LLMCompiler

Source#

• Repository: SqueezeAILab/LLMCompiler
• Last source update: 2026-02-26
• Last verified: 2026-03-01

Tags#

Integration notes#

Framework-level abstraction; derive bounded worker contracts from concrete tasks and APIs in docs/examples.

worker.md example#

Starter worker.md contract mapped from this registry entry. Copy this file and adapt schemas, constraints, and statuses for your task.

---
id: llmcompiler-repo-derived-worker
name: LLMCompiler Repo-Derived Worker
version: 1.0.0
source_registry_url: https://worker.md/registry/llmcompiler/
source_repository: https://github.com/SqueezeAILab/LLMCompiler
repository_default_branch: main
repository_language: Python
repository_license: MIT
repository_updated_at: 2026-02-26
worker_mode: agent-orchestration-worker
derivation_method: github_repository_metadata_plus_raw_readme
derivation_confidence: 0.95
derived_on: 2026-03-01
tags:
  - efficient-inference
  - function-calling
  - large-language-models
  - llama
  - llama2
  - llm
---

# LLMCompiler Repo-Derived Worker

## Repo-derived summary
- Registry summary: [ICML 2024] LLMCompiler: An LLM Compiler for Parallel Function Calling
- Repository description: [ICML 2024] LLMCompiler: An LLM Compiler for Parallel Function Calling
- Stars (snapshot): 1,824
- Primary language: Python
- Worker mode classification: agent-orchestration-worker

## Extracted from
- https://github.com/SqueezeAILab/LLMCompiler
- https://github.com/SqueezeAILab/LLMCompiler/blob/main/README.md
- https://github.com/langchain-ai/langgraph/blob/main/examples/llm-compiler/LLMCompiler.ipynb
- https://docs.vllm.ai/en/latest/getting_started/quickstart.html#openai-compatible-server

## Evidence notes (from repository text)
- README summary paragraph: # LLMCompiler: An LLM Compiler for Parallel Function Calling https://arxiv.org/abs/2312.04511]
- that can be executed in parallel, thereby efficiently orchestrating multi-function calling.
- With LLMCompiler, the user specifies the tools
- along with optional in-context examples, and **LLMCompiler automatically computes an optimized orchestration for
- * 🦜 [2/13] LLMCompiler is available within the https://github.com/langchain-ai/langgraph/blob/main/examples/llm-compiler/LLMCompiler.ipynb framework of https://github.com/langchain-ai.
- * 🦙 [12/29] LLMCompiler is available on https://llamahub.ai/l/llama_packs-agents-llm_compiler?from=llama_packs

## Installation hints found in README
- `pip install -r requirements.txt`
- `pip install friendli-client`

## worker.md contract (derived starter)
Purpose: Execute one orchestrated agent task as a bounded worker step.

### Input schema
```json
{
  "type": "object",
  "additionalProperties": false,
  "required": [
    "run_id",
    "task",
    "context"
  ],
  "properties": {
    "run_id": {
      "type": "string"
    },
    "task": {
      "type": "string"
    },
    "context": {
      "type": "object"
    }
  }
}
```

### Output schema
```json
{
  "type": "object",
  "additionalProperties": false,
  "required": [
    "run_id",
    "status",
    "result"
  ],
  "properties": {
    "run_id": {
      "type": "string"
    },
    "status": {
      "type": "string",
      "enum": [
        "ok",
        "retryable_error",
        "invalid_request",
        "invalid_output"
      ]
    },
    "result": {
      "type": "object"
    }
  }
}
```

### Constraints
- timeout_seconds: 30
- max_attempts: 2
- idempotency_key: run_id
- status_enum: [ok, retryable_error, invalid_request, invalid_output]
- notes: adapt to concrete APIs/classes documented in this repository before production use

## How this should be used
1. Treat this file as a repo-derived starter profile, not a claim of an official repository API contract.
2. Replace schemas with exact interfaces from code/docs you adopt.
3. Keep execution bounded and auditable using worker protocol constraints.

How to use#

• Save this as a worker spec file (for example: llmcompiler-my-task.worker.md).
• Replace the input/output schemas and purpose with your real bounded task.
• Enforce schema validation + timeout + retry policy in your runtime before production use.

Citation#

Reference URL: https://worker.md/registry/llmcompiler/

Source URL: https://github.com/SqueezeAILab/LLMCompiler