> ## Documentation Index
> Fetch the complete documentation index at: https://docs.cognee.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# config
> Configure LLM providers, databases, chunking, and more
# cognee.config
Static class for configuring Cognee's runtime settings. All setters persist for the duration of the process (or until overridden). Use `cognee.config.set(...)` for supported runtime-safe settings inside a Python process.
Cognee can also be configured through `.env` or process environment variables before import. Use those for process-level settings such as auth, logging, cache backend, storage backend, telemetry, API server settings, and deployment credentials.
For the full environment variable reference and precedence rules, see [Setup Configuration](/setup-configuration/overview).
## Configuration Types
```python theme={null}
cognee.config.set_llm_provider("openai") # "openai", "anthropic", "ollama", "gemini", "mistral", "bedrock"
cognee.config.set_llm_model("gpt-4o-mini")
cognee.config.set_llm_api_key("sk-...")
cognee.config.set_llm_endpoint("https://custom-endpoint.example.com")
# Or set all at once — keys must match LLMConfig attribute names exactly
cognee.config.set_llm_config({
"llm_provider": "openai",
"llm_model": "gpt-4o",
"llm_api_key": "sk-...",
})
```
`set_llm_config()` keys must match the internal attribute names on `LLMConfig`. The exact internal attributes name are displayed in the table below.
| Key | Type | Default | Description |
| ----------------------------- | ------- | --------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `structured_output_framework` | `str` | `"litellm_native"` | Structured output backend: `"litellm_native"`, `"instructor"`, or `"baml"`. See [Structured Output Backends](/setup-configuration/structured-output-backends) |
| `llm_instructor_mode` | `str` | `""` | Instructor `Mode` string (e.g. `"json_schema_mode"`, `"json_mode"`, `"tool_call"`). Empty = provider default |
| `llm_provider` | `str` | `"openai"` | Provider: `"openai"`, `"anthropic"`, `"ollama"`, `"gemini"`, `"mistral"`, `"bedrock"`, `"azure"`, `"custom"` |
| `llm_model` | `str` | `"openai/gpt-5-mini"` | Model identifier |
| `llm_api_key` | `str` | `None` | API key for the provider |
| `llm_endpoint` | `str` | `""` | Custom endpoint URL (required for Ollama, vLLM, etc.) |
| `llm_api_version` | `str` | `None` | API version (required for Azure) |
| `llm_temperature` | `float` | unset | Response temperature (0.0–2.0). Takes effect via the `LLM_TEMPERATURE` env var **when you set it explicitly** — leave it unset and nothing is sent, so the provider's default applies rather than `0.0`. Local inference servers (`ollama`, `llama_cpp`, and LM Studio models, identified by an `lm_studio/` prefix) are the exception: there an unset value is folded in as `0.0`. Setting it through `set_llm_config()` has no effect; at runtime use `llm_args` instead. gpt-5 models (including the default `openai/gpt-5-mini`) reject any temperature but their own default |
| `llm_seed` | `int` | `None` | Sampling seed for reproducible outputs. Takes effect via the `LLM_SEED` env var; setting it through `set_llm_config()` has no effect — at runtime use `llm_args` instead. Provider support varies |
| `llm_streaming` | `bool` | `False` | Enable streaming responses |
| `llm_max_completion_tokens` | `int` | `16384` | Maximum tokens in the response |
| `llm_args` | `dict` | `{}` | Arbitrary provider-specific kwargs merged into every LLM call. Set as a JSON string in `.env` (e.g. `LLM_ARGS='{"top_p": 0.9}'`). The `LLM_TEMPERATURE` and `LLM_SEED` env vars are folded in here at startup, and a `temperature`/`seed` key set directly in `llm_args` takes precedence over them — this is also the key to use for temperature/seed with `set_llm_config()` |
| `llm_rate_limit_enabled` | `bool` | `False` | Enable client-side rate limiting for LLM calls |
| `llm_rate_limit_requests` | `int` | `60` | Max LLM requests allowed per interval |
| `llm_rate_limit_interval` | `int` | `60` | Duration of the rate limit window in seconds |
