This tutorial will guide you through using Arcee AI’s language models on Clarifai through the Clarifai SDK. #### Prerequisites * **Clarifai Account** * **Clarifai API Key**\ If you do not have one, create it in your Clarifai console. * **Clarifai Python SDK** #### Quickstart Run a completion using an Arcee model. ```python import os from openai import OpenAI client = OpenAI( base_url="https://api.clarifai.com/v2/ext/openai/v1", api_key="
import os
from typing_extensions import Annotated, TypedDict
from langgraph.graph import StateGraph, START, END
from langgraph.graph.message import add_messages
from langchain_core.messages import AnyMessage, SystemMessage, HumanMessage
from langchain_openai import ChatOpenAI
class State(TypedDict):
messages: Annotated[list[AnyMessage], add_messages]
# Configure Arcee AI Model
ARCEE_BASE = os.getenv("OPENAI_API_BASE", "http://127.0.0.1:8080/v1")
ARCEE_KEY = os.getenv("OPENAI_API_KEY", "your-arcee-api-key")
ARCEE_MODEL = os.getenv("OPENAI_MODEL_NAME", "trinity-mini")
# Initialize Arcee AI model with OpenAI-compatible configuration
arcee_llm = ChatOpenAI(
model=ARCEE_MODEL,
api_key=ARCEE_KEY,
base_url=ARCEE_BASE,
)
# Define a simple graph node that uses the Arcee AI model
def summarize(state: State) -> State:
# Add a system instruction once, if not present
msgs = state["messages"]
if not any(getattr(m, "role", "") == "system" for m in msgs):
msgs = [SystemMessage(content="You are a concise technical writer.")] + msgs
# Ask Arcee to respond
ai = arcee_llm.invoke(msgs)
return {"messages": [ai]}
# Build the graph that LangGraph will execute
builder = StateGraph(State)
builder.add_node("summarize", summarize)
builder.add_edge(START, "summarize")
builder.add_edge("summarize", END)
graph = builder.compile()
if __name__ == "__main__":
text = """Arcee AI is a foundation model provider with a focus on building the highest performing models per parameter.
They offer a range of models from on-device and edge optimized models to large language models. Their suite of models
provides customers with the flexibility to choose the right model for the right task. All models are released Apache 2.0
enabling the community to use safe, built-in-the-US models in their own environment or via the Arcee AI API platform."""
inputs: State = {"messages": [HumanMessage(content=f"Summarize the following in three bullets:\n\n{text}")]}
# Execute the LangGraph agent
result = graph.invoke(inputs)
# Print the results
print("\n=== RESULT ===\n")
print(result["messages"][-1].content)
import os
from typing_extensions import Annotated, TypedDict
from langgraph.graph import StateGraph, START, END
from langgraph.graph.message import add_messages
from langchain_core.messages import AnyMessage, SystemMessage, HumanMessage
from langchain_openai import ChatOpenAI
class State(TypedDict):
messages: Annotated[list[AnyMessage], add_messages]
# Configure Arcee AI Model
ARCEE_BASE = os.getenv("ARCEE_API_BASE", "http://127.0.0.1:8080/v1")
ARCEE_KEY = os.getenv("ARCEE_API_KEY", "your-arcee-api-key")
ARCEE_MODEL = os.getenv("ARCEE_MODEL_NAME", "trinity-mini")
# Initialize Arcee AI model with OpenAI-compatible configuration
arcee_llm = ChatOpenAI(
model=ARCEE_MODEL,
api_key=ARCEE_KEY,
base_url=ARCEE_BASE,
)
# Define a simple graph node that uses the Arcee AI model
def summarize(state: State) -> State:
# Add a system instruction once, if not present
msgs = state["messages"]
if not any(getattr(m, "role", "") == "system" for m in msgs):
msgs = [SystemMessage(content="You are a concise technical writer.")] + msgs
# Ask Arcee to respond
ai = arcee_llm.invoke(msgs)
return {"messages": [ai]}
# Build the graph that LangGraph will execute
builder = StateGraph(State)
builder.add_node("summarize", summarize)
builder.add_edge(START, "summarize")
builder.add_edge("summarize", END)
graph = builder.compile()
if __name__ == "__main__":
text = """Arcee AI is a foundation model provider with a focus on building the highest performing models per parameter.
