feat: selectable wire format — ollama (default), openai (vLLM etc.), anthropic

Port the multi-API client from llm.chat (llm.rs kept in lockstep):
the optional api input selects the dialect, default stays the
unchanged v0.1.x Ollama behavior. openai covers vLLM, LM Studio,
LiteLLM and cloud OpenAI; api-specific error hints; api_key sent
as Bearer (ollama/openai) or x-api-key (anthropic). model_digest
stays an Ollama-only best-effort probe and is documented as such.

Proven end-to-end against a hermetic OpenAI-wire fake (request
shape validated, response parsed) via a hub flow run.

Signed-off-by: flemming-it <sf@flemming.it>

Cargo.toml

@@ -5,7 +5,7 @@
 
 [package]
 name = "text_summarize"
-version = "0.1.0"
+version = "0.2.0"
 edition = "2024"
 license = "Apache-2.0"
 publish = false

MODULE.de.md

@@ -1,6 +1,6 @@
 # text.summarize
 
-Ollama-basierte treue Zusammenfassung. Sendet Quelltext an
+LLM-basierte treue Zusammenfassung (Ollama, vLLM, jeder OpenAI-kompatible Server oder Anthropic). Sendet Quelltext an
 einen konfigurierten LLM-Endpunkt mit einem Treue-vor-Kreativität-
 System-Prompt und liefert die Zusammenfassung plus
 audit-taugliche Modell-Herkunfts-Felder.
@@ -16,7 +16,8 @@ audit-taugliche Modell-Herkunfts-Felder.
 | `text`      | text | Quelltext.                                                                         |
 | `style`     | text | Optionaler Stil-Hinweis (z.B. `one paragraph`, `three bullet points`, `a tweet`). Default: `one paragraph`. |
 | `language`  | text | Optionaler Ziel-Sprache (z.B. `German`, `ja-JP`). Leer = Quellsprache.             |
-| `endpoint`  | text | Ollama-förmiger `/api/chat`-Endpunkt.                                              |
+| `endpoint`  | text | Chat-Endpunkt passend zum gewählten `api` (Ollama `/api/chat`, OpenAI-kompatibel `/v1/chat/completions` — vLLM u. a., Anthropic `/v1/messages`). |
+| `api`       | text | Optionales Wire-Format: `ollama` (Default), `openai`, `anthropic`. |
 | `model`     | text | Modell-ID am Endpunkt.                                                             |
 | `api_key`   | text | Optionaler Bearer-Token für Cloud-Endpunkte.                                       |
 
@@ -29,7 +30,7 @@ audit-taugliche Modell-Herkunfts-Felder.
 | `language`       | text | Echo des Sprach-Inputs.                                            |
 | `model_endpoint` | text | URL, gegen die zusammengefasst wurde.                              |
 | `model_name`     | text | Modell-ID wie an die LLM-API gesendet.                             |
-| `model_digest`   | text | SHA-256-Digest des bedienenden Ollama-Modells (wenn erreichbar).   |
+| `model_digest`   | text | SHA-256-Digest des bedienten Modells — nur bei Ollama, sonst leer. |
 
 ## Treue vor Kreativität
 

MODULE.md

@@ -1,6 +1,6 @@
 # text.summarize
 
-Ollama-backed faithful summarisation. Sends source text to a
+LLM-backed faithful summarisation. Sends source text to a
 configured LLM endpoint with a fidelity-over-creativity system
 prompt and emits the summary plus audit-grade
 model-provenance fields.
@@ -16,7 +16,8 @@ model-provenance fields.
 | `text`      | text | Source text to summarise.                                                    |
 | `style`     | text | Optional style hint (e.g. `one paragraph`, `three bullet points`, `a tweet`). Default: `one paragraph`. |
 | `language`  | text | Optional output-language hint (e.g. `German`, `ja-JP`). Empty = same as source. |
-| `endpoint`  | text | Ollama-shaped `/api/chat` endpoint URL.                                      |
+| `endpoint`  | text | Chat endpoint URL matching the selected `api` (Ollama `/api/chat`, OpenAI-compatible `/v1/chat/completions` — vLLM etc., Anthropic `/v1/messages`). |
+| `api`       | text | Optional wire format: `ollama` (default), `openai`, `anthropic`.             |
 | `model`     | text | Model identifier the endpoint serves.                                        |
 | `api_key`   | text | Optional bearer token for cloud-hosted endpoints.                            |
 
@@ -29,7 +30,7 @@ model-provenance fields.
 | `language`       | text | Echo of the input language.                                  |
 | `model_endpoint` | text | URL the summary was generated against.                       |
 | `model_name`     | text | Model identifier as supplied to the LLM API.                 |
-| `model_digest`   | text | SHA-256 digest of the served Ollama model (when reachable).  |
+| `model_digest`   | text | SHA-256 digest of the served model — Ollama only, empty elsewhere. |
 
 ## Fidelity over creativity
 

module.yaml

@@ -1,12 +1,12 @@
 schema_version: 3
 provider: chain
 name: text-summarize
-version: 0.1.0
+version: 0.2.0
 
