1. Symptoms
When OpenClaw encounters the clw-llm-not-found error, the toolchain halts execution at the point where it attempts to invoke a Large Language Model provider. Users typically observe the error manifested through several distinct indicators that signal the provider resolution failure.
In the command-line interface, the error manifests as a non-zero exit code accompanied by a descriptive message. The terminal output commonly displays a message resembling the following pattern:
[OpenClaw] ERROR clw-llm-not-found: LLM provider 'provider-name' could not be resolved.
Please verify that the provider is installed and properly configured in your openclaw.yaml.
In structured JSON output mode, the error payload includes a code field with the value clw-llm-not-found, a provider field identifying which LLM provider failed, and an optional suggestion field offering remediation guidance. The application exits with a status code typically in the range of 20 to 30, depending on the specific OpenClaw version and invocation context.
Runtime behavior changes significantly when this error occurs. Any downstream tasks that depend on LLM inference—such as automated code review, documentation generation, or intelligent prompt enhancement—fail to execute. The error surfaces early in the execution pipeline, often before any API calls are made, indicating that the failure occurs at the configuration resolution stage rather than during network communication with the LLM provider.
Configuration file inspection reveals that the specified provider name either does not exist within the OpenClaw supported provider registry, has been misspelled in the configuration, or has not been registered with the OpenClaw plugin system. In some cases, the provider package itself is installed but fails to expose the expected entry point, causing the resolution mechanism to report the provider as missing.
2. Root Cause
The clw-llm-not-found error originates from OpenClaw’s provider resolution subsystem, which validates and instantiates LLM providers based on configuration directives. Understanding the architecture of this resolution process illuminates why the error occurs and how to prevent it.
OpenClaw maintains an internal provider registry that maps human-readable provider names to their respective implementation modules. When a user specifies a provider in their openclaw.yaml configuration file, the core engine consults this registry to locate the corresponding plugin. If the requested name does not appear in the registry, or if the registry itself cannot be accessed, the engine raises the clw-llm-not-found error before any further processing occurs.
Several underlying conditions can cause this resolution failure. The most common cause is a simple mismatch between the provider name specified in configuration and the canonical names registered by installed provider packages. Provider packages like openclaw-provider-openai, openclaw-provider-anthropic, and openclaw-provider-ollama register themselves under specific identifiers. Typos, case sensitivity issues, or the use of deprecated provider aliases frequently trigger the not-found error.
A second major cause involves incomplete plugin installation. When a provider package is listed as a dependency but fails to install correctly—due to network issues, permission problems, or Python package resolution conflicts—the package’s entry point may not be registered with OpenClaw’s plugin system. The core engine searches for the provider, finds no registered implementation, and reports the error.
Version incompatibility between OpenClaw and its provider plugins represents a subtler but equally disruptive cause. If a provider plugin was built against an older version of the OpenClaw core API, the plugin registration mechanism may silently fail, leaving the provider unavailable despite appearing in the package list. This scenario is particularly common when users upgrade OpenClaw without updating their provider packages, or vice versa.
Environment isolation issues can also produce the clw-llm-not-found error. In containerized environments or virtualized setups, the Python environment where OpenClaw is installed may differ from the environment where provider packages were installed. When OpenClaw executes within a specific virtual environment or conda environment that lacks the provider packages, the resolution subsystem cannot locate them even though they exist elsewhere on the system.
Finally, misconfigured environment variables that control OpenClaw’s plugin search paths can prevent the engine from discovering installed providers. The OPENCLAW_PLUGIN_PATH environment variable, when set incorrectly, redirects the plugin loader to directories that do not contain the provider implementations.
3. Step-by-Step Fix
Resolving the clw-llm-not-found error requires systematic investigation of the provider configuration, installation state, and environment setup. Follow these steps in sequence to identify and eliminate the root cause.
Step 1: Verify the provider name in your configuration file.
Open your openclaw.yaml configuration file and locate the llm section. Confirm that the provider field contains a valid, correctly spelled provider name.
Before:
llm:
provider: opanai # misspelled
model: gpt-4
api_key: ${OPENAI_API_KEY}
After:
llm:
provider: openai # corrected spelling
model: gpt-4
api_key: ${OPENAI_API_KEY}
Run the following command to list all providers currently registered with your OpenClaw installation:
openclaw provider list
Compare the output against the provider name in your configuration file. Use the exact name from the list, respecting case sensitivity and hyphens.
Step 2: Confirm the provider package is installed.
Check whether the required provider package exists in your Python environment. The package name typically follows the pattern openclaw-provider-<name>.
pip list | grep openclaw-provider
If the package is missing, install it using pip:
pip install openclaw-provider-openai
For providers requiring additional dependencies, you may need to install them explicitly:
pip install openclaw-provider-anthropic
pip install openclaw-provider-ollama
Step 3: Re-register the provider plugin.
In some cases, the plugin registration becomes stale. Re-register the provider by reinstalling the package:
pip uninstall openclaw-provider-openai -y
pip install openclaw-provider-openai
Alternatively, use OpenClaw’s built-in plugin refresh command if available:
openclaw plugin refresh
Step 4: Check environment isolation.
If you are using virtual environments, conda environments, or Docker containers, ensure that OpenClaw and all provider packages reside in the same environment. Activate the correct environment before running OpenClaw commands.
source /path/to/venv/bin/activate
openclaw --version
openclaw provider list
When using Docker, verify that the Dockerfile installs both OpenClaw and the required provider packages in the same image layer:
Before:
FROM python:3.11-slim
RUN pip install openclaw
After:
FROM python:3.11-slim
RUN pip install openclaw openclaw-provider-openai openclaw-provider-anthropic
Step 5: Validate the plugin search path.
