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Custom Models

Add custom providers and models (Ollama, vLLM, LM Studio, proxies) via ~/.pi/agent/models.json.

Table of Contents

Minimal Example

For local models (Ollama, LM Studio, vLLM), only id is required per model:

{
  "providers": {
    "ollama": {
      "baseUrl": "http://localhost:11434/v1",
      "api": "openai-completions",
      "apiKey": "ollama",
      "models": [
        { "id": "llama3.1:8b" },
        { "id": "qwen2.5-coder:7b" }
      ]
    }
  }
}

The apiKey is required but Ollama ignores it, so any value works.

Some OpenAI-compatible servers do not understand the developer role used for reasoning-capable models. For those providers, set compat.supportsDeveloperRole to false so pi sends the system prompt as a system message instead. If the server also does not support reasoning_effort, set compat.supportsReasoningEffort to false too.

You can set compat at the provider level to apply to all models, or at the model level to override a specific model. This commonly applies to Ollama, vLLM, SGLang, and similar OpenAI-compatible servers.

{
  "providers": {
    "ollama": {
      "baseUrl": "http://localhost:11434/v1",
      "api": "openai-completions",
      "apiKey": "ollama",
      "compat": {
        "supportsDeveloperRole": false,
        "supportsReasoningEffort": false
      },
      "models": [
        {
          "id": "gpt-oss:20b",
          "reasoning": true
        }
      ]
    }
  }
}

Full Example

Override defaults when you need specific values:

{
  "providers": {
    "ollama": {
      "baseUrl": "http://localhost:11434/v1",
      "api": "openai-completions",
      "apiKey": "ollama",
      "models": [
        {
          "id": "llama3.1:8b",
          "name": "Llama 3.1 8B (Local)",
          "reasoning": false,
          "input": ["text"],
          "contextWindow": 128000,
          "maxTokens": 32000,
          "cost": { "input": 0, "output": 0, "cacheRead": 0, "cacheWrite": 0 }
        }
      ]
    }
  }
}

The file reloads each time you open /model. Edit during session; no restart needed.

Google AI Studio Example

Use google-generative-ai with a baseUrl to add models from Google AI Studio, including custom Gemma 4 entries:

{
  "providers": {
    "my-google": {
      "baseUrl": "https://generativelanguage.googleapis.com/v1beta",
      "api": "google-generative-ai",
      "apiKey": "GEMINI_API_KEY",
      "models": [
        {
          "id": "gemma-4-31b-it",
          "name": "Gemma 4 31B",
          "input": ["text", "image"],
          "contextWindow": 262144,
          "reasoning": true
        }
      ]
    }
  }
}

The baseUrl is required when adding custom models to the google-generative-ai API type.

Supported APIs

API Description
openai-completions OpenAI Chat Completions (most compatible)
openai-responses OpenAI Responses API
anthropic-messages Anthropic Messages API
google-generative-ai Google Generative AI

Set api at provider level (default for all models) or model level (override per model).

Provider Configuration

Field Description
baseUrl API endpoint URL
api API type (see above)
apiKey API key (see value resolution below)
headers Custom headers (see value resolution below)
authHeader Set true to add Authorization: Bearer <apiKey> automatically
models Array of model configurations
modelOverrides Per-model overrides for built-in models on this provider

Value Resolution

The apiKey and headers fields support three formats:

  • Shell command: "!command" executes and uses stdout 
    "apiKey": "!security find-generic-password -ws 'anthropic'"
"apiKey": "!op read 'op://vault/item/credential'"
  • Environment variable: Uses the value of the named variable 
    "apiKey": "MY_API_KEY"
  • Literal value: Used directly 
    "apiKey": "sk-..."

For models.json, shell commands are resolved at request time. pi intentionally does not apply built-in TTL, stale reuse, or recovery logic for arbitrary commands. Different commands need different caching and failure strategies, and pi cannot infer the right one.

If your command is slow, expensive, rate-limited, or should keep using a previous value on transient failures, wrap it in your own script or command that implements the caching or TTL behavior you want.

/model availability checks use configured auth presence and do not execute shell commands.