 # Capability provided by this module.
 provides:
   - capability: text.summarize
-    version: 0.1.0
+    version: 0.2.0
 
 # Inputs the invoke function accepts.
 inputs:
@@ -38,8 +38,27 @@ inputs:
   endpoint:
     type: text
     description:
-      en: Ollama-shaped /api/chat endpoint.
+      en: |
-      de: Ollama-kompatibler /api/chat-Endpunkt.
+        Chat endpoint URL matching the selected api:
+        ollama "http://localhost:11434/api/chat",
+        openai "http://localhost:8000/v1/chat/completions"
+        (vLLM etc.), anthropic "https://.../v1/messages".
+      de: |
+        Chat-Endpunkt-URL passend zum gewählten api:
+        ollama "http://localhost:11434/api/chat",
+        openai "http://localhost:8000/v1/chat/completions"
+        (vLLM u. a.), anthropic "https://.../v1/messages".
+  api:
+    type: text
+    description:
+      en: |
+        Optional wire format: "ollama" (default), "openai"
+        (OpenAI-compatible servers such as vLLM or LM Studio),
+        or "anthropic" (Messages API). Empty = ollama.
+      de: |
+        Optionales Wire-Format: "ollama" (Default), "openai"
+        (OpenAI-kompatible Server wie vLLM oder LM Studio)
+        oder "anthropic" (Messages API). Leer = ollama.
   model:
     type: text
     description:
@@ -81,8 +100,14 @@ outputs:
   model_digest:
     type: text
     description:
-      en: SHA-256 digest of the served Ollama model (when reachable).
+      en: |
-      de: SHA-256-Digest des bedienten Ollama-Modells (wenn erreichbar).
+        SHA-256 digest of the served model, best-effort probe.
+        Ollama only — empty for other endpoints (OpenAI / vLLM /
+        Anthropic expose no digest API).
+      de: |
+        SHA-256-Digest des bedienten Modells (Best-Effort-Probe).
+        Nur bei Ollama — leer bei anderen Endpunkten (OpenAI /
+        vLLM / Anthropic bieten keine Digest-API).
 
 # Permissions required.
 permissions:

src/lib.rs

@@ -1,11 +1,15 @@
-//! `text.summarize` — Ollama-backed text summarisation.
+//! `text.summarize` — LLM-backed text summarisation.
 //!
-//! Sends `text` plus a length / style directive to an Ollama
+//! Sends `text` plus style/language directives to an LLM chat
-//! `/api/chat` endpoint and returns a summary. Reports
+//! endpoint and returns the summary. Reports `model_endpoint`,
-//! `model_endpoint`, `model_name`, and `model_digest` as audit
+//! `model_name`, and `model_digest` as audit fields, same as
-//! fields, same as `llm.chat` and `text.translate`.
+//! `llm.chat`.
 //!
-//! v0.1.0 targets Ollama only.
+//! The wire format is selected by the optional `api` input:
+//! `ollama` (default, unchanged v0.1.x behavior), `openai`
+//! (OpenAI-compatible `/v1/chat/completions` — vLLM, LM Studio,
+//! LiteLLM, cloud OpenAI), or `anthropic` (Messages API). The
+//! client logic in `llm.rs` is kept in lockstep with `llm.chat`.
 
 mod llm;
 
@@ -33,11 +37,15 @@ pub fn invoke(_ctx: Context, inputs: Inputs) -> Result<Outputs, ModuleError> {
         .get("language")
         .and_then(payload_text)
         .unwrap_or_default();
+    let api_raw = inputs.get("api").and_then(payload_text).unwrap_or_default();
+    let api =
+        crate::llm::Api::parse(&api_raw).map_err(|e| ModuleError::invalid_input(e.to_string()))?;
 
     let prompt = build_prompt(&style, &language, &text);
 
     let client = make_client();
     let params = crate::llm::ChatParams {
+        api,
         endpoint: &endpoint,
         model: &model,
         api_key: &api_key,
@@ -45,7 +53,7 @@ pub fn invoke(_ctx: Context, inputs: Inputs) -> Result<Outputs, ModuleError> {
         prompt: &prompt,
     };
     let result = crate::llm::chat_with_identity(&client, &params)
-        .map_err(|e| llm_error_to_module_error(e, &endpoint, &model))?;
+        .map_err(|e| llm_error_to_module_error(e, api, &endpoint, &model))?;
 
     Ok(Outputs::new()
         .with_text("summary", result.response)
@@ -58,27 +66,51 @@ pub fn invoke(_ctx: Context, inputs: Inputs) -> Result<Outputs, ModuleError> {
 