They offer a range of models from on-device and edge optimized models to large language models. Their suite of models
provides customers with the flexibility to choose the right model for the right task. All models are released Apache 2.0
enabling the community to use safe, built-in-the-US models in their own environment or via the Arcee AI API platform."""
inputs: State = {"messages": [HumanMessage(content=f"Summarize the following in three bullets:\n\n{text}")]}
# Execute the LangGraph agent
result = graph.invoke(inputs)
# Print the results
print("\n=== RESULT ===\n")
print(result["messages"][-1].content)
- temperature: 0.5
- top\_k: 50
- top\_p: 0.95
- repeat\_penalty: 1.1
| Hardware | Max Model Len | Quantization | Max Concurrent Requests | TPS per Request* |
|---|---|---|---|---|
| H100 x 1 | 65536 (Max) | bf16 | 16 | 136 |
| H100 x 1 | 4096 | bf16 | 250 | 74.5 |
| L40S x 1 | 8192 | bf16 | 55 | 59 |
| L40S x 1 | 4096 | bf16 | 109 | 64 |
| A10 x 1 | 8192 | bf16 | 12 | 65 |
| A10 x 1 | 4096 | bf16 | 25 | 75 |
| Intel CPU1 | 1024 | Q4_0 | 4 | 29 |
| Graviton42 | 1024 | Q4_0 | 4 | 60 |
- temperature: 0.15
- top\_p: 0.75
- top\_k: 50
- min\_p: 0.06
- temperature: 0.15
- top\_p: 0.75
- top\_k: 50
- min\_p: 0.06
- temperature: 0.8
- top\_p: 0.8
| Benchmark | Trinity-Large-Thinking | Opus-4.6 | GLM-5 | MiniMax-M2.7 | Kimi-K2.5 |
|---|---|---|---|---|---|
| IFBench | 52.3 | 53.1 | 72.3 | 75.7 | 70.2 |
| GPQA-Diamond | 76.3 | 89.2 | 81.6 | 86.2 | 86.9 |
| Tau2-Airline | 88.0 | 82.0 | 80.5 | 80.0 | 80.0 |
| Tau2-Telecom | 94.7 | 92.1 | 98.2 | 84.8 | 95.9 |
| PinchBench | 91.9 | 93.3 | 86.4 | 89.8 | 84.8 |
| AIME25 | 96.3 | 99.8 | 93.3 | 80.0 | 96.3 |
| BCFLv4 | 70.1 | 77.0 | 70.8 | 70.6 | 68.3 |
| MMLU-Pro | 83.4 | 89.1 | 85.8 | 80.8 | 87.1 |
| SWE-bench Verified* | 63.2 | 75.6 | 72.8 | 75.4 | 70.8 |
- temperature: 0.3
Expand for Example Code
```python import json import yfinance as yf from openai import OpenAI import httpx # Initialize client endpoint = "https://api.arcee.ai/api/v1" model = "trinity-nano-6b" api_key = "| /pages/zQZAepohq22jRDjIlPMK | Chat with our models directly. |
| /pages/C4UFW40P6xGRHUzSTEDw | Add, remove, or manage members in your workspace. |
| /pages/x8a5JP1x5cvG46AT2k7R | Create and manage API keys for model access. |
| /pages/8dBjPJwqbeDMYtGvs1Xq | Set up and organize separate workspaces. |
| /pages/h2TocElpiJixHKCKGPas | Monitor request logs, usage metrics, and costs. |
Navigate to members under the Workspaces section of the panel on the left
View members and invites, and search for or add members.