If you have set a custom plugin path, verify that it points to the correct directory:
echo $OPENCLAW_PLUGIN_PATH
ls -la $OPENCLAW_PLUGIN_PATH
Ensure the provider packages’ entry point files are present within the configured directory. If the path is incorrect, either unset the variable to use the default behavior or correct the path to point to a directory containing valid provider implementations.
Step 6: Update OpenClaw and provider packages to compatible versions.
Incompatibility between versions is a frequent source of provider resolution failures. Upgrade both OpenClaw and all provider packages simultaneously to ensure API compatibility:
pip install --upgrade openclaw
pip install --upgrade openclaw-provider-openai openclaw-provider-anthropic openclaw-provider-ollama
Consult the OpenClaw compatibility matrix in the official documentation to confirm that your provider versions support your OpenClaw version.
4. Verification
After applying the fix, verifying that the error has been resolved requires confirming both the provider registration and the ability to execute a basic LLM operation.
First, confirm that the provider appears in the registered provider list:
openclaw provider list
The output should include the provider you configured, marked as active or available. If the provider still does not appear, repeat the installation and registration steps, paying particular attention to environment isolation issues.
Second, perform a minimal end-to-end test by invoking a simple LLM operation through OpenClaw:
openclaw llm generate --prompt "Hello, world" --model gpt-4
If the command initiates successfully and returns a response without throwing clw-llm-not-found, the provider resolution is functioning correctly. The absence of the error in the output and the presence of a non-zero LLM response confirm that the fix has taken effect.
Third, verify that your configuration file is being read correctly by running OpenClaw with explicit configuration validation:
openclaw config validate
This command parses the configuration file and reports any structural issues or references to unknown providers. A successful validation with no errors indicates that the configuration is well-formed and that all referenced providers are recognized.
Finally, for CI/CD pipelines or automated workflows, incorporate the provider list check into your verification process:
#!/bin/bash
PROVIDER_STATUS=$(openclaw provider list | grep -c "^openai\s")
if [ "$PROVIDER_STATUS" -ge 1 ]; then
echo "Provider verification passed"
exit 0
else
echo "Provider verification failed: clw-llm-not-found risk"
exit 1
fi
This script confirms that the provider is registered before proceeding with any downstream operations that depend on LLM availability.
5. Common Pitfalls
Several recurring mistakes lead to the clw-llm-not-found error persisting despite apparent fixes. Avoiding these pitfalls saves significant debugging time and ensures stable operation.
One of the most prevalent mistakes is mismatching provider package names and configuration names. The package installed via pip follows one naming convention, while the provider name registered with OpenClaw may follow another. Always use the canonical name returned by openclaw provider list, not an intuitive name derived from the package name.
Upgrading only one component of the OpenClaw ecosystem frequently creates silent incompatibilities. When updating OpenClaw core, always update provider packages in the same operation. Conversely, when adding a new provider package, verify that it is compatible with your current OpenClaw version by reviewing the package’s dependency specifications.
Environment confusion causes persistent issues in development workflows. Developers working across multiple projects often maintain several virtual environments, and running OpenClaw from the wrong environment yields the clw-llm-not-found error even when the packages are installed somewhere on the system. Always verify the active Python environment using which python and pip show openclaw before troubleshooting further.
Neglecting to set required environment variables for provider authentication is a related pitfall. Some providers require API keys or endpoint URLs to be available in the environment. While missing credentials typically produce different error codes, the provider resolution subsystem may fail before reaching the authentication stage, masking the true issue. Always double-check that all required environment variables are set and exported.
Finally, manually editing configuration files with incorrect YAML syntax introduces subtle errors that prevent proper provider loading. A misplaced indentation, an unrecognized key, or an unquoted string containing special characters can cause the entire configuration section to be ignored. Validate your YAML file using an online YAML validator or openclaw config validate before assuming that the provider name is incorrect.
6. Related Errors
clw-llm-timeout
The clw-llm-timeout error occurs when OpenClaw successfully resolves and connects to an LLM provider but fails to receive a response within the configured timeout period. Unlike clw-llm-not-found, which indicates a configuration or installation problem, clw-llm-timeout reflects a network or service availability issue. This error typically appears after the provider has been found and an API request has been dispatched, suggesting that the fix involves adjusting timeout settings, checking network connectivity, or investigating rate limiting on the provider’s side.
clw-llm-auth-failed
The clw-llm-auth-failed error indicates that OpenClaw successfully located the provider but failed to authenticate with it. This error surfaces after the provider resolution phase completes, meaning the provider is installed and registered correctly. The root cause usually involves invalid or expired API keys, missing environment variables, or misconfigured authentication endpoints. Resolving this error requires updating credentials, setting the correct environment variables, or regenerating API keys through the provider’s dashboard.
clw-config-invalid
The clw-config-invalid error signals that OpenClaw could not parse or validate the openclaw.yaml configuration file. While this error is distinct from clw-llm-not-found, misconfigured YAML files frequently cause provider resolution failures because the llm section may be ignored entirely during configuration loading. This error manifests during the earliest stages of OpenClaw startup, producing a detailed message about the specific YAML parsing failure. Fixing this error involves correcting the YAML syntax, removing unsupported configuration keys, or ensuring that the configuration file is properly encoded in UTF-8.