 /// Turn a transport/protocol error into a message that names the
 /// likely cause and the fix, instead of a raw `ConnectionRefused`.
-fn llm_error_to_module_error(e: crate::llm::LlmError, endpoint: &str, model: &str) -> ModuleError {
+/// The hints are api-specific: an Ollama connect failure almost
-    use crate::llm::LlmError;
+/// always means Ollama isn't running or the model isn't pulled,
-    match e {
+/// while cloud/vLLM failures are usually endpoint or key issues.
-        LlmError::Http(detail) => ModuleError::internal(format!(
+/// The message must never contain the api_key.
+fn llm_error_to_module_error(
+    e: crate::llm::LlmError,
+    api: crate::llm::Api,
+    endpoint: &str,
+    model: &str,
+) -> ModuleError {
+    use crate::llm::{Api, LlmError};
+    match (api, e) {
+        (Api::Ollama, LlmError::Http(detail)) => ModuleError::internal(format!(
             "LLM endpoint {endpoint} not reachable ({detail}). Is Ollama running? \
              Start it with `ollama serve`, then pull the model with `ollama pull {model}`. \
              If the LLM runs elsewhere, set the `endpoint` input to its /api URL."
         )),
-        LlmError::Status(404) => ModuleError::internal(format!(
+        (Api::Openai, LlmError::Http(detail)) => ModuleError::internal(format!(
+            "LLM endpoint {endpoint} not reachable ({detail}). Expected an \
+             OpenAI-compatible server (OpenAI, vLLM, ...) at a /v1/chat/completions URL."
+        )),
+        (Api::Anthropic, LlmError::Http(detail)) => ModuleError::internal(format!(
+            "LLM endpoint {endpoint} not reachable ({detail}). Expected the \
+             Anthropic Messages API at a /v1/messages URL."
+        )),
+        (Api::Ollama, LlmError::Status(404)) => ModuleError::internal(format!(
             "LLM endpoint {endpoint} returned 404 for model '{model}' — the model is \
              likely not pulled. Run `ollama pull {model}` (or check the model name)."
         )),
-        LlmError::Status(code) => ModuleError::internal(format!(
+        (_, LlmError::Status(401)) | (_, LlmError::Status(403)) => ModuleError::internal(format!(
+            "LLM endpoint {endpoint} rejected the request as unauthorized — check the \
+             `api_key` input."
+        )),
+        (_, LlmError::Status(code)) => ModuleError::internal(format!(
             "LLM endpoint {endpoint} returned HTTP {code} for model '{model}'."
         )),
-        LlmError::Decode(detail) => ModuleError::internal(format!(
+        (_, LlmError::Decode(detail)) => ModuleError::internal(format!(
-            "LLM response from {endpoint} was not valid Ollama JSON: {detail}"
+            "LLM response from {endpoint} did not match the expected schema: {detail}"
         )),
-        LlmError::MissingInput(name) => {
+        (_, LlmError::MissingInput(name)) => {
             ModuleError::invalid_input(format!("missing required input '{name}'"))
         }
+        (_, LlmError::UnsupportedApi(raw)) => ModuleError::invalid_input(format!(
+            "unsupported api '{raw}' (expected: ollama, openai, anthropic)"
+        )),
     }
 }
 
@@ -120,7 +152,7 @@ impl crate::llm::LlmClient for HostStubClient {
         &self,
         _url: &str,
         _body: &str,
-        _api_key: &str,
+        _headers: &[(&'static str, String)],
     ) -> Result<String, crate::llm::LlmError> {
         Err(crate::llm::LlmError::Http(
             "LLM HTTP path is unavailable on the host build; only wasm32 supports outbound HTTP"
@@ -138,14 +170,14 @@ impl crate::llm::LlmClient for WakiClient {
         &self,
         url: &str,
         body: &str,
-        api_key: &str,
+        headers: &[(&'static str, String)],
     ) -> Result<String, crate::llm::LlmError> {
         let mut request = waki::Client::new()
             .post(url)
             .header("Content-Type", "application/json")
             .body(body.to_string());
-        if !api_key.is_empty() {
+        for (name, value) in headers {
-            request = request.header("Authorization", &format!("Bearer {api_key}"));
+            request = request.header(*name, value);
         }
         let response = request
             .send()
@@ -184,4 +216,17 @@ mod tests {
         let p = build_prompt("one paragraph", "", "x");
         assert!(!p.contains(" in "));
     }
+
+    #[test]
+    fn empty_api_input_defaults_to_ollama() {
+        assert_eq!(crate::llm::Api::parse("").unwrap(), crate::llm::Api::Ollama);
+    }
+
+    #[test]
+    fn openai_api_is_accepted_for_vllm_endpoints() {
+        assert_eq!(
+            crate::llm::Api::parse("openai").unwrap(),
+            crate::llm::Api::Openai
+        );
+    }
 }

src/llm.rs

@@ -1,35 +1,84 @@
-//! Ollama-shaped chat client.
+//! Multi-API chat client: Ollama (default), OpenAI, Anthropic.
 //!
-//! v0.1.0 targets the Ollama `/api/chat` endpoint. OpenAI- and
+//! The wire format is selected via the optional `api` input:
-//! Anthropic-compatible adapters are deliberately deferred — each
+//!
-//! has its own request/response shape that warrants its own crate
+//! - `ollama` (default) — Ollama `/api/chat`; unchanged v0.1.0
-//! (or at least its own module here) once a flow needs it.
+//!   behavior including the best-effort model-digest probe.
+//! - `openai` — OpenAI Chat Completions wire format
+//!   (`/v1/chat/completions`), the de-facto standard implemented
+//!   by vLLM and most self-hosted inference servers. Bearer auth.
+//! - `anthropic` — Anthropic Messages API (`/v1/messages`),
+//!   `x-api-key` + `anthropic-version` headers, top-level
+//!   `system` field, mandatory `max_tokens`.
+//!
+//! No streaming, no tool calls — one prompt in, one completion out.
 //!
 //! All HTTP I/O lives behind a `LlmClient` trait so unit tests can
-//! exercise prompt building, response parsing, and the digest
+//! exercise request building, response parsing, and the digest
 //! probe on the host without making real network calls.
 
