> ## Documentation Index > Fetch the complete documentation index at: https://langchain-5e9cc07a-preview-docscl-1781043860-248c713.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # Build a data analysis agent from scratch > Build a data analysis agent step by step using create_agent and deepagents middleware. This guide builds a data analysis agent from first principles using `create_agent` and deepagents middleware. Rather than starting with `create_deep_agent`, we assemble the harness one piece at a time: so you can see exactly what each component adds and swap in only what your use case needs. The agent we'll build: 1. Accepts a CSV file for analysis 2. Writes and executes Python code in an isolated sandbox 3. Delegates visualization work to a specialized subagent 4. Loads data analysis patterns from a skills file ## Setup ```bash theme={null} pip install deepagents langsmith ``` Enable LangSmith tracing to inspect every step: ```bash theme={null} export LANGSMITH_TRACING=true export LANGSMITH_API_KEY=... ``` *** ## Step 1: The minimal agent A model, a loop. Nothing else yet. ```python theme={null} from langchain.agents import create_agent agent = create_agent("anthropic:claude-sonnet-4-6", tools=[]) ``` This runs, but the agent has no filesystem and no way to execute code. The next steps add those. *** ## Step 2: Add a sandbox backend `LangSmithSandbox` gives the agent an isolated environment with a filesystem and an `execute` tool for running shell commands. The agent can install packages, write scripts, and run them: without touching the host. ```python theme={null} from langchain.agents import create_agent from langsmith.sandbox import SandboxClient from deepagents.backends.langsmith import LangSmithSandbox from deepagents.middleware import FilesystemMiddleware client = SandboxClient() sandbox = client.create_sandbox(template_name="deepagents-deploy") backend = LangSmithSandbox(sandbox=sandbox) agent = create_agent( "anthropic:claude-sonnet-4-6", tools=[], middleware=[FilesystemMiddleware(backend=backend)], ) ``` [`FilesystemMiddleware`](https://reference.langchain.com/python/deepagents/middleware/filesystem/FilesystemMiddleware) adds `read_file`, `write_file`, `edit_file`, `glob`, and `grep`. Because `LangSmithSandbox` implements the sandbox protocol, it also adds `execute`: the agent can now run shell commands. Upload a CSV and invoke: ```python theme={null} import csv, io rows = [ ["Date", "Product", "Units", "Revenue"], ["2025-08-01", "Widget A", 10, 250], ["2025-08-02", "Widget B", 5, 125], ["2025-08-03", "Widget A", 7, 175], ["2025-08-04", "Widget C", 3, 90], ] buf = io.StringIO() csv.writer(buf).writerows(rows) backend.upload("sales.csv", buf.getvalue().encode()) result = agent.invoke({ "messages": [{"role": "user", "content": "Analyze sales.csv. Summarize trends."}] }) ``` *** ## Step 3: Add context management For longer analysis sessions the context window fills. `SummarizationMiddleware` compresses history automatically so the agent keeps working without hitting token limits. ```python theme={null} from deepagents.middleware import FilesystemMiddleware, SummarizationMiddleware model = "anthropic:claude-sonnet-4-6" agent = create_agent( model=model, tools=[], middleware=[ FilesystemMiddleware(backend=backend), SummarizationMiddleware(model=model, backend=backend), ], ) ``` *** ## Step 4: Add skills Skills give the agent on-demand domain knowledge via progressive disclosure: loaded only when the current task calls for it. Create a skill file in your skills directory: ``` skills/ pandas-patterns/ SKILL.md ``` ```markdown theme={null} --- name: pandas-patterns description: Common pandas and matplotlib patterns for data analysis and visualization --- ## Data loading Use `pd.read_csv()` for CSV files. Always check `df.info()` and `df.describe()` first. ## Visualization Use `matplotlib` for bar charts, `seaborn` for statistical plots. Save figures with `plt.savefig("output.png", dpi=150, bbox_inches="tight")`. ## Reporting Write a markdown summary to `report.md` alongside any generated charts. ``` ```python theme={null} from deepagents.middleware import FilesystemMiddleware, SkillsMiddleware, SummarizationMiddleware agent = create_agent( model=model, tools=[], middleware=[ FilesystemMiddleware(backend=backend), SummarizationMiddleware(model=model, backend=backend), SkillsMiddleware(backend=backend, sources=["./skills/"]), ], ) ``` *** ## Step 5: Add a visualization subagent Some tasks benefit from isolation. A visualization subagent runs in its own context window, keeping chart generation separate from the main analysis: and enabling parallel execution. ```python theme={null} from langchain.agents.middleware import TodoListMiddleware from deepagents import SubAgent from deepagents.middleware import ( FilesystemMiddleware, SkillsMiddleware, SubAgentMiddleware, SummarizationMiddleware, ) visualizer: SubAgent = { "name": "visualizer", "description": "Generates charts and visualizations from data files in the sandbox.", "system_prompt": "You are a data visualization specialist. Write Python scripts using matplotlib and seaborn. Save all figures as PNG files.", "tools": [], } agent = create_agent( model=model, tools=[], middleware=[ FilesystemMiddleware(backend=backend), SummarizationMiddleware(model=model, backend=backend), SkillsMiddleware(backend=backend, sources=["./skills/"]), TodoListMiddleware(), SubAgentMiddleware(backend=backend, subagents=[visualizer]), ], ) ``` The main agent handles analysis and planning; it delegates chart generation to the `visualizer` subagent via the `task` tool. *** ## What you built | Middleware | What it adds | | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------ | | [`FilesystemMiddleware`](https://reference.langchain.com/python/deepagents/middleware/filesystem/FilesystemMiddleware) + `LangSmithSandbox` | Isolated filesystem + `execute` tool | | [`SummarizationMiddleware`](https://reference.langchain.com/python/langchain/agents/middleware/summarization/SummarizationMiddleware) | Automatic context compression | | [`SkillsMiddleware`](https://reference.langchain.com/python/deepagents/middleware/skills/SkillsMiddleware) | Domain knowledge loaded on demand | | [`TodoListMiddleware`](https://reference.langchain.com/python/langchain/agents/middleware/todo/TodoListMiddleware) + [`SubAgentMiddleware`](https://reference.langchain.com/python/deepagents/middleware/subagents/SubAgentMiddleware) | Parallel visualization subagent | This is the same foundation as `create_deep_agent`: assembled manually so you control exactly what's included. The possibilities don't end here: see [Prebuilt middleware](/oss/python/langchain/middleware/built-in) for the full list of composable capabilities, and the [`create_agent`](https://reference.langchain.com/python/langchain/agents/factory/create_agent) reference for all configuration options. For a pre-assembled version, see [`create_deep_agent`](https://reference.langchain.com/python/deepagents/graph/create_deep_agent) and [Customize Deep Agents](/oss/python/deepagents/customization). For the full data analysis example using `create_deep_agent`, see [Data analysis](/oss/python/deepagents/data-analysis). ***
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