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LLM (Large Language Model) providers handle text generation, reasoning, and structured output tasks in Cognee. You can choose from cloud providers like OpenAI and Anthropic, or run models locally with Ollama.
New to configuration?See the Setup Configuration Overview for the complete workflow:install extras → create .env → choose providers → handle pruning.

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Supported Providers

Cognee supports multiple LLM providers:
  • OpenAI — GPT models via OpenAI API (default)
  • Azure OpenAI — GPT models via Azure OpenAI Service
  • Google Gemini — Gemini models via Google AI
  • Anthropic — Claude models via Anthropic API
  • AWS Bedrock — Models available via AWS Bedrock
  • Groq — Fast inference via Groq API (via LiteLLM)
  • Ollama — Local models via Ollama
  • LM Studio — Local models via LM Studio
  • HuggingFace — Models via HuggingFace Inference API or Inference Endpoints
  • llama.cpp — Local models via llama-cpp-python (in-process or server mode)
  • Custom — OpenAI-compatible endpoints (like vLLM, OpenRouter, DeepInfra, company-internal)
  • MCP Sampling — Reuse the host harness’s LLM via MCP sampling/createMessage (no LLM_API_KEY; only when Cognee runs as an MCP server under a host that grants sampling)
Model names are not an allowlist. Any model reachable through an OpenAI-compatible endpoint can be configured — see Custom Providers for the generic path and its compatibility requirements.
LLM/Embedding Configuration: If you configure only LLM or only embeddings, the other defaults to OpenAI. Cognee rejects this mismatch up front — add() and remember() fail with ProviderConfigMismatchError before any ingestion work happens. Configure both LLM and embeddings, or keep a working OpenAI API key for the side you leave at its defaults — see LLM/Embedding Configuration.

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Choosing a Model

Cognee always uses two models together: an LLM for entity/relationship extraction and reasoning, and an embedding model for semantic search. An embedding model is mandatory — every cognify run writes vectors to a vector store, and recall depends on them. If you only set one, the other silently falls back to OpenAI (see the warning above).
A small, fast model is the right default. Cognee ships with one (openai/gpt-5-mini) and the examples on this page use comparable light models such as gpt-4o-mini. Knowledge-graph extraction is many short, schema-constrained calls per document rather than a few long ones, so a light model keeps cost and latency low while handling most workloads well.Reach for a more powerful model when:
  • Your sources are dense or domain-specific (legal, medical, scientific) and you need higher-fidelity entities and relationships.
  • You use a custom graph model or ontology with a complex schema the model must populate accurately.
  • A light model produces noisy or incomplete graphs on your data.
Extraction relies on structured output, so very small or weak models may return malformed JSON or lower-quality graphs. If a small local model struggles, try a stronger one or adjust the instructor mode.
The embedding model is lightweight — defaults like nomic-embed-text (Ollama) or all-MiniLM-L6-v2 (Fastembed, CPU-only) run comfortably on a CPU or a small GPU and rarely dominate resource use.The LLM is the constraint for local setups. The Local Setup guide defaults to an 8B model (llama3.1:8b); as a rough guide, an 8B model quantized to 4-bit needs roughly 6 GB of free VRAM, while larger or less-quantized models need proportionally more. If a model does not fit, it spills to system RAM and CPU, which still works but is much slower — for llama.cpp you can tune LLAMA_CPP_N_GPU_LAYERS to offload only as many layers as fit. Limited VRAM does not change graph quality; it mainly affects how fast cognify runs, since extraction issues many sequential LLM calls per document. With low VRAM, prefer a smaller LLM and lower EMBEDDING_BATCH_SIZE (see Embedding Providers) over a large model that does not fit.

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Configuration

Set these environment variables in your .env file:
  • LLM_PROVIDER — The provider to use: openai, azure, anthropic, gemini, mistral, bedrock, ollama, llama_cpp, custom, mcp-sampling. Optional — when it is unset, Cognee infers it from the LLM_MODEL prefix (see the note below)
  • LLM_MODEL — The specific model to use
  • LLM_API_KEY — Your API key for the provider (not used by mcp-sampling)
  • LLM_ENDPOINT — Custom endpoint URL (for Azure, Ollama, or custom providers)
  • LLM_API_VERSION — API version (for Azure OpenAI)
  • LLM_TEMPERATURE — Sampling temperature, sent with every LLM call when you set it explicitly — and, on local inference servers (ollama, llama_cpp, LM Studio), also when you don’t: there an unset value sends 0.0. On every other provider, leaving it unset sends no temperature at all and the provider’s own default applies (see Temperature and Seed)
  • LLM_SEED — Sampling seed for reproducible outputs, sent when set (provider support varies)
  • LLM_MAX_COMPLETION_TOKENS — Maximum tokens per request (optional)
  • LLM_INSTRUCTOR_MODE — Structured-output mode override for Instructor-backed LLM calls (optional)
  • LLM_EXTRACTION_*, LLM_SUMMARIZATION_*, LLM_QUERY_* — Optional per-stage overrides that route individual pipeline stages to different models/providers (see Per-Stage Model Routing)
A preflight LLM connection test can time out at 30s, especially against smaller models. Workaround: add COGNEE_SKIP_CONNECTION_TEST=true to your .env.
Why do model names have a prefix like gemini/ or openrouter/?Cognee routes all LLM requests through LiteLLM, which uses provider prefixes to identify the correct API endpoint. For example, Google lists their model as gemini-2.0-flash, but in Cognee you must write gemini/gemini-2.0-flash. This prefix tells LiteLLM to use the Gemini API. The same applies to custom providers — openrouter/, hosted_vllm/, lm_studio/, etc. See each provider section below for the correct format.

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Provider Inference

LLM_PROVIDER is optional. When you don’t set it, Cognee infers the provider from the prefix of LLM_MODEL — anthropic/claude-3-5-sonnet resolves to anthropic, gemini/gemini-2.0-flash to gemini, and so on. Precedence, highest first:
  1. An explicit llm_provider passed in Python (for example to cognee.config.set_llm_config()).
  2. The LLM_PROVIDER environment variable.
  3. Inference from the LLM_MODEL prefix.
An explicitly set provider always wins, even when it disagrees with the model prefix — that is what makes the Azure recipe below (LLM_PROVIDER="openai" with LLM_MODEL="azure/gpt-4o-mini") keep working. A model id with no / (for example llama3.1:8b) has no prefix to infer from, so it falls back to the default openai unless you set LLM_PROVIDER yourself. Inference only recognises prefixes Cognee has an adapter for: openai, azure, anthropic, gemini, mistral, bedrock, ollama, llama_cpp, custom, mcp-sampling. Inference resolves LLM_PROVIDER from the model name. The opposite direction also exists, but only on the litellm_native structured output backend: once the provider is known, a model id LiteLLM cannot route on its own — a bare llama3.1:8b, or a namespaced library/phi4 — is re-qualified with that provider’s prefix before the request is sent. See Model-name qualification for which providers are prefixed.
Inference runs once, when the configuration is loaded from the environment. Setting llm_model later in Python via cognee.config.set_llm_config() does not re-trigger it — the provider stays whatever it already was (the default openai unless configured otherwise). When you configure the model in Python instead of through LLM_MODEL, set llm_provider explicitly in the same call.
Any other prefix raises ProviderNotDeducibleError — Cognee refuses to guess rather than silently falling back to OpenAI. This includes LiteLLM-routed prefixes Cognee has no adapter of its own for, such as openrouter/, groq/, or deepseek/. For those, set LLM_PROVIDER="custom", as every recipe in Custom Providers already does — with custom, the prefix is passed straight through to LiteLLM for routing.

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Provider Setup Guides

OpenAI is the default provider and works out of the box with minimal configuration.
Use Azure OpenAI Service with your own deployment.
Cognee routes Gemini requests through LiteLLM. There are two ways to reach Gemini models: the Google AI Studio API (a single API key) or Vertex AI (Google Cloud project + service-account credentials).
The simplest setup. Get an API key from Google AI Studio and use the gemini/ model prefix.
This path talks to the Gemini REST API directly and needs no extra Google packages.
Use Anthropic’s Claude models for reasoning tasks.
Groq provides fast inference for open models. Cognee routes Groq requests through LiteLLM using the groq/ model prefix.
Installation: Install the Groq dependency:
Popular Groq models (use with the groq/ prefix):
  • groq/llama-3.3-70b-versatile
  • groq/llama3-8b-8192
  • groq/mixtral-8x7b-32768
  • groq/gemma2-9b-it
See the Groq model list for all available models. Your Groq API key can be created in the Groq Console.
No endpoint needed: The LLM_ENDPOINT variable is not required for Groq — LiteLLM resolves the Groq API endpoint automatically from the groq/ prefix.
Use models available on AWS Bedrock for various tasks. For Bedrock specifically, you will need to also specify some information regarding AWS.
There are multiple ways of connecting to Bedrock models. Cognee picks the first one it finds, in this order:
  1. Using an API key and region. Simply generate your key on AWS, and put it in the LLM_API_KEY env variable. If LLM_API_KEY is set, it takes precedence over the credential and profile methods below, so leave it empty when you want to use those.
  2. Using AWS Credentials. You can only specify AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY, no need for the LLM_API_KEY. In this case, if you are using temporary credentials (e.g. AWS_ACCESS_KEY_ID starting with ASIA..., such as those issued by aws sso login or aws sts assume-role), then you also must specify the AWS_SESSION_TOKEN. All three values expire and must be refreshed when AWS rotates them.
  3. Using AWS profiles. AWS_PROFILE_NAME is the name of a profile (for example default or my-sso-profile), not a path to a file or to a folder. Cognee hands the name to boto3, which resolves the credentials through the standard AWS chain using the shared config and credentials files at ~/.aws/config and ~/.aws/credentials (override their locations with the AWS_CONFIG_FILE and AWS_SHARED_CREDENTIALS_FILE env variables). This is the recommended path for AWS SSO: run aws sso login --profile <your_aws_profile_name> first, then set AWS_PROFILE_NAME to that profile name and Cognee will use the temporary SSO credentials boto3 caches for it — no need to copy the ASIA... keys into your .env.
  4. Using an ambient IAM role. If LLM_API_KEY, the AWS_ACCESS_KEY_ID/AWS_SECRET_ACCESS_KEY pair, and AWS_PROFILE_NAME are all unset, Cognee passes no credentials to Bedrock and boto3 resolves them from the default AWS credential chain — an EC2 instance profile, ECS task role, or EKS IRSA service-account role. Unlike most providers, bedrock does not require LLM_API_KEY, so no missing-key error is raised; LLM_PROVIDER, LLM_MODEL, and AWS_REGION are enough to authenticate to Bedrock (an embedding provider still needs its own configuration). Since resolution is first-match, remove any stale key, credential, or profile values from your .env and shell — they take precedence over the role.
Installation: Install the required dependency:
Model Name The name of the model might differ based on the region (the name begins with eu for Europe, us of USA, etc.)
See the AWS Bedrock Integration guide for the full setup walkthrough.
Run models locally with Ollama for privacy and cost control.
LLM_API_KEY="ollama" is a placeholder required by the client library — Ollama itself does not validate it.Installation: Install Ollama from ollama.ai and pull your desired model:
Namespaced tags and Hugging Face GGUFs: Ollama model names are not always bare tags — they can be namespaced (library/phi4), and a GGUF pulled from Hugging Face keeps its full path. Both work as LLM_MODEL on the litellm_native structured output backend, which qualifies them to ollama/… for routing:
Setting LLM_PROVIDER="ollama" is required here, not optional: library and hf.co are not prefixes provider inference recognises, so leaving LLM_PROVIDER unset raises ProviderNotDeducibleError at configuration load.
Zero-API-key setup: To avoid falling back to OpenAI for embeddings, you must also configure the embedding provider to use a local backend. See the Local Setup guide for a complete .env example using Ollama or Fastembed for both LLM and embeddings.

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Model Support Warning

When LLM_PROVIDER="ollama", Cognee classifies the configured LLM_MODEL against a built-in support matrix as the LLM configuration is loaded, and logs a warning for models it has not validated for structured graph extraction. The check is advisory only — nothing is blocked and no exception is raised, so cognify() runs either way. The line appears the first time the LLM configuration is loaded in the process.Matching ignores an ollama/ prefix and everything after the : in a tag, so llama3.1, llama3.1:8b, and ollama/llama3.1:70b are all classified as llama3.1. qwen2.5 is the exception: its tag is parsed for a parameter count, and that count is what decides between recommended and problematic. A custom Modelfile tag (see below) inherits the classification of its base name — llama3.1:8b-8k is still llama3.1.Each distinct LLM_MODEL value warns at most once per process, so you see the line once even though the configuration is read many times. The message points at docs/ollama_models.md in the repo, which carries a similar matrix plus troubleshooting notes.

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Known Issues

  • ValidationError on import (Missing: [...]): Cognee validates the LLM variables as an all-or-nothing group when LLM_PROVIDER="ollama" — if you set any of LLM_MODEL, LLM_ENDPOINT, or LLM_API_KEY, you must set all three. Setting only some raises Value error, You have set some but not all of the required environment variables for LLM usage. The check reads the resolved LLM configuration rather than the process environment, so values loaded from a .env file count exactly the same as exported environment variables. A blank or whitespace-only value counts as unset, and LLM_MODEL counts as set only if you actually supply it — leaving it at its default (openai/gpt-5-mini) does not satisfy the group, so setting just LLM_ENDPOINT and LLM_API_KEY fails with Missing: ['LLM_MODEL']. The embedding variables are no longer part of this check: EMBEDDING_PROVIDER, EMBEDDING_MODEL and EMBEDDING_DIMENSIONS are owned by the embedding configuration and can be set independently of one another. Setting them together is still the recommended configuration for a local Ollama setup, since Ollama embeddings need HUGGINGFACE_TOKENIZER for token counting and an explicit EMBEDDING_DIMENSIONS avoids a wrong auto-derived vector size:
    See Embedding Providers → Ollama for model-to-tokenizer mappings and how to find the right HUGGINGFACE_TOKENIZER value.
  • NoDataError with mixed providers: Using Ollama as LLM and OpenAI as embedding provider may fail with NoDataError. Workaround: configure both LLM and embeddings to the same local provider (see the local setup guide above).
  • Audio transcription is not supported: AudioLoader relies on a Whisper-compatible transcription endpoint. Cognee’s Ollama adapter does not provide one, so audio ingestion will fail when LLM_PROVIDER="ollama".

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Context Window (num_ctx) and Custom Modelfiles

If cognify() returns HTTP 500 errors while the same model answers fine when you run ollama run <model> in a terminal, the usual cause is context-window truncation, not a connection problem.Ollama’s default context window can be much smaller than Cognee’s extraction window. Cognee sizes extraction chunks from LLM_MAX_COMPLETION_TOKENS (default 16384) — up to roughly half that per chunk — so the entity-extraction prompts it sends can be far larger than a short terminal prompt. When a prompt exceeds num_ctx, Ollama may truncate it, the model can return malformed or empty structured output, and Instructor’s parse failure can surface as a 500.LLM_MODEL is just an Ollama model tag, so the fix is to point it at a model whose num_ctx is large enough. Create a custom Modelfile:
Build the tag and reference it in your .env:
A larger num_ctx uses more memory. If you can’t raise it, lower LLM_MAX_COMPLETION_TOKENS instead so Cognee builds smaller chunks that fit the model’s existing context window.

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Connection Troubleshooting

If you see cannot connect to host or connection refused errors, the most common causes are an unreachable endpoint, the wrong protocol, or a Docker networking mismatch.Default endpoint protocolOllama’s local server speaks plain HTTP, not HTTPS. Cognee does not add TLS by default — the protocol is determined entirely by the scheme in LLM_ENDPOINT and EMBEDDING_ENDPOINT. Use https:// only if you have placed Ollama behind a TLS-terminating reverse proxy (Caddy, nginx, Traefik, etc.). For a local Ollama setup, use:The Ollama embedding engine builds a secure SSL context for outgoing requests, but it is only applied when the endpoint URL uses https:// — plain HTTP requests are not upgraded.localhost vs host.docker.internalInside a Docker container, localhost refers to the container itself, not your host machine where Ollama is running. If Cognee runs in Docker and Ollama runs on the host, use host.docker.internal instead:
host.docker.internal is available on Docker Desktop (macOS/Windows) and on Linux when the host-gateway mapping is configured in docker-compose.yml. On Linux without that mapping, use --network host or the Docker bridge IP.Other common causes
  • Ollama not running: verify with curl http://localhost:11434/api/tags from the same machine and network namespace Cognee is running in.
  • Wrong port: the default Ollama port is 11434. If you started Ollama with OLLAMA_HOST=0.0.0.0:<port>, match that port in LLM_ENDPOINT.
  • Missing path suffix: the LLM endpoint must end in /v1 (OpenAI-compatible chat completions), and the embedding endpoint must end in /api/embed. Pointing either at the bare host (e.g. http://localhost:11434) will fail.
  • Bind address: Ollama binds to 127.0.0.1 by default. To accept connections from other machines or Docker containers via a LAN IP, start it with OLLAMA_HOST=0.0.0.0:11434.
  • Inside Docker Compose: if the LLM or embedding endpoint runs on your host machine, localhost inside the container points back to the container itself. Use host.docker.internal on Docker Desktop (macOS/Windows), or add a host-gateway mapping in docker-compose.yml on Linux.
    If the service runs in the same Compose project, use the Compose service name instead of localhost for any DB_HOST, VECTOR_DB_URL, or GRAPH_DATABASE_URL setting.
Use models from HuggingFace via the HuggingFace Inference API (serverless) or dedicated Inference Endpoints.
Installation: Install the HuggingFace extra to enable the HuggingFace tokenizer used for chunking:
Model names: Use the full HuggingFace model repo ID after the huggingface/ prefix (e.g., huggingface/mistralai/Mixtral-8x7B-Instruct-v0.1). Not all models on HuggingFace support the text generation inference API — check the model card for compatibility. The model is routed through LiteLLM.
Run models locally with LM Studio for privacy and cost control.
Installation: Install LM Studio from lmstudio.ai and download your desired model from LM Studio’s interface. Load your model, start the LM Studio server, and Cognee will be able to connect to it.
Set up instructor mode: LLM_INSTRUCTOR_MODE controls how Cognee asks the model for structured output. LM Studio models often work best with json_schema_mode. For more detail, see LLM Instructor Modes below and Structured Output Backends.

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Complete .env (LLM + embeddings on one LM Studio server)

Configure the embedding side too, or embeddings still default to OpenAI — succeeding silently against api.openai.com if OPENAI_API_KEY is set in your environment, or failing on the missing key. Load both a chat model and an embedding model in LM Studio, then point Cognee at the same base URL:
Both endpoints are the base URL ending in /v1 — not /v1/chat/completions or /v1/embeddings. LLM_API_KEY/EMBEDDING_API_KEY are placeholders LM Studio does not validate. Set EMBEDDING_DIMENSIONS to your embedding model’s real output size (768 for nomic-embed-text-v1.5), otherwise it falls back to 3072 and the first vector-store write fails with a shape mismatch.The embedding half can also be routed through LiteLLM with EMBEDDING_PROVIDER="custom" and an lm_studio/-prefixed model — see Embedding Providers → LM Studio and Valid EMBEDDING_PROVIDER values and endpoint URL forms for when to pick which.
Run models locally using llama-cpp-python for full offline inference.Cognee supports two setup modes:
  • Local mode — Load a .gguf model directly in-process
  • Server mode — Connect to a running llama-cpp-python server over HTTP
Installation: Install the required dependency:
Choosing a mode: Use local mode for the simplest setup with no separate server process. Use server mode if you want to share one model across multiple processes or run the model on another machine.
Load a GGUF model file directly. No server setup required.
GPU acceleration: Set LLAMA_CPP_N_GPU_LAYERS=-1 to offload all layers to GPU, or set a positive integer to offload a specific number of layers. Leave it at 0 for CPU-only inference.
Concurrency: In local in-process mode the model is loaded once and shared across calls. Because the underlying llama_cpp.Llama instance is not thread-safe, Cognee serializes concurrent structured-output calls (such as the per-chunk extraction that cognify() fans out) on that single instance. This means in-process requests are processed one at a time rather than in parallel; if you need parallel decoding, run a llama-cpp-python server and use Server Mode instead.
Connect to a running llama-cpp-python server. Start the server separately:
Then configure Cognee to connect to it:
Use any OpenAI-compatible endpoint — OpenRouter, vLLM, a company-internal gateway, or other services.
See Fallback Provider in Advanced Options for full details.Custom Provider Prefixes: When using LLM_PROVIDER="custom", you must include the correct provider prefix in your model name. Cognee forwards requests to LiteLLM, which uses these prefixes to route requests correctly.LLM_PROVIDER="custom" is required for these recipes, not just conventional: Cognee cannot infer a provider from a prefix it has no adapter for, so omitting the line raises ProviderNotDeducibleError. See Provider Inference.Common prefixes include:
  • hosted_vllm/ — vLLM servers
  • openrouter/ — OpenRouter
  • lm_studio/ — LM Studio
  • openai/ — OpenAI-compatible APIs
See the LiteLLM providers documentation for the full list of supported prefixes.Any OpenAI-compatible model works — the providers listed on this page are examples, not an allowlist. Cognee does not validate LLM_MODEL against a set of known models: the value is passed through to LiteLLM, which forwards the request to whatever LLM_ENDPOINT you configure. A model that was released after your Cognee version, or one served only from a private gateway, is configured with the same template shown at the top of this section.Compatibility requirements
  • OpenAI-compatible chat completions: the endpoint must expose a /v1/chat/completions route that accepts system and user messages. LLM_ENDPOINT is the base URL (usually ending in /v1).
  • Exact model id: whatever follows the LiteLLM prefix must be the id your server accepts — typically the id returned by the endpoint’s /v1/models.
  • Structured output: graph extraction requires the model to return JSON matching a schema. The custom provider defaults to the json_mode instructor mode; if extraction returns malformed JSON, try tool_call or json_schema_mode. Weaker models may produce lower-quality graphs even when the transport works — see Structured Output Backends.
  • An API key value: custom always sends one, so set LLM_API_KEY="." if your server does not authenticate.
  • Embeddings are separate: the model does not need to serve embeddings — configure those independently under Embedding Providers.
Token limits for unknown models: a model missing from LiteLLM’s registry has no known output limit, so your LLM_MAX_COMPLETION_TOKENS is used as-is — lower it if the model’s real output limit is smaller. See Max Completion Tokens under Advanced Options for how the ceiling is applied.
Below are examples for common providers and patterns:
Use DeepSeek’s models for reasoning and chat via their OpenAI-compatible API.
Get your API key from platform.deepseek.com. The deepseek/ prefix tells LiteLLM to route to the DeepSeek API.Popular DeepSeek models (use with the deepseek/ prefix):
  • deepseek/deepseek-chat — DeepSeek-V3 (general chat and instruction following)
  • deepseek/deepseek-reasoner — DeepSeek-R1 (chain-of-thought reasoning)
Structured output: DeepSeek’s API is OpenAI-compatible, so the default json_mode for custom providers works well. If you encounter issues with structured output, try setting LLM_INSTRUCTOR_MODE="tool_call".
Use Moonshot AI’s Kimi models via their OpenAI-compatible API.
Get your API key from platform.moonshot.cn. The moonshot/ prefix tells LiteLLM to route to the Moonshot AI API.Available Kimi models (use with the moonshot/ prefix):
  • moonshot/moonshot-v1-8k — 8k context window
  • moonshot/moonshot-v1-32k — 32k context window
  • moonshot/moonshot-v1-128k — 128k context window (for long documents)
Use OpenRouter to access hundreds of models from a single API endpoint.
Get your API key from openrouter.ai/keys. Browse all available models at openrouter.ai/models — prefix the model slug with openrouter/.Example models (use with the openrouter/ prefix):
  • openrouter/deepseek/deepseek-r1 — DeepSeek R1 via OpenRouter
  • openrouter/openai/gpt-4o-mini — GPT-4o Mini via OpenRouter
Model ids change — confirm before you copy. OpenRouter adds and retires models continuously, and the free (:free) tier rotates fastest of all, so a slug that worked last month may return a model-not-found error today. Check the live catalogue rather than trusting an example:
Embeddings need their own configuration. These variables set only the LLM. If you leave EMBEDDING_* untouched it stays on the OpenAI defaults, so the LLM connects fine and ingestion fails later at embedding time on a missing or invalid OpenAI key. OpenRouter serves embedding models too — see OpenRouter embeddings — or point EMBEDDING_* at OpenAI or a local provider.
Use DeepInfra to access open-source models via their OpenAI-compatible API.
Find your model name in the DeepInfra model catalog. The deepinfra/ prefix tells LiteLLM to route to DeepInfra.
Any internal LLM server that exposes an OpenAI-compatible REST API (e.g., a corporate vLLM deployment, internal TGI server, or private OpenRouter proxy) can be used with the custom provider.
The model prefix you use (openai/, hosted_vllm/, etc.) determines which LiteLLM adapter handles the request. For most OpenAI-compatible servers, openai/ works best. Set LLM_API_KEY to whatever bearer token your server requires (use . if no auth is needed).
Use vLLM for high-performance model serving with OpenAI-compatible API.
Example with Gemma:
Important: The hosted_vllm/ prefix is required for LiteLLM to correctly route requests to your vLLM server. The model name after the prefix should match the model ID returned by your vLLM server’s /v1/models endpoint.
To find the correct model name, see their documentation.
When Cognee runs as an MCP server (cognee-mcp) inside a host that grants the MCP sampling capability, LLM_PROVIDER="mcp-sampling" delegates completions to the host’s own model through sampling/createMessage. No LLM_API_KEY is required.
Preconditions: This provider only works while Cognee is running as an MCP server inside a host process that granted the sampling capability to the client. If Cognee is not running under such a host — or the host did not grant sampling — the adapter fails closed with MCPSamplingUnavailableError before issuing any request. Treat that error as a configuration/capability issue: set LLM_PROVIDER to a provider with credentials, or run inside a sampling-granting host.
Host support varies. Not every MCP host grants the sampling capability. For example, as of early 2026 Claude Code does not yet grant sampling (anthropics/claude-code#1785). Check your host’s MCP documentation.
Completions only. MCP sampling covers text completions — it does not provide embeddings, audio transcription, or image description. Because vector search needs embeddings, you must still configure an embedding provider (audio transcription returns nothing and image description raises NotImplementedError).Structured output. The MCP protocol returns free text only, so Cognee produces structured output by embedding the response model’s JSON Schema in the prompt and running a bounded validate/repair loop (up to 5 attempts) before raising an error. Plain-string responses pass through unchanged.Background tasks (such as the cognify tasks launched from within a request) inherit the host MCP session automatically via the SDK’s per-request context, so no changes to cognee-mcp server code are needed.

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Advanced Options

LLM configuration is read at runtime from your environment/.env — it is not stored per dataset. Changing LLM_PROVIDER / LLM_MODEL therefore works fine on top of a dataset you have already processed; nothing about the existing data blocks the switch.What is not affected. Already-processed data is left untouched: the entities and relationships in your graph store and the embeddings in your vector store are neither re-computed nor invalidated. Cognee does not re-run past extraction, and vectors depend on the embedding model, not the LLM, so recall over existing data keeps working.What is affected. The new LLM applies only to future work:
  • Subsequent cognify / memify runs — new data is extracted and summarized with the new model.
  • Query-time reasoning during search (e.g. GRAPH_COMPLETION) — answers are generated by the new model over the existing graph and vectors.
This means a graph can mix output from different LLMs: nodes written by the old model stay as-is, and only newly cognified data reflects the new one. If you want the whole dataset to reflect the new model’s extraction quality, re-process it: run cognify with incremental_loading=False to force a full reprocess, or empty the dataset, re-add the source data, and run cognify again. Simply re-running cognify is not enough — it skips already-processed data by default.
Changing the embedding model is different: existing vectors were written with the old embeddings and become inconsistent with new ones. To change embeddings on an existing dataset you must re-process it — run cognify with incremental_loading=False, or delete and re-add the data — and if the new model has a different EMBEDDING_DIMENSIONS, remove the existing vector collections first (e.g. with prune, which wipes all datasets). See Embedding Providers.
By default Cognee uses a single model — the base LLM_* settings — for every stage of the pipeline. You can optionally route individual stages to different models or providers by setting stage-specific env var groups. Because extraction runs once per chunk and typically dominates token spend, it is often worth routing a cheaper or local model there while keeping a stronger model for summarization and query-time reasoning.Stages and their env groupsEach group accepts the same fields as the base LLM_* config, with the stage name in place of the leading LLM:
  • LLM_<STAGE>_MODEL
  • LLM_<STAGE>_PROVIDER
  • LLM_<STAGE>_ENDPOINT
  • LLM_<STAGE>_API_KEY
  • LLM_<STAGE>_API_VERSION
Fallback to base config: any stage field you leave unset (empty or absent) falls back to the corresponding base LLM_* value, so you only set what you want to override. If you set no stage overrides at all, the effective config is identical to a single-model setup — default single-model behavior is unchanged.Only those five fields are overridden. Everything else (temperature, LLM_MAX_COMPLETION_TOKENS, instructor mode, the rate-limit settings) is inherited from the base config — there are no LLM_<STAGE>_* equivalents for them. One default is re-derived rather than inherited: unless you set LLM_RATE_LIMIT_REQUESTS yourself, a stage routed to a local inference server resolves the lower local RPM budget of 10 instead of the 60 derived for a cloud base provider — in the example below, extraction resolves 10 while summarization and query keep 60.Example — route extraction to a local Ollama model while summarization and query keep using OpenAI:
The stage-level budget shapes that stage’s resolved config only — the limiter that paces dispatch is built once, process-wide, from the base configuration, so if you route high-volume extraction to a local server, set LLM_RATE_LIMIT_REQUESTS explicitly to what that server can absorb. The re-derivation is also one-way: a local base provider resolves 10, and a stage routed from there to a cloud provider keeps 10 rather than returning to 60.
No SDK or pipeline call signatures change when you enable per-stage routing. Each stage transparently gets its own cached client derived from its effective config, so concurrent stages can use different models safely.
When using the Instructor structured-output framework (opt-in via STRUCTURED_OUTPUT_FRAMEWORK=instructor; the default is litellm_native), Cognee instructs the model to return structured data in a specific way. The LLM_INSTRUCTOR_MODE environment variable controls which strategy is used.Each provider has a built-in default that matches its API capabilities. Override it only when the default doesn’t work for your specific model.Available modes:Per-provider defaults (from source code):Example — override the mode:
Override the default only when the model you are using requires a different mode. For example, LM Studio models typically need json_schema_mode even though the custom provider defaults to json_mode.
Ollama’s default changed to json_schema_mode. It was previously json_mode, which sent response_format: {"type": "json_object"} — that asks for some JSON and passes the Pydantic schema to the model as prompt text only, so validity is checked after the fact. With json_schema_mode, the schema is sent as a decoder constraint that Ollama enforces, which cuts first-attempt validation failures and the resulting InstructorRetryException on local models (measured 2/5 → 5/5 valid first attempts with llama3.1:8b and max_retries=0).Ollama has supported JSON-schema structured outputs since 0.5. If you run an older Ollama, set the previous behavior back explicitly:
Control the randomness of LLM responses with the LLM_TEMPERATURE and LLM_SEED environment variables.Both are sent when you set them. If you leave LLM_TEMPERATURE unset, Cognee sends no temperature at all and the provider’s own default applies (OpenAI’s is 1.0) — it does not fall back to 0.0. Same for LLM_SEED. Local inference servers are the one exception for temperature; see the note below.