High Performance,
More Versatile
Arcee Conductor
Arcee Conductor Registration
Model Response Example
**Performance Enhancement & Transparency:** * By selecting the most appropriate model for each task, Arcee Conductor ensures that you receive high-quality answers efficiently. * It avoids the latency associated with using overly powerful models for simple queries and the potentially lower quality of underpowered models for complex tasks. * In some instances, Arcee Conductor may provide a rationale behind its model selection, explaining why a particular model was chosen based on the prompt's characteristics. This feature enhances user understanding of the platform's intelligent routing process # Auto Mode The `auto` model mode utilizes Arcee AI's intelligent model router to route prompts to the most optimal and efficient language model. ### Router The power behind Arcee Conductor comes in the form of an ultra-lightweight model router which classifies and routes prompts to the most cost-effective model which can accurately complete the request. The router is 150M parameter, custom architecture model which classifies based on four primary categories: task type, domain, complexity, and language. The categories for each class consist of: * **Task Type**: Analytical, Classification, Code Generation, Extraction, Math, QA, Rewrite, Summarization, Text Generation, Other * **Domain**: Adult, Arts and Entertainment, Autos and Vehicles, Beauty and Fitness, Books and Literature, Business and Industrial, Computers and Electronics, Finance, Food and Drink, Games, Health, Hobbies and Leisure, Home and Garden, Internet and Telecom, Jobs and Eduction, Law and Government, News, Online Communities, People and Society, Pets and Animals, Real Estate, Science, Sensitive Subjects, Shopping, Sports, Travel and Transportation * **Complexity**: 1-10 * **Language**: Arabic, Bengali, Cantonese, Filipino, French, German, Hindi, Italian, Korean, Mandarin, Marathi, Portuguese, Russian, Spanish, Tamil, Telugu Due to the exceptionally small parameter count and custom architecture, the router executes in roughly 150ms resulting in negligible impact to user experience, when considering time to first token. ### Models Based on the classifications from the model router, the request is routed to one of the language models behind Arcee Conductor. The models which can currently be routed to include:
| Model | Description | |
|---|---|---|
| Blitz | A 24B parameter SLM from Arcee AI, distilled from Deepseek V-3 Blitz offers blazing fast response times and exceptionally low costs with strong general knowledge. Ideal for simple and creative tasks. | |
| Virtuoso Medium | A 32B parameter SLM from Arcee AI, which was distilled from Deepseek V-3 giving it an impressive knowledge distribution. | |
| Virtuoso Large | Arcee AI's premier 72B parameter SLM which competes with the leading LLMs on complex and analytical tasks. | |
| GPT-4.1 | A closed-source LLM from Open AI with impressive analytical and complex problem solving capabilities. | |
| Claude Sonnet 3.7 | A closed-source LLM from Anthropic with strong performance on coding and complex tasks. |
| Model | Description | |
|---|---|---|
| Arcee Maestro | Arcee AI's 32B parameter reasoning SLM which offers advanced reasoning capabilities without sacrificing performance. Maestro excels at simple to medium complexity reasoning tasks and offers substantially cheaper cost compared to other options. | |
| Deepseek R1 | An open-source reasoning model from Deepseek which excels at math, coding, and logic tasks. | |
| o3 mini-high | A closed source reasoning model from Open AI optimized for STEM applications, exceling in science, math, and coding benchmarks. | |
| Claude Sonnet 3.7 Extended Thinking | A closed-source LLM from Anthropic with extended reasoning capabilities which performs well for complex problem solving and coding tasks. |
| Model | Description | |
|---|---|---|
| Arcee Caller Large | Arcee AI's 32B parameter function calling SLM optimized for managing complex tool-based interactions and API function calls. | |
| GPT-4.1 | A closed-source LLM from Open AI with function calling ability and impressive analytical and complex problem solving capabilities. | |
| Claude Sonnet 3.7 | A closed-source LLM from Anthropic with function calling ability and strong performance on coding and complex tasks. |
Conductor Comparison
Compare
Comparison Usage
| Model | API Name | Description | |
|---|---|---|---|
| Virtuoso Small | virtuoso-small | A 14B parameter SLM from Arcee AI, distilled from Deepseek V-3. Virtuoso Small is extremely performant and excels at simple tasks such as text generation and summarization. | |
| Blitz | blitz | A 24B parameter SLM from Arcee AI, distilled from Deepseek V-3 Blitz offers blazing fast response times and exceptionally low costs with strong general knowledge. Ideal for simple and creative tasks. | |
| Virtuoso Medium | virtuoso-medium | A 32B parameter SLM from Arcee AI, which was distilled from Deepseek V-3 giving it an impressive knowledge distribution. | |
| Virtuoso Large | virtuoso-large | Arcee AI's premier 72B parameter SLM which competes with the leading LLMs on complex and analytical tasks. | |
| Coder Large | coder | A 32B parameter SLM from Arcee AI fine-tuned to excel at Coding tasks. | |
| Caller Large | caller-large | A 32B parameter SLM from Arcee AI fine-tuned to excel at function calling. | |
| GPT-4.1 | gpt-4.1 | A closed-source LLM from Open AI with impressive analytical and complex problem solving capabilities. | |
| Claude Sonnet 3.7 | claude-3-7-sonnet-20250219 | A closed-source LLM from Anthropic with strong performance on coding and complex tasks. |
Conductor Usage
Select API Key
Create API Key
Blitz - General Purpose
### **Arcee Blitz** * **Description:** Arcee-Blitz (24B) is a new Mistral-based 24B model distilled from DeepSeek, designed to be both **fast and efficient**. We view it as a practical “workhorse” model that can tackle a range of tasks without the overhead of larger architectures. * **#Parameters:** 24B * **Base Model:** Mistral-Small-24B-Instruct-2501 * Open-source and available on Hugging Face under the Apache-2.0 license: [arcee-ai/Arcee-Blitz](https://huggingface.co/arcee-ai/Arcee-Blitz) * **Top Use Cases:** * General-purpose task handling * Business communication * Automated document processing for mid-scale applicationsBlitz
Virtuoso (Small, Large, Medium) - General Purpose
### **Virtuoso Large** * **Description:** Our most powerful and versatile general-purpose model, designed to excel at handling complex and varied tasks across domains. With state-of-the-art performance, it offers unparalleled capability for nuanced understanding, contextual adaptability, and high accuracy. * **#Parameters:** 72B * **Base Model:** Qwen-2.5-72B * API Access is Available via Arcee Conductor: [https://conductor.arcee.ai](/arcee-orchestra/introduction-to-arcee-orchestra) * **Top Use Cases:** * Advanced content creation, such as technical writing and creative storytelling * Data summarization and report generation for cross-functional domains * Detailed knowledge synthesis and deep-dive insights from diverse datasets * Multilingual support for international operations and communications ### **Virtuoso Medium** * **Description:** A versatile and powerful model, capable of handling complex and varied tasks with precision and adaptability across multiple domains. Ideal for dynamic use cases requiring significant computational power. * **#Parameters:** 32B * **Base Model:** Qwen-2.5-32B * API Access is Available via Arcee Conductor: [https://conductor.arcee.ai](/arcee-orchestra/introduction-to-arcee-orchestra) * **Top Use Cases:** * Content generation * Knowledge retrieval * Advanced language understanding * Comprehensive data interpretation ### **Virtuoso Small** * **Description:** A streamlined version of Virtuoso, maintaining robust capabilities for handling complex tasks across domains while offering enhanced cost-efficiency and quicker response times. * **#Parameters:** 14B * **Base Model:** Qwen-2.5-14B * API access is available via Arcee Conductor: [https://conductor.arcee.ai](/arcee-orchestra/introduction-to-arcee-orchestra) * Open-source and available on Hugging Face under the Apache-2.0 license: [arcee-ai/Virtuoso-Small](https://huggingface.co/arcee-ai/Virtuoso-Small) * **Top use cases:** * General-purpose task handling * Business communication * Automated document processing for mid-scale applications \`Virtuoso
Coder (Small, Large) - Coding
