8 Best Gorilla Alternatives in 2026 (Open Source)
Gorilla — Gorilla: Training and Evaluating LLMs for Function Calls (Tool Calls). vs ChatGPT function calling: open-source model + comprehensive BFCL leaderboard + GoEx safe execution engine, all from UC Berkeley research
These 8 open-source tools do the same job. They are ordered by how closely they match Gorilla, with live GitHub data so you can see which projects are actively maintained.
| Tool | GitHub stars | Stars / 30d | Last commit |
|---|---|---|---|
| Gorilla(original) | 13.0k | +42 | 2026-03-23 |
| llama-cpp-agent | 659 | +6 | 2026-03-09 |
| Agentflow | 321 | +0 | 2023-08-11 |
| Open Assistant API | 367 | +1 | 2024-12-14 |
| Langroid | 4.1k | +27 | 2026-09-23 |
| AgentRun | 380 | +2 | 2024-11-10 |
| Cheshire Cat AI | 3.1k | +14 | 2026-07-29 |
| Swarm | 22.0k | +126 | 2026-04-15 |
| BabyAGI | 22.4k | +24 | 2026-01-31 |
1. llama-cpp-agent
The llama-cpp-agent framework is a tool designed for easy interaction with Large Language Models (LLMs). Allowing users to chat with LLM models, execute structured function calls and get structured ou
What sets it apart: Enabled function calling and structured output from any local LLM through grammar-based guided sampling, making capabilities previously exclusive to fine-tuned models available to all llama.cpp-compatible models — now deprecated
Best for: Getting structured output from local LLMs without fine-tuning; Building function-calling agents with open-source models locally
2. Agentflow
Complex LLM Workflows from Simple JSON.
What sets it apart: vs AutoGPT / LangChain agents: deterministic step-by-step workflow execution from JSON definitions — balanced between chat flexibility and autonomous agent unpredictability, with custom function support
Best for: Developers wanting structured, repeatable LLM workflows vs. freeform chat; Multi-step content generation pipelines (e.g., market research → analysis → report); Teams needing predictable LLM execution with human-readable workflow definitions
3. Open Assistant API
The Open Assistant API is a ready-to-use, open-source, self-hosted agent/gpts orchestration creation framework, supporting customized extensions for LLM, RAG, function call, and tools capabilities. It
What sets it apart: Open-source OpenAI Assistant API compatible service supporting multiple LLMs via One API, with RAG, web search, and local deployment
Best for: self-hosted-openai-assistant-alternative; multi-llm-assistant-apps; enterprise-local-deployment
4. Langroid
Harness LLMs with Multi-Agent Programming
What sets it apart: vs LangChain/CrewAI: Actor-model-inspired multi-agent framework from CMU/UW-Madison researchers, praised for intuitive Agent-Task abstractions, lightweight design, and production use at companies like Nullify - no dependency on LangChain
Best for: Building multi-agent systems with clean Agent-Task abstractions; Teams wanting an intuitive, lightweight alternative to LangChain; Research applications with complex agent collaboration patterns
5. AgentRun
The easiest, and fastest way to run AI-generated Python code safely
What sets it apart: Single-line safe Python code execution from LLMs in Docker containers with automatic dependency management, safety checks, and resource limiting
Best for: safe-llm-code-execution; sandboxed-python-runtime; giving-code-execution-to-llm-agents
6. Cheshire Cat AI
AI agent microservice
What sets it apart: vs LangChain/LlamaIndex: opinionated, ready-to-deploy conversational AI microservice with built-in admin panel, plugin system, and Qdrant RAG — not a framework but a complete product
Best for: Building custom AI assistants as embeddable microservices; Teams needing plugin-extensible conversational AI with admin panel
7. Swarm
Educational framework exploring ergonomic, lightweight multi-agent orchestration. Managed by OpenAI Solution team.
Best for: Developers learning multi-agent orchestration patterns and concepts; Rapid prototyping of multi-agent workflows before production implementation; Educational settings exploring agent handoff and coordination
8. BabyAGI
What sets it apart: vs static agent frameworks (LangChain/CrewAI): focuses on self-building capability where agents autonomously generate and improve their own functions — 'the simplest thing that can build itself'
Best for: Exploring autonomous agent architecture concepts; Educational experimentation with self-building AI systems