 use serde::Serialize;
 
+/// Anthropic's Messages API requires `max_tokens`. This default is
+/// large enough for document-processing completions while staying
+/// well below every current model's output cap.
+const ANTHROPIC_DEFAULT_MAX_TOKENS: u32 = 4096;
+
+/// Pinned Messages API version header. Anthropic keeps old
+/// versions working; bump deliberately, never implicitly.
+const ANTHROPIC_VERSION: &str = "2023-06-01";
+
 #[allow(dead_code)]
 #[derive(Debug, thiserror::Error)]
 pub enum LlmError {
     #[error("missing required input '{0}'")]
     MissingInput(&'static str),
+    #[error("unsupported api '{0}' (expected: ollama, openai, anthropic)")]
+    UnsupportedApi(String),
     #[error("http error: {0}")]
     Http(String),
     #[error("non-success status: {0}")]
     Status(u16),
-    #[error("response body could not be parsed as Ollama schema: {0}")]
+    #[error("response body could not be parsed as the expected schema: {0}")]
     Decode(String),
 }
 
+/// Which wire format to speak. Selected by the `api` input.
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+pub enum Api {
+    Ollama,
+    Openai,
+    Anthropic,
+}
+
+impl Api {
+    /// Parse the `api` input. Empty string means the input was not
+    /// provided and falls back to Ollama (v0.1.0 behavior).
+    pub fn parse(raw: &str) -> Result<Self, LlmError> {
+        match raw.trim().to_ascii_lowercase().as_str() {
+            "" | "ollama" => Ok(Api::Ollama),
+            "openai" => Ok(Api::Openai),
+            "anthropic" => Ok(Api::Anthropic),
+            other => Err(LlmError::UnsupportedApi(other.to_string())),
+        }
+    }
+}
+
 pub trait LlmClient {
-    fn post_json(&self, url: &str, body: &str, api_key: &str) -> Result<String, LlmError>;
+    /// POST `body` as JSON to `url` with the given extra headers.
+    /// `Content-Type: application/json` is implied. Header VALUES
+    /// may carry credentials — implementations must never log them.
+    fn post_json(
+        &self,
+        url: &str,
+        body: &str,
+        headers: &[(&'static str, String)],
+    ) -> Result<String, LlmError>;
 }
 
 #[derive(Debug, Clone)]
 pub struct ChatParams<'a> {
+    pub api: Api,
     pub endpoint: &'a str,
     pub model: &'a str,
     pub api_key: &'a str,
@@ -37,8 +86,29 @@ pub struct ChatParams<'a> {
     pub prompt: &'a str,
 }
 
+/// Auth/protocol headers for the selected API. The api_key is only
+/// ever placed into a header value here; it must not appear in any
+/// error, output, or log line.
+pub fn build_headers(p: &ChatParams) -> Vec<(&'static str, String)> {
+    let mut headers = Vec::with_capacity(2);
+    match p.api {
+        Api::Ollama | Api::Openai => {
+            if !p.api_key.is_empty() {
+                headers.push(("Authorization", format!("Bearer {}", p.api_key)));
+            }
+        }
+        Api::Anthropic => {
+            if !p.api_key.is_empty() {
+                headers.push(("x-api-key", p.api_key.to_string()));
+            }
+            headers.push(("anthropic-version", ANTHROPIC_VERSION.to_string()));
+        }
+    }
+    headers
+}
+
 #[derive(Serialize)]
-struct OllamaMessage<'a> {
+struct ChatMessage<'a> {
     role: &'a str,
     content: &'a str,
 }
@@ -46,33 +116,86 @@ struct OllamaMessage<'a> {
 #[derive(Serialize)]
 struct OllamaRequest<'a> {
     model: &'a str,
-    messages: Vec<OllamaMessage<'a>>,
+    messages: Vec<ChatMessage<'a>>,
     stream: bool,
 }
 
-/// Build the request body for an Ollama `/api/chat` invocation.
+#[derive(Serialize)]
-/// The system prompt is omitted from the messages list when empty
+struct OpenAiRequest<'a> {
-/// so a deployment can use the model's built-in system prompt.
+    model: &'a str,
-pub fn build_ollama_body(p: &ChatParams) -> String {
+    messages: Vec<ChatMessage<'a>>,
+    stream: bool,
+}
+
+#[derive(Serialize)]
+struct AnthropicRequest<'a> {
+    model: &'a str,
+    max_tokens: u32,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    system: Option<&'a str>,
+    messages: Vec<ChatMessage<'a>>,
+}
+
+/// System-then-user message list shared by the Ollama and OpenAI
+/// wire formats. The system message is omitted when empty so a
+/// deployment can use the model's built-in system prompt.
+fn build_messages<'a>(p: &ChatParams<'a>) -> Vec<ChatMessage<'a>> {
     let mut messages = Vec::with_capacity(2);
     if !p.system_prompt.is_empty() {
-        messages.push(OllamaMessage {
+        messages.push(ChatMessage {
             role: "system",
             content: p.system_prompt,
         });
     }
-    messages.push(OllamaMessage {
+    messages.push(ChatMessage {
         role: "user",
         content: p.prompt,
     });
+    messages
+}
+
+/// Build the request body for an Ollama `/api/chat` invocation.
+pub fn build_ollama_body(p: &ChatParams) -> String {
     let req = OllamaRequest {
         model: p.model,
-        messages,
+        messages: build_messages(p),
         stream: false,
     };
     serde_json::to_string(&req).unwrap_or_else(|_| String::from("{}"))
 }
 
+/// Build the request body for an OpenAI-compatible
+/// `/v1/chat/completions` invocation (OpenAI, vLLM, most
+/// self-hosted inference servers).
+pub fn build_openai_body(p: &ChatParams) -> String {
+    let req = OpenAiRequest {
+        model: p.model,
+        messages: build_messages(p),
+        stream: false,
+    };
+    serde_json::to_string(&req).unwrap_or_else(|_| String::from("{}"))
+}
+
+/// Build the request body for an Anthropic `/v1/messages`
+/// invocation. `system` is a top-level field (not a message);
+/// `max_tokens` is mandatory in the Messages API.
+pub fn build_anthropic_body(p: &ChatParams) -> String {
+    let req = AnthropicRequest {
+        model: p.model,
+        max_tokens: ANTHROPIC_DEFAULT_MAX_TOKENS,
+        system: if p.system_prompt.is_empty() {
+            None
+        } else {
+            Some(p.system_prompt)
+        },
+        messages: vec![ChatMessage {
+            role: "user",
+            content: p.prompt,
+        }],
+    };
+    serde_json::to_string(&req).unwrap_or_else(|_| String::from("{}"))
+}
+
 /// Extract the assistant's message text from an Ollama
 /// /api/chat response.
 pub fn extract_ollama_content(body: &str) -> Result<String, LlmError> {
@@ -85,16 +208,44 @@ pub fn extract_ollama_content(body: &str) -> Result<String, LlmError> {
         .ok_or_else(|| LlmError::Decode("missing message.content".into()))
 }
 
+/// Extract the assistant text from an OpenAI Chat Completions
+/// response: `choices[0].message.content`.
+pub fn extract_openai_content(body: &str) -> Result<String, LlmError> {
+    let v: serde_json::Value =
+        serde_json::from_str(body).map_err(|e| LlmError::Decode(e.to_string()))?;
+    v.get("choices")
+        .and_then(|c| c.get(0))
+        .and_then(|c| c.get("message"))
+        .and_then(|m| m.get("content"))
+        .and_then(|c| c.as_str())
+        .map(|s| s.to_string())
+        .ok_or_else(|| LlmError::Decode("missing choices[0].message.content".into()))
+}
+
+/// Extract the assistant text from an Anthropic Messages
+/// response: `content[0].text`.
+pub fn extract_anthropic_content(body: &str) -> Result<String, LlmError> {
+    let v: serde_json::Value =
+        serde_json::from_str(body).map_err(|e| LlmError::Decode(e.to_string()))?;
+    v.get("content")
+        .and_then(|c| c.get(0))
+        .and_then(|c| c.get("text"))
+        .and_then(|t| t.as_str())
+        .map(|s| s.to_string())
+        .ok_or_else(|| LlmError::Decode("missing content[0].text".into()))
+}
+
 #[derive(Debug, Clone)]
 pub struct ChatWithIdentity {
     pub response: String,
     pub model_digest: Option<String>,
 }
 
-/// Run an Ollama chat call AND probe the model digest. Probe is
+/// Run one chat call in the selected wire format AND, for Ollama,
-/// best-effort — non-Ollama endpoints (no `/api/chat` suffix) and
+/// probe the model digest. The probe is best-effort — non-Ollama
-/// transient failures yield `model_digest = None` rather than
+/// APIs expose no digest endpoint, so `model_digest = None` there
-/// failing the whole call.
+/// (documented empty, never invented); transient probe failures on
+/// Ollama also yield `None` rather than failing the whole call.
 pub fn chat_with_identity<C: LlmClient>(
     client: &C,
     p: &ChatParams,
@@ -108,10 +259,22 @@ pub fn chat_with_identity<C: LlmClient>(
     if p.prompt.is_empty() {
         return Err(LlmError::MissingInput("prompt"));
     }
-    let body = build_ollama_body(p);
+    let body = match p.api {
-    let response_body = client.post_json(p.endpoint, &body, p.api_key)?;
+        Api::Ollama => build_ollama_body(p),
-    let response = extract_ollama_content(&response_body)?;
+        Api::Openai => build_openai_body(p),
-    let model_digest = probe_model_digest(client, p);
+        Api::Anthropic => build_anthropic_body(p),
+    };
+    let headers = build_headers(p);
+    let response_body = client.post_json(p.endpoint, &body, &headers)?;
+    let response = match p.api {
+        Api::Ollama => extract_ollama_content(&response_body)?,
+        Api::Openai => extract_openai_content(&response_body)?,
+        Api::Anthropic => extract_anthropic_content(&response_body)?,
+    };
+    let model_digest = match p.api {
+        Api::Ollama => probe_model_digest(client, p),
+        Api::Openai | Api::Anthropic => None,
+    };
     Ok(ChatWithIdentity {
         response,
         model_digest,
@@ -121,7 +284,8 @@ pub fn chat_with_identity<C: LlmClient>(
 fn probe_model_digest<C: LlmClient>(client: &C, p: &ChatParams) -> Option<String> {
     let show_url = derive_show_url(p.endpoint)?;
     let body = serde_json::to_string(&serde_json::json!({ "name": p.model })).ok()?;
-    let response = client.post_json(&show_url, &body, p.api_key).ok()?;
+    let headers = build_headers(p);
+    let response = client.post_json(&show_url, &body, &headers).ok()?;
     extract_show_digest(&response)
 }
 
@@ -160,20 +324,39 @@ mod tests {
     use super::*;
     use std::cell::RefCell;
 
+    /// (url, body, headers) of one recorded call.
+    type RecordedCall = (String, String, Vec<(String, String)>);
+
     struct MockClient {
         responses: RefCell<Vec<Result<String, LlmError>>>,
+        /// Every call, in call order.
+        calls: RefCell<Vec<RecordedCall>>,
     }
 
     impl MockClient {
         fn new(responses: Vec<Result<String, LlmError>>) -> Self {
             Self {
                 responses: RefCell::new(responses),
+                calls: RefCell::new(Vec::new()),
             }
         }
     }
 
     impl LlmClient for MockClient {
-        fn post_json(&self, _url: &str, _body: &str, _api_key: &str) -> Result<String, LlmError> {
+        fn post_json(
+            &self,
+            url: &str,
+            body: &str,
+            headers: &[(&'static str, String)],
+        ) -> Result<String, LlmError> {
+            self.calls.borrow_mut().push((
+                url.to_string(),
+                body.to_string(),
+                headers
+                    .iter()
+                    .map(|(k, v)| (k.to_string(), v.clone()))
+                    .collect(),
+            ));
             self.responses
                 .borrow_mut()
                 .pop()
@@ -181,14 +364,42 @@ mod tests {
         }
     }
 
-    #[test]
+    fn params<'a>(api: Api) -> ChatParams<'a> {
-    fn ollama_body_includes_system_when_provided() {
+        ChatParams {
-        let p = ChatParams {
+            api,
             endpoint: "http://x/api/chat",
             model: "qwen",
             api_key: "",
-            system_prompt: "be helpful",
+            system_prompt: "",
             prompt: "hello",
+        }
+    }
+
+    // ---------------- api input parsing ----------------
+
+    #[test]
+    fn api_parse_defaults_to_ollama() {
+        assert_eq!(Api::parse("").unwrap(), Api::Ollama);
+        assert_eq!(Api::parse("ollama").unwrap(), Api::Ollama);
+        assert_eq!(Api::parse("  OpenAI ").unwrap(), Api::Openai);
+        assert_eq!(Api::parse("anthropic").unwrap(), Api::Anthropic);
+    }
+
+    #[test]
+    fn api_parse_rejects_unknown() {
+        assert!(matches!(
+            Api::parse("gemini"),
+            Err(LlmError::UnsupportedApi(_))
+        ));
+    }
+
+    // ---------------- Ollama wire format (regression) ----------------
+
+    #[test]
+    fn ollama_body_includes_system_when_provided() {
+        let p = ChatParams {
+            system_prompt: "be helpful",
+            ..params(Api::Ollama)
         };
         let body = build_ollama_body(&p);
         let v: serde_json::Value = serde_json::from_str(&body).unwrap();
@@ -198,18 +409,12 @@ mod tests {
         assert_eq!(messages[0]["content"], "be helpful");
         assert_eq!(messages[1]["role"], "user");
         assert_eq!(messages[1]["content"], "hello");
+        assert_eq!(v["stream"], false);
     }
 
     #[test]
     fn ollama_body_omits_system_when_empty() {
-        let p = ChatParams {
+        let body = build_ollama_body(&params(Api::Ollama));
-            endpoint: "http://x/api/chat",
-            model: "qwen",
-            api_key: "",
-            system_prompt: "",
-            prompt: "hello",
-        };
-        let body = build_ollama_body(&p);
         let v: serde_json::Value = serde_json::from_str(&body).unwrap();
         let messages = v["messages"].as_array().unwrap();
         assert_eq!(messages.len(), 1);
@@ -231,6 +436,208 @@ mod tests {
         ));
     }
 
