Practical overview
When it fits
Use LLMCompiler when an application needs an LLM to coordinate multiple user-provided functions and some calls can run concurrently while others depend on prior results.
Who it is for
- Developers building LLM applications that use multiple tools or functions.
- Teams prototyping task pipelines that combine parallel calls with dependent follow-up work.
- Researchers reproducing or adapting the repository's documented benchmark evaluations.
Documented uses
- Define user-provided functions and descriptions for an LLM to call.
- Orchestrate independent function calls concurrently while preserving task dependencies.
- Prepare custom benchmark inputs using tool definitions and in-context example prompts.
- Run documented evaluations and save predictions and latency results.
Requirements & limits
The project is a public GitHub repository licensed under MIT. The documented setup uses Python 3.10, Conda and pip.
- Tool definitions and descriptions are supplied by the user; optional in-context examples can also guide orchestration.
- The README documents OpenAI, Azure and Friendli endpoints, as well as custom models served through vLLM.
- Default prompts are tailored to non-chat LLaMA-2 70B and may require adjustment for other models.
- The documented benchmark flow stores questions, true labels, predictions and per-example latency in JSON.
- Streaming is documented as a way to pass generated tasks to the executor before the planner has finished producing all tasks.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Install LLMCompiler and its Python dependencies
Prepare the documented repository environment for working with LLMCompiler.
Before you start
- Conda
- Python 3.10
Steps
Create and activate a Conda environment named llmcompiler with Python 3.10.
Clone the LLMCompiler repository and change into its directory.
Install the listed dependencies with pip using requirements.txt.
Expected outcome
The repository dependencies are installed in the named Conda environment.
Guide limitations
- These steps cover setup only; they do not configure a model endpoint or run a benchmark.
Configure functions and prompts for a custom use case
Prepare the function definitions and example prompts that LLMCompiler requires for a custom benchmark or use case.
Before you start
- A checked-out LLMCompiler repository
- Functions for the intended use case and descriptions of those functions
- Example prompts
Steps
Review the example configurations in configs/hotpotqa, configs/movie and configs/parallelqa.
Define the tools, instructions and arguments in tools.py.
Define in-context example prompts in gpt_prompts.py.
Expected outcome
The custom use case has the functions, descriptions and example prompts identified by the README.
Guide limitations
- The README describes preparing custom benchmark inputs here; it does not specify an end-to-end execution procedure for a custom use case.
Compare options for Agent orchestration
These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.
| Project | Documented purpose | Platform | Access and requirements |
|---|---|---|---|
| Llmcompiler | Define user-provided functions and descriptions for an LLM to call. | Linux, macOS, Windows | The project is a public GitHub repository licensed under MIT. The documented setup uses Python 3.10, Conda and pip. |
| Agenite | Define typed tools with JSON Schema validation and error handling. | CLI | |
| Veldra | Create an agent with a policy, tools, skills, knowledge base and reasoning method. | Self-hosted | The documented deployment is self-hosted and uses a sign-in gate after a first-run administrator account is created. |
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 14 October 2026
- Original website title
- SqueezeAILab/LLMCompiler · Official homepage title · retained in its source language
Related options
ReAct — The README documents a --react option for using ReAct instead of LLMCompiler as a baseline evaluation.