When to adjust: setting LLM_TEMPERATURE=0.0 is recommended for knowledge-graph extraction because it produces consistent, structured output; add LLM_SEED on top when you need runs to be reproducible. Raise the temperature only if you need more variety in generated text (e.g. conversational responses or creative summarisation).Under the hood, both values are merged into LLM_ARGS, the provider kwargs Cognee sends with each call. A temperature or seed key given directly in LLM_ARGS wins over LLM_TEMPERATURE / LLM_SEED, so existing LLM_ARGS='{"temperature": 0}' setups keep working unchanged.Deterministic default on local inference servers. When LLM_TEMPERATURE is not set explicitly, Ollama, llama.cpp, and LM Studio are sent temperature: 0.0 rather than nothing — they accept the field, and leaving it out meant extraction ran at whatever the model itself defaults to (1.0 for several Ollama models). vLLM is treated as a regular provider here and still gets no temperature when the variable is unset, as do all hosted providers. The precedence is unchanged, so both escape hatches still work: set LLM_TEMPERATURE to the value you want, or put a temperature key in LLM_ARGS to override the dedicated field entirely.
gpt-5 models — including the default openai/gpt-5-mini — reject any temperature other than their own default. Setting LLM_TEMPERATURE to something else (0.0, for instance) on a gpt-5 model makes every generation call fail. That restriction is why Cognee sends nothing on hosted providers unless you opt in: leave it unset, or set it only alongside a model that accepts custom temperatures. It does not apply to the local servers above, which is why they are exempt from that rule.
LLM_MAX_COMPLETION_TOKENS sets the maximum number of tokens an LLM call may generate per request (passed to the provider as max_tokens/max_completion_tokens).Observable impact:
  • Truncation. If extraction or summarisation responses are larger than this ceiling, the provider stops generating mid-response. With structured output this surfaces as malformed/incomplete JSON or, with some local models, HTTP 500 errors. Raise the value if you see truncated output.
  • Effective value is clamped. When the model is in LiteLLM’s model registry, Cognee uses min(model limit from LiteLLM's registry, LLM_MAX_COMPLETION_TOKENS). Setting it far above the registry limit has no effect — “higher” is not automatically “better”.
  • Chunk size, cost and latency. Extraction chunks are sized as min(EMBEDDING_MAX_COMPLETION_TOKENS, LLM_MAX_COMPLETION_TOKENS // 2) — so this value also caps how much text goes into each cognify chunk. A larger value means fewer, larger chunks (fewer LLM calls but more tokens per call); a smaller value means more, smaller chunks (more calls, finer-grained extraction). See Chunkers for how chunk size shapes the graph.
Tuning guidance: the default 16384 is a good starting point for cloud models. Lower it for local models with a small context window so chunks fit (see the Ollama num_ctx note). Raise it only if your model supports a larger output window and you observe truncated extraction.
Control client-side throttling for LLM calls to manage API usage and costs.The limiter starts off, but switches itself on when your provider shows signs of overload. Cognee dispatches at full speed until a request comes back rate limited, times out, or returns HTTP 429/503/529 — then it logs one warning and paces every dispatch with the RPM budget below for a 15-minute cooldown. Set LLM_RATE_LIMIT_ENABLED="true" to pace from the first request instead, or AUTO_RATE_LIMIT="false" to stay unbounded no matter what the provider reports.Defaults:The cloud defaults (60 requests / 60 seconds) allow 1 request/second on average. Adjust both values to match your provider’s tier limit.Lower default budget for local inference servers. When LLM_RATE_LIMIT_REQUESTS is not set explicitly, Ollama, llama.cpp, and LM Studio get 10 requests per interval instead of 60 — they process requests near-serially, so the cloud default would still flood them. vLLM is treated as a regular provider: continuous batching absorbs concurrency like a cloud endpoint, so it keeps the 60 default. An explicitly configured LLM_RATE_LIMIT_REQUESTS always wins, whatever the provider.How auto rate limiting behaves:
  • What counts as evidence: a rate-limit error, a timeout (how overwhelmed local servers usually surface — they never send rate limits), or HTTP 429, 503, or 529. Evidence is looked for through the whole exception cause chain, so a provider error wrapped by Instructor still counts. Slow responses on their own are not evidence.
  • Cooldown window: the limiter stays engaged for 900 seconds (15 minutes). Further evidence inside the window extends it; once the window lapses quietly, behavior returns to whatever you configured.
  • One warning per episode: the first piece of evidence in an episode logs a warning naming the cause and the budget being applied; evidence inside an active window extends it silently. A fresh episode after a quiet cooldown warns again.
  • Opting out: AUTO_RATE_LIMIT="false" disables the automatic engagement entirely. LLM_RATE_LIMIT_ENABLED="true" keeps the limiter on from the start, independent of the auto behavior.
How it works:
  • Client-side limiter: Cognee paces outbound LLM calls before they reach the provider
  • Moving window: Spreads allowance across the time window for smoother throughput
  • Per-process scope: In-memory limits don’t share across multiple processes/containers
  • Retries are paced too: adapters enter the limiter inside their retry loop, so retried attempts are throttled alongside first attempts
  • Auto-applied: Works with all providers (OpenAI, Gemini, Anthropic, Ollama, Custom)
Sizing guidance:Set LLM_RATE_LIMIT_REQUESTS to your provider’s RPM (requests per minute) limit, and LLM_RATE_LIMIT_INTERVAL to 60. To leave headroom, use ~80–90% of the advertised limit. Check your provider’s dashboard for your current tier limits.Each cognify() call issues multiple LLM requests (entity extraction, summarization, etc.) per document chunk — plan for several requests per chunk, not one.Example configurations for common provider tiersThese examples target chat/completions-style LLM endpoints, such as OpenAI models like gpt-4o-mini.
Always verify your exact tier limits in your provider’s dashboard — limits vary by model, tier, and region. The examples above are approximations for common tiers and may change.