+    #[test]
+    fn ollama_bearer_header_only_with_key() {
+        assert!(build_headers(&params(Api::Ollama)).is_empty());
+        let p = ChatParams {
+            api_key: "sk-test",
+            ..params(Api::Ollama)
+        };
+        assert_eq!(
+            build_headers(&p),
+            vec![("Authorization", "Bearer sk-test".to_string())]
+        );
+    }
+
+    // ---------------- OpenAI wire format ----------------
+
+    #[test]
+    fn openai_body_matches_chat_completions_format() {
+        let p = ChatParams {
+            api: Api::Openai,
+            endpoint: "http://vllm:8000/v1/chat/completions",
+            model: "meta-llama/Llama-3.1-8B-Instruct",
+            api_key: "sk-x",
+            system_prompt: "be terse",
+            prompt: "hello",
+        };
+        let body = build_openai_body(&p);
+        let v: serde_json::Value = serde_json::from_str(&body).unwrap();
+        assert_eq!(v["model"], "meta-llama/Llama-3.1-8B-Instruct");
+        assert_eq!(v["stream"], false);
+        let messages = v["messages"].as_array().unwrap();
+        assert_eq!(messages.len(), 2);
+        assert_eq!(messages[0]["role"], "system");
+        assert_eq!(messages[0]["content"], "be terse");
+        assert_eq!(messages[1]["role"], "user");
+        assert_eq!(messages[1]["content"], "hello");
+        // No Anthropic-only fields leak into the OpenAI body.
+        assert!(v.get("max_tokens").is_none());
+        assert!(v.get("system").is_none());
+    }
+
+    #[test]
+    fn openai_response_parses_fixture() {
+        // Shape as returned by OpenAI / vLLM /v1/chat/completions.
+        let body = r#"{
+            "id": "chatcmpl-123",
+            "object": "chat.completion",
+            "created": 1719000000,
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
+            "choices": [{
+                "index": 0,
+                "message": {"role": "assistant", "content": "Hi from vLLM"},
+                "finish_reason": "stop"
+            }],
+            "usage": {"prompt_tokens": 9, "completion_tokens": 4, "total_tokens": 13}
+        }"#;
+        assert_eq!(extract_openai_content(body).unwrap(), "Hi from vLLM");
+    }
+
+    #[test]
+    fn openai_response_errors_on_missing_choices() {
+        assert!(matches!(
+            extract_openai_content(r#"{"object":"chat.completion","choices":[]}"#),
+            Err(LlmError::Decode(_))
+        ));
+        assert!(matches!(
+            extract_openai_content(r#"{"error":{"message":"invalid key"}}"#),
+            Err(LlmError::Decode(_))
+        ));
+    }
+
+    #[test]
+    fn openai_uses_bearer_auth() {
+        let p = ChatParams {
+            api_key: "sk-test",
+            ..params(Api::Openai)
+        };
+        assert_eq!(
+            build_headers(&p),
+            vec![("Authorization", "Bearer sk-test".to_string())]
+        );
+    }
+
+    #[test]
+    fn openai_chat_returns_content_and_no_digest() {
+        let canned = r#"{"choices":[{"message":{"role":"assistant","content":"ok"}}]}"#;
+        let client = MockClient::new(vec![Ok(canned.to_string())]);
+        let p = ChatParams {
+            api: Api::Openai,
+            endpoint: "http://vllm:8000/v1/chat/completions",
+            model: "m",
+            api_key: "sk",
+            system_prompt: "",
+            prompt: "hi",
+        };
+        let result = chat_with_identity(&client, &p).unwrap();
+        assert_eq!(result.response, "ok");
+        // No digest API on OpenAI-compatible endpoints — documented
+        // empty, and exactly one HTTP call (no probe).
+        assert_eq!(result.model_digest, None);
+        assert_eq!(client.calls.borrow().len(), 1);
+    }
+
+    // ---------------- Anthropic wire format ----------------
+
+    #[test]
+    fn anthropic_body_matches_messages_format() {
+        let p = ChatParams {
+            api: Api::Anthropic,
+            endpoint: "https://api.anthropic.com/v1/messages",
+            model: "claude-fable-5",
+            api_key: "sk-ant",
+            system_prompt: "be terse",
+            prompt: "hello",
+        };
+        let body = build_anthropic_body(&p);
+        let v: serde_json::Value = serde_json::from_str(&body).unwrap();
+        assert_eq!(v["model"], "claude-fable-5");
+        // max_tokens is mandatory in the Messages API.
+        assert_eq!(v["max_tokens"], 4096);
+        // system is a top-level field, never a message.
+        assert_eq!(v["system"], "be terse");
+        let messages = v["messages"].as_array().unwrap();
+        assert_eq!(messages.len(), 1);
+        assert_eq!(messages[0]["role"], "user");
+        assert_eq!(messages[0]["content"], "hello");
+        // No OpenAI/Ollama-only fields leak in.
+        assert!(v.get("stream").is_none());
+    }
+
+    #[test]
+    fn anthropic_body_omits_system_when_empty() {