### Coder Large * **Description:** A high-performance model tailored for intricate programming tasks, Coder-Large thrives in software development environments. With its focus on efficiency, reliability, and adaptability, it supports developers in crafting, debugging, and refining code for complex systems. * **#Parameters:** 32B * **Base Model:** Qwen-2.5-32B-Instruct * Arcee Conductor: [https://conductor.arcee.ai](/arcee-orchestra/introduction-to-arcee-orchestra) * **Top use cases:** * Writing modular, reusable code across various programming languages * Debugging and optimizing performance in large-scale applications * Generating efficient algorithms for computationally intensive tasks * Supporting DevOps processes, such as script automation and CI/CD pipelines ### Coder Small * **Description:** A compact, high-performance coding model designed for efficient programming tasks, including generating code, debugging, and optimizing scripts for smaller projects. * **#Parameters:** 14B * **Base Model:** Qwen-2.5-32B-Instruct * **Top use cases:** * Lightweight development tasks * Automated code reviews * Generating templates or prototypes quickly, code completionCoder
Caller (Large) - Tool Use and Function Call
### Caller * **Description:** Engineered for seamless integrations, Caller-Large is a robust model optimized for managing complex tool-based interactions and API function calls. Its strength lies in precise execution, intelligent orchestration, and effective communication between systems, making it indispensable for sophisticated automation pipelines. * **#Parameters:** 32B * **Base Model:** Qwen-2.5-32B * API Access is Available via Arcee Conductor: [https://conductor.arcee.ai](/arcee-orchestra/introduction-to-arcee-orchestra) * **Top use cases:** * Managing integrations between CRMs, ERPs, and other enterprise systems * Running multi-step workflows with intelligent condition handling * Orchestrating external tool interactions like calendar scheduling, email parsing, or data extraction * Real-time monitoring and diagnostics in IoT or SaaS environmentsCaller
Maestro - Reasoning
### Maestro * **Description:** An advanced reasoning model optimized for high-performance enterprise applications. Building on the innovative training techniques first deployed in maestro-7b-preview, Maestro-32B offers significantly enhanced reasoning capabilities at scale, rivaling or surpassing leading models like OpenAI’s O1 and DeepSeek’s R1, but at substantially reduced computational costs. * **#Parameters:** 32B * **Base Model:** Qwen-2.5-32B * API Access is Available via Arcee Conductor: [https://conductor.arcee.ai](/arcee-orchestra/introduction-to-arcee-orchestra) * Hybrid training method: 1. Warm-up (SFT Phase): Quick supervised fine-tuning phase to prime the model with high-quality reasoning exemplars. 2. RL Optimization Phase: Utilizes Reinforcement Learning techniques, specifically designed to boost logical coherence, depth of reasoning, and accurate inference by encouraging problem-solving from fundamental principles. * **Top Use Cases:** * Enterprise decision support systems * Complex analytical and logical inference tasks * Automated research and analysis workflows * Generative reasoning for technical and professional contextsMaestro
Evaluation Results of Arcee Models on Open LLM Leaderboard Benchmarks
Basic RAG Overview
Conductor Model Token Cost
Arcee Orchestra
News Analysis Workflow