| `llm_rate_limit_tokens` | `int` | `0` | Max tokens per interval (`0` = disabled) |
```python theme={null}
cognee.config.set_embedding_provider("fastembed")
cognee.config.set_embedding_model("BAAI/bge-small-en-v1.5")
cognee.config.set_embedding_dimensions(384)
# Or set all at once — keys must match EmbeddingConfig attribute names exactly
cognee.config.set_embedding_config({
"embedding_provider": "fastembed",
"embedding_model": "BAAI/bge-small-en-v1.5",
"embedding_dimensions": 384,
})
```
| Key | Type | Default | Description |
| --------------------------------- | ------ | --------------------------------- | ------------------------------------------------------------------------------------------------- |
| `embedding_provider` | `str` | `"openai"` | Provider: `"openai"`, `"ollama"`, `"fastembed"`, `"gemini"`, `"mistral"`, `"bedrock"`, `"custom"` |
| `embedding_model` | `str` | `"openai/text-embedding-3-large"` | Model identifier |
| `embedding_dimensions` | `int` | `3072` | Vector dimension size (must match your vector store) |
| `embedding_api_key` | `str` | `None` | API key (falls back to `LLM_API_KEY` if unset) |
| `embedding_endpoint` | `str` | `None` | Custom endpoint URL |
| `embedding_api_version` | `str` | `None` | API version |
| `embedding_max_completion_tokens` | `int` | `8191` | Maximum tokens for embedding input |
| `embedding_batch_size` | `int` | `36` | Batch size for embedding requests |
| `huggingface_tokenizer` | `str` | `None` | HuggingFace Hub model ID for token counting with Ollama |
| `embedding_rate_limit_enabled` | `bool` | `False` | Enable client-side rate limiting for embedding calls |
| `embedding_rate_limit_requests` | `int` | `60` | Max embedding requests allowed per interval |
| `embedding_rate_limit_interval` | `int` | `60` | Duration of the rate limit window in seconds |
| `embedding_rate_limit_tokens` | `int` | `0` | Max tokens per interval (`0` = disabled) |
```python theme={null}
cognee.config.set_graph_database_provider("kuzu") # "kuzu", "neo4j", "kuzu-remote", "neptune", "neptune_analytics"
# Keys must match GraphConfig attribute names exactly
cognee.config.set_graph_db_config({
"graph_database_provider": "neo4j",
"graph_database_url": "bolt://localhost:7687",
"graph_database_username": "neo4j",
"graph_database_password": "password",
})
```
```python theme={null}
cognee.config.set_vector_db_provider("lancedb") # "lancedb", "pgvector", "qdrant", "chromadb"
cognee.config.set_vector_db_url("http://localhost:6333")
cognee.config.set_vector_db_key("your-key")
# Keys must match VectorConfig attribute names exactly
cognee.config.set_vector_db_config({
"vector_db_provider": "qdrant",
"vector_db_url": "http://localhost:6333",
"vector_db_key": "...",
})
```
```python theme={null}
cognee.config.set_chunk_size(1024)
cognee.config.set_chunk_overlap(128)
cognee.config.set_chunk_strategy("PARAGRAPH") # "EXACT", "PARAGRAPH", "SENTENCE", "CODE"
cognee.config.set_chunk_engine("DEFAULT_ENGINE") # "DEFAULT_ENGINE", "LANGCHAIN_ENGINE"
```
```python theme={null}
cognee.config.set_classification_model(MyClassifier)
cognee.config.set_summarization_model(MySummarizer)
cognee.config.set_graph_model(MyGraphModel)
```
```python theme={null}
cognee.config.system_root_directory("/custom/path")
cognee.config.data_root_directory("/data/path")
cognee.config.set_translation_provider("google") # "llm", "google", "azure"
cognee.config.set_translation_target_language("en")
# Generic setter for supported keys
cognee.config.set("llm_model", "openai/gpt-5-mini")
# Generic getters for the same keys
cognee.config.get("llm_model") # "openai/gpt-5-mini"
cognee.config.get_all() # dict of every known setting
```
### Reading configuration
`get(key)` returns the current value of any key `set()` accepts, and `get_all()` returns
all of them as a dict. Both mask secret values (`llm_api_key`, `embedding_api_key`,
`vector_db_key`) by default, keeping only the first three and last four characters as a
hint (values of eight characters or fewer are masked entirely); pass `reveal_secrets=True`
to get the raw value. `get()` raises
`InvalidConfigAttributeError` for an unrecognized key.
