Evolution API Python API Docs | dltHub

Evolution API is a REST API that exposes WhatsApp/instance management operations (instance creation, connection, messaging and webhooks) for an EvolutionAPI server. The REST API base URL is https://{server-url} and Requests do not use a global bearer or API‑key scheme; authentication is scoped to a specific instance identifier in the path..

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading Evolution API data in under 10 minutes.

---

What data can I load from Evolution API?

Here are some of the endpoints you can load from Evolution API:

---

How do I authenticate with the Evolution API API?

The published OpenAPI fragments show no global auth scheme. Most endpoints require the instance identifier as a path parameter (named instance). Some deployments may add API‑key or token headers, which should be obtained from the operator.

1. Get your credentials

1. Deploy or obtain an EvolutionAPI instance (hosted domain or self‑hosted).\n2) Note the instance identifier or domain assigned by the operator.\n3) If the deployment provides an access token or API key (not defined in the upstream OpenAPI), retrieve it from the operator/manager UI.\n4) Use the instance id in path parameters when calling the API; include any provider‑specified headers if required by your deployment.

2. Add them to .dlt/secrets.toml

Copy
[sources.evolution_api_source]
instance = "your_instance_id_or_domain"
# if your deployment requires an API key or token, add the provider header key, for example:
# api_key = "your_api_key_here"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

---

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

Copy
uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

Copy
dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

Copy
dlt ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Evolution API API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

Copy
python evolution_api_pipeline.py

If everything is configured correctly, you'll see output like this:

Copy
Pipeline evolution_api_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset evolution_api_data
The duckdb destination used duckdb:/evolution_api.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

Copy
dlt pipeline evolution_api_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

---

Python pipeline example

This example loads instances and messages from the Evolution API API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

Copy
import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def evolution_api_source(instance=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{server-url}",
            "auth": {
                "type": "none",
                "instance": instance,
            },
        },
        "resources": [
            {"name": "instances", "endpoint": {"path": "instances", "data_selector": "instances"}},
            {"name": "messages", "endpoint": {"path": "messages", "data_selector": "messages"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="evolution_api_pipeline",
        destination="duckdb",
        dataset_name="evolution_api_data",
    )
    load_info = pipeline.run(evolution_api_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

---

How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

Copy
import dlt

data = dlt.pipeline("evolution_api_pipeline").dataset()
sessions_df = data.instances.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM evolution_api_data.instances LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("evolution_api_pipeline").dataset()
data.instances.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

---

What destinations can I load Evolution API data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

---

Troubleshooting

Authentication / instance not found

If you receive 404 or "instance not found", verify the instance path parameter matches your assigned instance ID or domain. Some providers enforce additional API keys/tokens; check the manager/hosting UI for required headers.

401 / 403 Access Denied

The upstream OpenAPI shows no global auth scheme; however deployments may add API gateway auth. If you get 401/403, check for a required Authorization header or API key in the provider portal and include it per operator docs.

Rate limiting / 429

If you receive 429 Too Many Requests, back off and retry with exponential backoff. Check hosting provider docs for rate limit windows and plan limits.

Pagination quirks

List endpoints commonly return arrays under plural keys (e.g., "instances", "messages"). If pagination is applied, check for paging keys such as "page", "per_page", "total" or a top‑level "data"/"meta" structure depending on deployment version.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

---

Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

Copy
dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack