPENAI_API_KEY, AZURE_API_VERSION, and AZURE_OPENAI_ENDPOINT
  • OpenAI compatible endpoints: OAI_COMPATIBLE_BASE_URL and OAI_COMPATIBLE_API_KEY for OpenAI compatible endpoints, such as Ollama
• Install with uv venv --allow-existing && uv sync or pip install -e .
• Check out the examples, the robot evaluation in src/eval/robo_eval, and examples/local_examples.py for a local setup

• Python v3.10.11 recommended, higher versions may lead to issues with chroma during installation
• Pre-commit hooks - install with (uv run) pre-commit install
• Linting: ruff
• Formatting: black
• Import sorting: isort
• Tests: Run with (uv run) python -m unittest discover tests/

See these troubleshooting instructions

1. On Linux install pysqlite3-binary: uv add pysqlite3-binary
2. Add the following to lib/python3.10/site-packages/chromadb/__init__.py in your venv

__import__('pysqlite3')
import sys
sys.modules['sqlite3'] = sys.modules.pop('pysqlite3')

Make sure to install the package itself, e.g., with uv sync or pip install -e .
Then run the example with uv run examples/calculator_example.py