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‎ai/ai-document-understanding/ai-email-invoice/files/README.md‎

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# Invoice Document Processing from Gmail into ERP Systems using OCI Document Understanding & Oracle Integration Cloud
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Reviewed: 30.10.2024
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Reviewed: 04.11.2025
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# Introduction
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‎ai/gen-ai-agents/assistant-secretary-agent/files/local_requirements.txt‎

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langchain-experimental==0.3.4
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langchain-text-splitters==0.3.9
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langgraph==0.2.62
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langgraph-checkpoint==2.0.8
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langgraph-checkpoint==3.0.0
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langgraph-sdk==0.1.51
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langsmith==0.2.11
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markdown-it-py==3.0.0

‎ai/gen-ai-agents/crewai-oci-integration/LICENSE‎

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MIT License
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Copyright (c) 2025 Luigi Saetta
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.

‎ai/gen-ai-agents/crewai-oci-integration/README.md‎

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# CrewAI ↔ OCI Generative AI Integration
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This repository provides examples and configuration guidelines for integrating **[CrewAI](https://github.com/joaomdmoura/crewAI)** with **Oracle Cloud Infrastructure (OCI) Generative AI** services.
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The goal is to demonstrate how CrewAI agents can seamlessly leverage OCI-hosted models through the **LiteLLM gateway**.
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Reviewed: 31.10.2025
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---
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## 🔐 Security Configuration
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Before running the demos, you must configure access credentials for OCI.
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In these examples, we use a **locally stored key pair** for authentication.
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Ensure your local OCI configuration (`~/.oci/config` and private key) is correctly set up and accessible to the Python SDK.
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To correctly start the **LiteLLM gateway** you need to create and configure correctly a **config.yml** file. To create this file use the [template](./config_template.yml).
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In addition, you should be **enabled** to use OCI Generative AI Service in your tenant. If you haven't yet used OCI GenAI ask to your tenant's admin to setup the **needed policies**.
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---
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## 🧩 Demos Included
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- [Simple CrewAI Agent](./simple_test_crewai_agent.py) — basic CrewAI agent interacting with an LLM through OCI
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- [OCi Consumption Report](./crew_agent_mcp02.py)
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- *(More demos to be added soon)*
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---
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## 📦 Dependencies
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The project relies on the following main packages:
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| Dependency | Purpose |
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|-------------|----------|
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| **CrewAI** | Framework for creating multi-agent workflows |
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| **OCI Python SDK** | Access OCI services programmatically |
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| **LiteLLM (Gateway)** | OpenAI-compatible proxy for accessing OCI Generative AI models |
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To connect CrewAI to OCI models, we use a **LiteLLM gateway**, which exposes OCI GenAI via an **OpenAI-compatible** REST API.
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---
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## ⚙️ Environment Setup
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1. **Create a Conda environment**
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```bash
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conda create -n crewai python=3.11
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```
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2. **Activate** the environment
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```
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conda activate crewai
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```
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3. **Install** the required **packages**
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```
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pip install -U oci litellm "litellm[proxy]" crewai
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```
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4. Run the LiteLLM Gateway
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Start the LiteLLM gateway using your configuration file (config.yml):
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```
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./start_gateway.sh
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```
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Make sure the gateway starts successfully and is listening on the configured port (e.g., http://localhost:4000/v1).
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🧠 Test the Integration
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Run the sample CrewAI agent to verify that CrewAI can connect to OCI through LiteLLM:
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```
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python simple_test_crewai_agent.py
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```
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If the setup is correct, you should see the agent’s output using an OCI model.
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## Integrate Agents with **MCP** servers.
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Install this additional package:
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```
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pip install 'crewai-tools[mcp]'
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```
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You can test the integration with **MCP** using [OCI Consumption report](./crew_agent_mcp02.py) that generates a report
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of the consumption in your tenant (top 5 compartments, for 4 weeks).
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To have this demo up&running:
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* download the code for the MCP server from [here](https://github.com/oracle-devrel/technology-engineering/blob/main/ai/gen-ai-agents/mcp-oci-integration/mcp_consumption.py)
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* start the MCP server, on a free port (for example 9500)
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* register the URL, in [source](./crew_agent_mcp02.py), in the section:
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```
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server_params = {
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"url": "http://localhost:9500/mcp",
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"transport": "streamable-http"
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}
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```
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If you don't want to secure (with JWT) the communication with the MCP server, put
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```
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ENABLE_JWT_TOKEN = False
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```
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in the config.py file.
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‎ai/gen-ai-agents/crewai-oci-integration/config_template.yml‎