Cognee supports a primary-plus-fallback model configuration that automatically retries a failed request against a secondary provider. This is useful when your primary provider may reject certain content, and you want a fallback to handle those cases gracefully.When the fallback triggersThe fallback is invoked only on content policy violations from the primary provider:
  • ContentFilterFinishReasonError — the provider’s output filter blocked the response
  • ContentPolicyViolationError — the request was rejected for policy reasons
  • InstructorRetryException containing “content management policy”
The fallback does not activate for network errors, rate limits, or authentication failures.Supported providersFallback is available when LLM_PROVIDER is set to openai or custom. Other providers (Anthropic, Gemini, Mistral, Bedrock, Ollama) do not currently support the fallback chain.ConfigurationSet these three variables alongside your primary LLM configuration:
For LLM_PROVIDER="custom", all three fallback variables (FALLBACK_MODEL, FALLBACK_ENDPOINT, FALLBACK_API_KEY) must be set. If any is missing, Cognee raises a ContentPolicyFilterError instead of falling back.For LLM_PROVIDER="openai", only FALLBACK_MODEL and FALLBACK_API_KEY are required. If set, FALLBACK_ENDPOINT is now forwarded to the OpenAI adapter and routes the fallback request to that base URL; if omitted, the fallback request uses the default OpenAI endpoint.Variable reference
Structured-output LLM calls (acreate_structured_output, used internally for entity extraction, summarization, and other graph-building steps) are wrapped in a shared retry policy that retries transient failures with exponential backoff.How long a failing call persistsA call is allowed to give up only once both of these floors are met:Because both conditions must hold, a call against an unstable or rate-limited provider can keep retrying for up to a few minutes before it finally errors out. Backoff between attempts is exponential with jitter (starting around 8 seconds, capped near 128 seconds).
These floors are internal defaults shared across the OpenAI, Azure OpenAI, Anthropic, Gemini, Mistral, Ollama, Llama.cpp, Custom, and BAML structured-output paths. They are not environment-configurable.
Bedrock uses a separate retry path: its structured-output adapter relies on the Bedrock rate-limit/sleep retry wrapper and Instructor’s Bedrock retry setting instead of the shared ~240s retry floor.Some errors are treated as non-transient and are not retried — they fail immediately: authentication errors, model-not-found errors, cancellations, payment/budget exhaustion, and quota/billing exhaustion. That includes the shared retry paths for OpenAI, Azure OpenAI, Anthropic, Gemini, Mistral, Ollama, Llama.cpp, Custom, and BAML, so interrupted jobs and worker shutdowns stop promptly rather than waiting out the backoff window. Bedrock uses a separate retry path, but cancellations still unwind immediately there as well.Quota / billing exhaustion is terminal. When a provider reports that its quota or billing limit is exhausted, retrying cannot help, so the call fails fast instead of spinning through the retry window. The raw provider error is converted at the single acreate_structured_output choke point into an actionable LLMQuotaExceededError (provider- and framework-agnostic). The following provider wordings are classified as terminal:
Transient per-minute rate limits stay retryable. The patterns above are deliberately narrow: the bare phrase “exceeded your current quota” is intentionally not matched, because Gemini free tier uses it for recoverable RESOURCE_EXHAUSTED limits (OpenAI’s terminal case is still caught via insufficient_quota). Monitoring and alerting should treat LLMQuotaExceededError as a terminal condition and respond by checking the provider billing/quota dashboard, raising the limit, or switching credentials — not by retrying.
Operational note: when a provider is flaky, expect higher tail latency and additional API calls (and therefore cost) while retries play out. This persistent retry improves resilience for transient failures but does not mask genuine misconfiguration such as a bad API key.
LLM_ENDPOINT overrides the base URL Cognee uses to reach the LLM. Use it to point at an Azure deployment, a local server, an OpenAI-compatible proxy, or a company-internal gateway. For routing all outbound traffic through a corporate HTTP proxy without rewriting the endpoint, use the standard HTTPS_PROXY / HTTP_PROXY environment variables.Per-provider LLM_ENDPOINT semanticsRouting through a corporate HTTP/HTTPS proxyCognee’s LLM transport is built on the openai, anthropic, httpx, and litellm Python clients, all of which honor the standard proxy environment variables. Set them in your shell or .env before starting Cognee:
This is the right approach when the LLM provider’s public URL is correct but your network blocks direct egress. No Cognee config change is needed — outbound LLM, embedding, and HTTP loader calls all pick up these variables automatically.Troubleshooting “not connected / cannot reach LLM”
  • LLM_ENDPOINT typos — values are stripped of surrounding quotes, but a missing scheme (http:// / https://) or trailing path segment will surface as a connection error. For OpenAI-compatible endpoints, the URL must end in /v1 (or whatever the server exposes).
  • Preflight timeout — Cognee runs a 30s connection test at startup. If your proxy adds latency or your local model is slow to warm up, set COGNEE_SKIP_CONNECTION_TEST=true to skip it.
  • Provider mismatch — if LLM_ENDPOINT points at a non-OpenAI server but LLM_PROVIDER="openai", Cognee will hit the wrong route. For OpenAI-compatible third parties, use LLM_PROVIDER="custom" with the correct LiteLLM model prefix (see Custom Providers above).
  • TLS interception — if your corporate proxy uses its own CA, set SSL_CERT_FILE or REQUESTS_CA_BUNDLE to the CA bundle path so Python’s HTTP clients trust the proxy certificate.

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Notes

  • If EMBEDDING_API_KEY is not set, Cognee falls back to LLM_API_KEY for embeddings
  • Rate limiting helps manage API usage and costs
  • Structured output frameworks ensure consistent data extraction from LLM responses

Embedding Providers

Configure embedding providers for semantic search

Overview

Return to setup configuration overview

Relational Databases

Set up SQLite or Postgres for metadata storage