Model Configuration
Model Node Settings
Results
When you `return` an expression after a function, it can be referenced via `{{code_block_name.data.results}}`. For example, the code: ``` def example(): welcome = "Welcome to Orchestra" return welcome example() ``` will output: ``` { "code_1": { "successfull": true, "data": { "results": "Welcome to Arcee Orchestra", "stdout": "", "stderr": "", "error": "", "sandbox_id": "sandbox-32ad" }, "error": null } } ``` `{{code_1.data.results}}` will return "Welcome to Arcee Orchestra".Standard Output
When you `print()` an expression, it can be referenced via `{{code_block_name.data.stdout}}`. For example, the code: ``` welcome = "Welcome to Orchestra" print(welcome) ``` will output: ``` { "code_1": { "successfull": true, "data": { "results": "", "stdout": "Welcome to Orchestra\n", "stderr": "", "error": "", "sandbox_id": "sandbox-054a" }, "error": null } } ``` `{{code_1.data.stdout}}` will return "Welcome to Arcee Orchestra".Code Node Name
Select the code node name for the output mapping when you want to reference a part of the code node output that is not `results` or `stdout`. For example, if you have a code node named `code 1` and you wanted to check if there was an error in the code node, you could use: `{{code_1.data.error}}`Integrations
Actions
Integration Example
Data Upload
Knowledge Retrieval Node
Conditional Node Workflow
Condition
Add a New API Token
Add New API Token
List Available Workflows Response Example
Each workflow returned in the API list will include the following information.[
{
"workflow_id": "1234f567-bb12-1234-b7cd-f7a71a98cd24",
"user_id": "2*****************1",
"organization_id": "2*****************7",
"created_at": "2025-03-12 00:59:55.465508+00:00",
"created_by": "2*****************1",
"description": "A workflow that sends a simple \"hi\" message using the Virtuoso-Large model, starting from a start node, processing the message, and ending with the model's output.",
"file_metadata": {
"bucket": "orchestra-workflow-dev2025010618392776890000000a",
"url": "https://orchestra-workflow-dev2025010618392776890000000a.s3.amazonaws.com/288772212935168457/workflows/hi_20250312_005955_d5f4e21a-f49b-4420-a708-d40b252092be.yaml",
"key": "288772212935168457/workflows/hi_20250312_005955_d5f4e21a-f49b-4420-a708-d40b252092be.yaml"
},
"graph": {
"settings": {
"support_multiple_edges": true
},
"input_data": {},
"description": "A workflow that sends a simple \"hi\" message using the Virtuoso-Large model, starting from a start node, processing the message, and ending with the model's output.",
"nodes": {
"model_1": {
"type": "model",
"metadata": {
"description": "Model node",
"model": "virtuoso-large",
"stream": false,
"max_tokens": 8192,
"temperature": 0.7,
"top_p": 1,
"top_k": 50,
"repetition_penalty": 0,
"messages": [
{
"role": "system",
"content": ""
},
{
"role": "user",
"content": "say hi"
}
],
"output_mapping": {
"model_1": "choices[0].message.content"
}
}
}
},
"edges": [
[
"START",
"model_1"
],
[
"model_1",
"END"
]
],
"entry_point": "model_1",
"finish_point": "model_1",
"selectedOutput": "model_1",
"node_metadata": "{\"nodes\":[{\"id\":\"74326564-3041-41bc-bedc-44053c68bb7c\",\"type\":\"start\",\"data\":{\"inputData\":[],\"description\":\"\",\"outputData\":[],\"messages\":[],\"customName\":\"Start 1\",\"customLabel\":\"start_1\"},\"position\":{\"x\":400,\"y\":250},\"width\":260,\"height\":102},{\"id\":\"eafa038c-222a-4755-b240-a1d10c41fc8d\",\"type\":\"model\",\"data\":{\"inputData\":[],\"model\":{\"fullName\":\"virtuoso-large\",\"shortName\":\"Virtuoso-Large\",\"avatar\":\"/assets/Logo-small-DyzsrxbN.jpg\",\"modelName\":\"virtuoso-large\",\"key\":\"virtuosoLarge\"},\"outputName\":\"model_1\",\"description\":\"Model node\",\"output_mapping\":\"model_1\",\"modelSettings\":{\"max_tokens\":8192,\"temperature\":0.7,\"top_p\":1,\"top_k\":50,\"repetition_penalty\":0,\"stream\":false},\"customName\":\"Model 