+        let body = build_anthropic_body(&params(Api::Anthropic));
+        let v: serde_json::Value = serde_json::from_str(&body).unwrap();
+        assert!(v.get("system").is_none());
+        assert_eq!(v["max_tokens"], 4096);
+    }
+
+    #[test]
+    fn anthropic_response_parses_fixture() {
+        // Shape as returned by the Anthropic Messages API.
+        let body = r#"{
+            "id": "msg_01XFDUDYJgAACzvnptvVoYEL",
+            "type": "message",
+            "role": "assistant",
+            "model": "claude-fable-5",
+            "content": [{"type": "text", "text": "Hi from Claude"}],
+            "stop_reason": "end_turn",
+            "usage": {"input_tokens": 10, "output_tokens": 5}
+        }"#;
+        assert_eq!(extract_anthropic_content(body).unwrap(), "Hi from Claude");
+    }
+
+    #[test]
+    fn anthropic_response_errors_on_missing_content() {
+        assert!(matches!(
+            extract_anthropic_content(r#"{"type":"message","content":[]}"#),
+            Err(LlmError::Decode(_))
+        ));
+        assert!(matches!(
+            extract_anthropic_content(r#"{"type":"error","error":{"message":"x"}}"#),
+            Err(LlmError::Decode(_))
+        ));
+    }
+
+    #[test]
+    fn anthropic_uses_x_api_key_and_version_headers() {
+        let p = ChatParams {
+            api_key: "sk-ant-test",
+            ..params(Api::Anthropic)
+        };
+        let headers = build_headers(&p);
+        assert_eq!(
+            headers,
+            vec![
+                ("x-api-key", "sk-ant-test".to_string()),
+                ("anthropic-version", "2023-06-01".to_string()),
+            ]
+        );
+        // Never Bearer auth on the Anthropic path.
+        assert!(headers.iter().all(|(k, _)| *k != "Authorization"));
+    }
+
+    #[test]
+    fn anthropic_chat_returns_content_and_no_digest() {
+        let canned = r#"{"content":[{"type":"text","text":"ok"}]}"#;
+        let client = MockClient::new(vec![Ok(canned.to_string())]);
+        let p = ChatParams {
+            api: Api::Anthropic,
+            endpoint: "https://api.anthropic.com/v1/messages",
+            model: "claude-fable-5",
+            api_key: "sk-ant",
+            system_prompt: "",
+            prompt: "hi",
+        };
+        let result = chat_with_identity(&client, &p).unwrap();
+        assert_eq!(result.response, "ok");
+        assert_eq!(result.model_digest, None);
+        assert_eq!(client.calls.borrow().len(), 1);
+    }
+
+    // ---------------- digest probe (Ollama only) ----------------
+
     #[test]
     fn derive_show_url_swaps_chat_suffix() {
         assert_eq!(
@@ -263,10 +670,7 @@ mod tests {
         let client = MockClient::new(vec![Ok(canned_show), Ok(canned_chat)]);
         let p = ChatParams {
             endpoint: "http://localhost:11434/api/chat",
-            model: "qwen",
+            ..params(Api::Ollama)
-            api_key: "",
-            system_prompt: "",
-            prompt: "hi",
         };
         let result = chat_with_identity(&client, &p).unwrap();
         assert_eq!(result.response, "the response");
@@ -279,10 +683,7 @@ mod tests {
         let client = MockClient::new(vec![Err(LlmError::Status(500)), Ok(canned_chat)]);
         let p = ChatParams {
             endpoint: "http://localhost:11434/api/chat",
-            model: "qwen",
+            ..params(Api::Ollama)
-            api_key: "",
-            system_prompt: "",
-            prompt: "hi",
         };
         let result = chat_with_identity(&client, &p).unwrap();
         assert_eq!(result.response, "ok");
@@ -290,30 +691,26 @@ mod tests {
     }
 
     #[test]
-    fn chat_with_identity_skips_probe_for_non_ollama() {
+    fn chat_with_identity_skips_probe_for_non_ollama_url() {
         let canned_chat = r#"{"message":{"content":"ok"}}"#.to_string();
         let client = MockClient::new(vec![Ok(canned_chat)]);
         let p = ChatParams {
-            endpoint: "https://api.openai.com/v1/chat/completions",
+            endpoint: "https://example.com/proxy/chat",
-            model: "gpt",
+            ..params(Api::Ollama)
-            api_key: "k",
-            system_prompt: "",
-            prompt: "hi",
         };
         let result = chat_with_identity(&client, &p).unwrap();
         assert_eq!(result.response, "ok");
         assert_eq!(result.model_digest, None);
     }
 
+    // ---------------- input guards ----------------
+
     #[test]
     fn empty_prompt_is_rejected() {
         let client = MockClient::new(vec![]);
         let p = ChatParams {
-            endpoint: "http://x/api/chat",
-            model: "x",
-            api_key: "",
-            system_prompt: "",
             prompt: "",
+            ..params(Api::Ollama)
         };
         assert!(matches!(
             chat_with_identity(&client, &p),