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# config.yaml for litellm with OCI Grok models
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litellm_settings:
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drop_params: true
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# drop unsupported params instead of 500 errors
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additional_drop_params: ["max_retries"]
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# Common OCI connection parameters
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common_oci: &common_oci
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provider: oci
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oci_region: us-chicago-1
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oci_serving_mode: ON_DEMAND
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supports_tool_calls: true
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oci_user: your-oci-user-ocid
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oci_fingerprint: your-oci-api-key-fingerprint
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oci_tenancy: your-oci-tenancy-ocid
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oci_compartment_id: your-oci-compartment-ocid
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oci_key_file: /path/to/your/oci_api_key.pem
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api_key: key4321
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# List of models
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model_list:
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- model_name: grok4-oci
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litellm_params:
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<<: *common_oci # merge common OCI params
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model: oci/xai.grok-4
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- model_name: grok4-fast-oci
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litellm_params:
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<<: *common_oci
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model: oci/xai.grok-4-fast-reasoning
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general_settings:
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telemetry: false
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proxy_logging: false
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allow_model_alias: true

‎ai/gen-ai-agents/crewai-oci-integration/crew_agent_mcp01.py‎

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"""
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CrewAI agent with MCP
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This one is doing Deep research using internet search tools via MCP server.
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see:
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https://docs.crewai.com/en/mcp/overview
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https://docs.crewai.com/en/mcp/multiple-servers
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"""
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import os
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from crewai import Agent, Task, Crew, LLM
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from crewai_tools import MCPServerAdapter
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# Disable telemetry, tracing, and logging
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os.environ["CREWAI_LOGGING_ENABLED"] = "false"
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os.environ["CREWAI_TELEMETRY_ENABLED"] = "false"
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os.environ["CREWAI_TRACING_ENABLED"] = "false"
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llm = LLM(
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model="grok4-fast-oci",
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# LiteLLM proxy endpoint
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base_url="http://localhost:4000/v1",
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api_key="sk-local-any",
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temperature=0.2,
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max_tokens=4000,
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)
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server_params = {
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"url": "http://localhost:8500/mcp",
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"transport": "streamable-http"
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}
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# Create agent with MCP tools
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with MCPServerAdapter(server_params, connect_timeout=60) as mcp_tools:
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print(f"Available tools: {[tool.name for tool in mcp_tools]}")
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research_agent = Agent(
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role="Research Analyst",
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goal="Find and analyze information using advanced search tools",
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backstory="Expert researcher with access to multiple data sources",
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llm=llm,
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tools=mcp_tools,
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verbose=True
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)
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# Create task
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research_task = Task(
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description="Research the latest developments in AI agent frameworks",
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expected_output="Comprehensive research report with citations",
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agent=research_agent
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)
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# Create and run crew
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crew = Crew(agents=[research_agent], tasks=[research_task])
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result = crew.kickoff()
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print(result)

‎ai/gen-ai-agents/crewai-oci-integration/crew_agent_mcp02.py‎

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"""
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CrewAI agent with MCP
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This one is analyzing tenant consumption via MCP server.
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see:
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https://docs.crewai.com/en/mcp/overview
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https://docs.crewai.com/en/mcp/multiple-servers
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"""
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import os
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from datetime import datetime
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from crewai import Agent, Task, Crew, LLM
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from crewai_tools import MCPServerAdapter
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# Disable telemetry, tracing, and logging
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os.environ["CREWAI_LOGGING_ENABLED"] = "false"
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os.environ["CREWAI_TELEMETRY_ENABLED"] = "false"
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os.environ["CREWAI_TRACING_ENABLED"] = "false"
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llm = LLM(
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model="grok4-oci",
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# LiteLLM proxy endpoint
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base_url="http://localhost:4000/v1",
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api_key="sk-local-any",
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temperature=0.,
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max_tokens=4000,
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)
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# OCI consumption
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server_params = {
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"url": "http://localhost:9500/mcp",
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"transport": "streamable-http"
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}
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# Create agent with MCP tools
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with MCPServerAdapter(server_params, connect_timeout=60) as mcp_tools:
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print(f"Available tools: {[tool.name for tool in mcp_tools]}")
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research_agent = Agent(
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role="OCI Consumption Analyst",
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goal="Find and analyze information about OCI tenant consumption.",
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backstory="Expert analyst with access to multiple data sources",
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llm=llm,
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tools=mcp_tools,
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max_iter=30,
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max_retry_limit=5,
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verbose=True
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)
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# Create task
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research_task = Task(
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description="Identify the top 5 compartments by consumption (amount) for the OCI tenant "
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"in the weeks of the month of september 2025, analyze the trends and provide insights on usage patterns."
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"Analyze fully the top 5 compartments. Use only the amount, not the quantity.",
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expected_output="Comprehensive report with data-backed insights.",
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agent=research_agent
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)
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# Create and run crew
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crew = Crew(agents=[research_agent], tasks=[research_task])
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result = crew.kickoff()
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print(result)
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# --- Save the result to a Markdown file ---
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# Create an output directory if it doesn’t exist
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output_dir = "reports"
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os.makedirs(output_dir, exist_ok=True)
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# Use timestamped filename for clarity
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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output_path = os.path.join(output_dir, f"oci_consumption_report_{timestamp}.md")
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# Write the result
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with open(output_path, "w", encoding="utf-8") as f:
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f.write(str(result))
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print(f"\n✅ Report saved successfully to: {output_path}")

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