1\",\"customLabel\":\"model_1\",\"outputData\":[{\"id\":\"3487fe34-6ea3-4e27-b339-e1e919ec3216\",\"type\":\"variable\",\"outputVariable\":{\"variableName\":\"model_1\",\"variableType\":\"Text\",\"nodeName\":\"Model\"}}],\"messages\":[{\"role\":\"system\",\"content\":\"\"},{\"role\":\"user\",\"content\":\"say hi\"}]},\"position\":{\"x\":500,\"y\":360},\"width\":260,\"height\":86,\"selected\":false,\"dragging\":false,\"messages\":[{\"role\":\"system\",\"content\":\"\"},{\"role\":\"user\",\"content\":\"say hi\"}]},{\"id\":\"f5023d4b-fd60-4045-8c57-baf6ad89bfca\",\"type\":\"end\",\"data\":{\"label\":\"End\",\"description\":\"\",\"outputData\":[],\"customName\":\"End 1\",\"customLabel\":\"end_1\",\"output_variable\":{\"model_1\":\"Model 1\"},\"output_mapping\":\"model_1\"},\"position\":{\"x\":675,\"y\":470},\"width\":260,\"height\":132,\"selected\":true,\"dragging\":false}],\"edges\":[{\"id\":\"f88ce626-0f2e-4bf8-b559-8ba1691dbe61\",\"source\":\"74326564-3041-41bc-bedc-44053c68bb7c\",\"target\":\"eafa038c-222a-4755-b240-a1d10c41fc8d\",\"type\":\"default\"},{\"id\":\"a18e985c-67f5-4e15-bee9-d23199a905ad\",\"source\":\"eafa038c-222a-4755-b240-a1d10c41fc8d\",\"target\":\"f5023d4b-fd60-4045-8c57-baf6ad89bfca\",\"type\":\"default\"}],\"viewport\":{},\"avatar\":{\"emoji\":\"🤖\",\"bgColor\":\"hsl(160, 65%, 75%)\"},\"public\":false}"
},
"name": "hi",
"status": "active",
"updated_at": "2025-03-12 00:59:55.465513+00:00",
"updated_by": 2*****************1,
"version": "1.0.0",
"public": false
}
]
Execute a Workflow Response Example
```json { "message": "Workflow execution started", "run_id": "fda37c90-dd1c-4317-82d1-797b7f77fca5" } ```Execution Steps Response Example
This example includes the output for a workflow which took in a prompt, generated a response with a model node and then emailed the response. ``` [ { "id": "b2acc986-af06-418a-bf71-c730d5ghaf0b", "user_id": "2*****************1", "organization_id": "2*****************7", "node_id": "model", "status": "completed", "start_time": "2025-03-13T02:41:15.144470Z", "end_time": "2025-03-13T02:41:17.382162Z", "elapsed_time": 2, "error": null, "outputs": { "id": "chatcmpl-a9fd051615c7405e8d299c797ee16223", "object": "chat.completion", "created": 1741833676, "model": "virtuoso-medium", "choices": [ { "index": 0, "message": { "role": "assistant", "reasoning_content": null, "content": "Why was the weather forecast sad?\n\nBecause it had a lot of problems and was feeling a little \"forecast-ticular.\" \n\n(Note: \"Forecast-ticular\" is a play on words, combining \"forecast\" and \"particular.\")", "tool_calls": [] }, "logprobs": null, "finish_reason": "stop", "stop_reason": null } ], "usage": { "prompt_tokens": 36, "total_tokens": 84, "completion_tokens": 48, "prompt_tokens_details": null }, "prompt_logprobs": null, "confidence_info": { "error": "No logprobs available - model/provider may not support confidence calculation", "confidence_score": null, "mean_entropy": null, "total_entropy": null } }, "inputs": { "model": "virtuoso-medium", "stream": false, "max_tokens": 8192, "temperature": 0.7, "top_p": 1, "top_k": 50, "repetition_penalty": 0, "messages": [ { "role": "user", "content": "Tell me a joke about the weather" } ], "run_id": "5b69669f-975a-49af-a33a-61ff9ae5a20f", "input_data": { "prompt": "Tell me a joke about the weather" } }, "step_metadata": null, "tokens": 0 }, { "id": "ad6edfea-bf1c-4639-9d9f-b53c9ad60bgg", "user_id": "2*****************1", "organization_id": "2*****************7", "node_id": "slack_integration", "status": "completed", "start_time": "2025-03-13T02:41:17.403879Z", "end_time": "2025-03-13T02:41:19.179523Z", "elapsed_time": 1, "error": null, "outputs": { "result": { "data": { "ok": true, "channel": "C0257V9FS09", "ts": "1741833679.099979", "warning": "missing_charset", "response_metadata": { "warnings": [ "missing_charset" ] } }, "error": null, "successfull": true, "successful": true } }, "inputs": { "app_name": "slack", "action": "SLACK_SHARE_A_ME_MESSAGE_IN_A_CHANNEL", "entity_id": "a***********m", "workspace_type": "local", "input_mapping": { "channel": "orchestra-demo", "text": "Why was the weather forecast sad?