```python theme={null}
cognee.config.get("llm_api_key") # "sk-...c3d4"
cognee.config.get("llm_api_key", reveal_secrets=True) # "sk-proj-9f2a7b41c3d4"
cognee.config.get_all(reveal_secrets=True) # unmasked dict
```
### Persisting a value to `.env`
`set()` normally only updates the in-process configuration. Pass `persist=True` to also
write the resolved value into a `.env` file in the current working directory (created if
missing), so it is picked up by the next process started from that directory. This is
what `cognee-cli config set` does. When persisting, `set()` returns a dict describing
where the value was written:
```python theme={null}
cognee.config.set("llm_model", "openai/gpt-5-mini", persist=True)
# {"path": "/home/you/project/.env", "created": True, "env_var": "LLM_MODEL"}
```
Without `persist=True`, `set()` returns `None` and changes nothing on disk.
Persisting a secret writes it in plaintext to `.env` in whatever directory the process is
running from. Keep that file out of version control and restrict its permissions.
`cognee.config.set(key, value)` is not a free-form setter. Use it for supported runtime-safe settings such as LLMs, embeddings, graph/vector databases, chunking, model overrides, and root directories. Use `.env`, shell variables, deployment variables, or pre-import `os.environ` for process-level settings such as auth, logging, cache backend, storage backend, API server settings, telemetry, and cloud credentials.
## All Configuration Methods
| Method | Description |
| --------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------- |
| `set_llm_provider(provider)` | Set the LLM provider |
| `set_llm_model(model)` | Set the LLM model name |
| `set_llm_api_key(key)` | Set the LLM API key |
| `set_llm_endpoint(url)` | Set a custom LLM endpoint |
| `set_llm_config(dict)` | Set all LLM config at once |
| `set_embedding_provider(provider)` | Set the embedding provider |
| `set_embedding_model(model)` | Set the embedding model name |
| `set_embedding_dimensions(dimensions)` | Set embedding vector dimensions |
| `set_embedding_endpoint(url)` | Set a custom embedding endpoint |
| `set_embedding_api_key(key)` | Set the embedding API key |
| `set_embedding_config(dict)` | Set all embedding config at once |
| `set_graph_database_provider(provider)` | Set the graph DB provider |
| `set_relational_db_config(dict)` | Set relational DB config |
| `set_migration_db_config(dict)` | Set migration DB config |
| `set_graph_db_config(dict)` | Set all graph DB config |
| `set_vector_db_provider(provider)` | Set the vector DB provider |
| `set_vector_db_url(url)` | Set the vector DB URL |
| `set_vector_db_key(key)` | Set the vector DB API key |
| `set_vector_db_config(dict)` | Set all vector DB config |
| `set_chunk_size(size)` | Set chunk size in tokens |
| `set_chunk_overlap(overlap)` | Set chunk overlap |
| `set_chunk_strategy(strategy)` | Set chunking strategy |
| `set_chunk_engine(engine)` | Set chunking engine |
| `set_classification_model(model)` | Set classification model |
| `set_summarization_model(model)` | Set summarization model |
| `set_graph_model(model)` | Set graph extraction model |
| `system_root_directory(path)` | Set system root directory |
| `data_root_directory(path)` | Set data root directory |
| `monitoring_tool(tool)` | Set the monitoring tool |
| `set_translation_provider(provider)` | Set translation provider |
| `set_translation_target_language(lang)` | Set translation target language |
| `set_translation_config(dict)` | Set translation config |
| `set(key, value, persist=False)` | Generic config setter. With `persist=True`, also writes the value to `.env` in the current directory and returns `{"path", "created", "env_var"}` |
| `get(key, reveal_secrets=False)` | Generic config getter. Secret values are masked unless `reveal_secrets=True` |
| `get_all(reveal_secrets=False)` | Return every known setting as a dict. Secret values are masked unless `reveal_secrets=True` |