Custom Headers

{
  "providers": {
    "custom-proxy": {
      "baseUrl": "https://proxy.example.com/v1",
      "apiKey": "MY_API_KEY",
      "api": "anthropic-messages",
      "headers": {
        "x-portkey-api-key": "PORTKEY_API_KEY",
        "x-secret": "!op read 'op://vault/item/secret'"
      },
      "models": [...]
    }
  }
}

Model Configuration

Field Required Default Description
id Yes — Model identifier (passed to the API)
name No id Human-readable model label. Used for matching (--model patterns) and shown in model details/status text.
api No provider's api Override provider's API for this model
reasoning No false Supports extended thinking
input No ["text"] Input types: ["text"] or ["text", "image"]
contextWindow No 128000 Context window size in tokens
maxTokens No 16384 Maximum output tokens
cost No all zeros {"input": 0, "output": 0, "cacheRead": 0, "cacheWrite": 0} (per million tokens)
compat No provider compat Provider compatibility overrides. Merged with provider-level compat when both are set.

Current behavior:

  • /model and --list-models list entries by model id.
  • The configured name is used for model matching and detail/status text.

Overriding Built-in Providers

Route a built-in provider through a proxy without redefining models:

{
  "providers": {
    "anthropic": {
      "baseUrl": "https://my-proxy.example.com/v1"
    }
  }
}

All built-in Anthropic models remain available. Existing OAuth or API key auth continues to work.

To merge custom models into a built-in provider, include the models array:

{
  "providers": {
    "anthropic": {
      "baseUrl": "https://my-proxy.example.com/v1",
      "apiKey": "ANTHROPIC_API_KEY",
      "api": "anthropic-messages",
      "models": [...]
    }
  }
}

Merge semantics:

  • Built-in models are kept.
  • Custom models are upserted by id within the provider.
  • If a custom model id matches a built-in model id, the custom model replaces that built-in model.
  • If a custom model id is new, it is added alongside built-in models.

Per-model Overrides

Use modelOverrides to customize specific built-in models without replacing the provider's full model list.

{
  "providers": {
    "openrouter": {
      "modelOverrides": {
        "anthropic/claude-sonnet-4": {
          "name": "Claude Sonnet 4 (Bedrock Route)",
          "compat": {
            "openRouterRouting": {
              "only": ["amazon-bedrock"]
            }
          }
        }
      }
    }
  }
}

modelOverrides supports these fields per model: name, reasoning, input, cost (partial), contextWindow, maxTokens, headers, compat.

Behavior notes:

  • modelOverrides are applied to built-in provider models.
  • Unknown model IDs are ignored.
  • You can combine provider-level baseUrl/headers with modelOverrides.
  • If models is also defined for a provider, custom models are merged after built-in overrides. A custom model with the same id replaces the overridden built-in model entry.

Anthropic Messages Compatibility

For providers or proxies using api: "anthropic-messages", use compat.supportsEagerToolInputStreaming to control Anthropic fine-grained tool streaming compatibility.

By default pi sends per-tool eager_input_streaming: true. If a proxy or Anthropic-compatible backend rejects that field, set supportsEagerToolInputStreaming to false. Pi will omit tools[].eager_input_streaming and send the legacy fine-grained-tool-streaming-2025-05-14 beta header for tool-enabled requests instead.

{
  "providers": {
    "anthropic-proxy": {
      "baseUrl": "https://proxy.example.com",
      "api": "anthropic-messages",
      "apiKey": "ANTHROPIC_PROXY_KEY",
      "compat": {
        "supportsEagerToolInputStreaming": false,
        "supportsLongCacheRetention": true
      },
      "models": [
        {
          "id": "claude-opus-4-7",
          "reasoning": true,
          "input": ["text", "image"]
        }
      ]
    }
  }
}
Field Description
supportsEagerToolInputStreaming Whether the provider accepts per-tool eager_input_streaming. Default: true. Set to false to omit that field and use the legacy fine-grained tool streaming beta header on tool-enabled requests.
supportsLongCacheRetention Whether the provider accepts Anthropic long cache retention (cache_control.ttl: "1h") when cache retention is long. Default: true.

OpenAI Compatibility

For providers with partial OpenAI compatibility, use the compat field.

  • Provider-level compat applies defaults to all models under that provider.
  • Model-level compat overrides provider-level values for that model.
{
  "providers": {
    "local-llm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "compat": {
        "supportsUsageInStreaming": false,
        "maxTokensField": "max_tokens"
      },
      "models": [...]
    }
  }
}
Field Description
supportsStore Provider supports store field
supportsDeveloperRole Use developer vs system role
supportsReasoningEffort Support for reasoning_effort parameter
reasoningEffortMap Map pi thinking levels to provider-specific reasoning_effort values
supportsUsageInStreaming Supports stream_options: { include_usage: true } (default: true)
maxTokensField Use max_completion_tokens or max_tokens
requiresToolResultName Include name on tool result messages
requiresAssistantAfterToolResult Insert an assistant message before a user message after tool results
requiresThinkingAsText Convert thinking blocks to plain text
requiresReasoningContentOnAssistantMessages Include empty reasoning_content on all replayed assistant messages when reasoning is enabled
thinkingFormat Use reasoning_effort, deepseek, zai, qwen, or qwen-chat-template thinking parameters
cacheControlFormat Use Anthropic-style cache_control markers on the system prompt, last tool definition, and last user/assistant text content. Currently only anthropic is supported.
supportsStrictMode Include the strict field in tool definitions
supportsLongCacheRetention Whether the provider accepts long cache retention when cache retention is long: prompt_cache_retention: "24h" for OpenAI prompt caching, or cache_control.ttl: "1h" when cacheControlFormat is anthropic. Default: true.
openRouterRouting OpenRouter provider routing preferences. This object is sent as-is in the provider field of the OpenRouter API request.
vercelGatewayRouting Vercel AI Gateway routing config for provider selection (only, order)

qwen uses top-level enable_thinking. Use qwen-chat-template for local Qwen-compatible servers that require chat_template_kwargs.enable_thinking.

cacheControlFormat: "anthropic" is for OpenAI-compatible providers that expose Anthropic-style prompt caching through cache_control markers on text content and tool definitions.

Example:

{
  "providers": {
    "openrouter": {
      "baseUrl": "https://openrouter.ai/api/v1",
      "apiKey": "OPENROUTER_API_KEY",
      "api": "openai-completions",
      "models": [
        {
          "id": "openrouter/anthropic/claude-3.5-sonnet",
          "name": "OpenRouter Claude 3.5 Sonnet",
          "compat": {
            "openRouterRouting": {
              "allow_fallbacks": true,
              "require_parameters": false,
              "data_collection": "deny",
              "zdr": true,
              "enforce_distillable_text": false,
              "order": ["anthropic", "amazon-bedrock", "google-vertex"],
              "only": ["anthropic", "amazon-bedrock"],
              "ignore": ["gmicloud", "friendli"],
              "quantizations": ["fp16", "bf16"],
              "sort": {
                "by": "price",
                "partition": "model"
              },
              "max_price": {
                "prompt": 10,
                "completion": 20
              },
              "preferred_min_throughput": {
                "p50": 100,
                "p90": 50
              },
              "preferred_max_latency": {
                "p50": 1,
                "p90": 3,
                "p99": 5
              }
            }
          }
        }
      ]
    }
  }
}

Vercel AI Gateway example:

{
  "providers": {
    "vercel-ai-gateway": {
      "baseUrl": "https://ai-gateway.vercel.sh/v1",
      "apiKey": "AI_GATEWAY_API_KEY",
      "api": "openai-completions",
      "models": [
        {
          "id": "moonshotai/kimi-k2.5",
          "name": "Kimi K2.5 (Fireworks via Vercel)",
          "reasoning": true,
          "input": ["text", "image"],
          "cost": { "input": 0.6, "output": 3, "cacheRead": 0, "cacheWrite": 0 },
          "contextWindow": 262144,
          "maxTokens": 262144,
          "compat": {
            "vercelGatewayRouting": {
              "only": ["fireworks", "novita"],
              "order": ["fireworks", "novita"]
            }
          }
        }
      ]
    }
  }
}