\n\nBecause it had a lot of problems and was feeling a little \"forecast-ticular.\" \n\n(Note: \"Forecast-ticular\" is a play on words, combining \"forecast\" and \"particular.\")" }, "tool_config": { "tool_type": "slack" }, "run_id": "5b69669f-975a-49af-a33a-61ff9ae5a20f", "input_data": { "prompt": "Tell me a joke about the weather", "model": "Why was the weather forecast sad?\n\nBecause it had a lot of problems and was feeling a little \"forecast-ticular.\" \n\n(Note: \"Forecast-ticular\" is a play on words, combining \"forecast\" and \"particular.\")" } }, "step_metadata": null, "tokens": 0 } ] ```Retrieve all Runs Response Example
{% code overflow="wrap" %} ```json [ { "id": "a40d4d53-7e5e-432b-8f23-8afdc53eefa3", "status": "completed", "start_time": "2025-03-12T03:39:30.476391Z", "end_time": "2025-03-12T03:39:36.133074Z", "elapsed_time": 5, "error": null, "outputs": "Here's a weather-related joke for you:\n\nWhy did the cloud cross the sky?\n\nBecause it wanted to go to the other side of the rainbow!\n\nI hope you found this joke sunny and amusing! If you'd like another weather joke or a different kind of joke, please let me know.", "all_outputs": { "prompt": "Tell me a joke about the weather", "model": "Here's a weather-related joke for you:\n\nWhy did the cloud cross the sky?\n\nBecause it wanted to go to the other side of the rainbow!\n\nI hope you found this joke sunny and amusing! If you'd like another weather joke or a different kind of joke, please let me know.", }, "inputs": { "prompt": "Tell me a joke about the weather" } }, { ... } ] ``` {% endcode %}Retrieve Diagram Response Example
{% code overflow="wrap" %} ```json { "diagram": "graph TD\n model[\"modelModel node\"]\n slack_integration[\"slack_integration
\"]\n START((START))\n END((END))\n slack_integration --> END\n START --> model\n model --> slack_integration\n style model fill:#bbf,stroke:#333,stroke-width:1px,color:#fff\n style slack_integration fill:#bbf,stroke:#333,stroke-width:1px,color:#fff\n style START fill:#9f9,stroke:#333,stroke-width:2px,color:#fff\n style END fill:#f99,stroke:#333,stroke-width:2px,color:#fff" } ``` {% endcode %}
Download workflow JSON in the workflow builder
``` [{'id': 'THE_RUN_ID', 'status': 'completed', 'start_time': '2025-04-24T03:18:12.322630Z', 'end_time': '2025-04-24T03:18:13.048954Z', 'elapsed_time': 0, 'error': None, 'outputs': {'query': 'Hello from Python', 'model_1': 'Hello there! How can I assist you today?'}, 'all_outputs': {'query': 'Hello from Python', 'model_1': 'Hello there! How can I assist you today?'}, 'inputs': {'query': 'Hello from Python'}}] ``` To check the granular status of each step you can check the [Workflow Execution Steps API](/arcee-orchestra/workflows/api-invocation/workflow-execution-steps-api). # Workflow Library The workflow library serves as a repository for common use cases and workflows, which you can download and utilize in Arcee Orchestra. Each use case within the workflow library contains: 1. A video demonstration of the workflow 2. The workflow JSON so you can upload the workflow and try it yourself 3. Example input/output for the specific workflow These workflows provide examples of how different integrations can be utilized, how nodes can be combined to create complex automations, and can serve as a starting place for one of your workflows. # Research Automation The Market Research workflow makes it quick and easy to conduct research on a topic and generate a report with the findings. The workflow takes in a user question, researches the topic, creates both a brief and detailed summary, performs data extraction, generates a report and uploads the report to Google Docs and sends an email.
Research Automation Workflow
Real Time Financial Analysis
Real Time Financial Analysis Output
Blog Writer Workflow
Code Improvement Workflow
Energy Domain Assistant Workflow
Output Email for Energy Domain Assistant
Workflow Selection