Extract OpenAI helpers and add Ollama support
Introduce shared OpenAI/Ollama wire-format helpers (src/llm/openai-common.ts) and refactor the Ollama adapter to reuse them. Update Ollama client baseURL and API key handling, add example env detection for OLLAMA_BASE_URL and prefer Ollama for the analyst agent when present. Also update types to include 'ollama' in provider unions and adjust readonly annotations. Minor formatting/packaging updates and small cleanup in adapter and example files.
This commit is contained in:
Masteromanlol
parent
5a384a9315
commit
4e4918c4bb
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@ -136,6 +136,7 @@ function buildCustomAgent(
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// ---------------------------------------------------------------------------
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const useOpenAI = Boolean(process.env.OPENAI_API_KEY)
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const useOllama = Boolean(process.env.OLLAMA_BASE_URL);
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const researcherConfig: AgentConfig = {
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name: 'researcher',
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@ -151,7 +152,8 @@ Return the raw rates as a JSON object keyed by pair, e.g. { "USD/EUR": 0.91, "US
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const analystConfig: AgentConfig = {
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name: 'analyst',
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model: useOllama ? 'llama3.1' : useOpenAI ? 'gpt-4o-mini' : 'claude-3-5-sonnet-20240620',\n provider: useOllama ? 'ollama' : useOpenAI ? 'openai' : 'anthropic',
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model: useOllama ? 'llama3.1' : useOpenAI ? 'gpt-4o-mini' : 'claude-3-5-sonnet-20240620',
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provider: useOllama ? 'ollama' : useOpenAI ? 'openai' : 'anthropic',
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systemPrompt: `You are a foreign exchange analyst.
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You receive exchange rate data and produce a short briefing.
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Use format_currency to show example conversions.
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@ -1,15 +1,16 @@
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{
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"name": "maestro-agents",
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"name": "@jackchen_me/open-multi-agent",
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"version": "0.1.0",
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"lockfileVersion": 3,
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"requires": true,
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"packages": {
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"": {
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"name": "maestro-agents",
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"name": "@jackchen_me/open-multi-agent",
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"version": "0.1.0",
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"license": "MIT",
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"dependencies": {
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"@anthropic-ai/sdk": "^0.52.0",
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"ollama": "^0.3.0",
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"openai": "^4.73.0",
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"zod": "^3.23.0"
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},
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50
package.json
50
package.json
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@ -1,49 +1 @@
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{
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"name": "@jackchen_me/open-multi-agent",
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"version": "0.1.0",
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"description": "Production-grade multi-agent orchestration framework. Model-agnostic, supports team collaboration, task scheduling, and inter-agent communication.",
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"type": "module",
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"main": "dist/index.js",
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"types": "dist/index.d.ts",
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"exports": {
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".": {
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"types": "./dist/index.d.ts",
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"import": "./dist/index.js"
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}
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},
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"scripts": {
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"build": "tsc",
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"dev": "tsc --watch",
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"test": "vitest run",
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"test:watch": "vitest",
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"lint": "tsc --noEmit",
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"prepublishOnly": "npm run build"
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},
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"keywords": [
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"ai",
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"agent",
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"multi-agent",
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"orchestration",
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"llm",
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"claude",
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"openai",
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"mcp",
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"tool-use",
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"agent-framework"
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],
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"author": "",
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"license": "MIT",
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"engines": {
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"node": ">=18.0.0"
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},
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"dependencies": {
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"@anthropic-ai/sdk": "^0.52.0",
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"openai": "^4.73.0",
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"zod": "^3.23.0"
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},
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"devDependencies": {
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"typescript": "^5.6.0",
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"vitest": "^2.1.0",
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"@types/node": "^22.0.0"
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}
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}
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{"name":"@jackchen_me/open-multi-agent","version":"0.1.0","description":"Production-grade multi-agent orchestration framework. Model-agnostic, supports team collaboration, task scheduling, and inter-agent communication.","type":"module","main":"dist/index.js","types":"dist/index.d.ts","exports":{".":{"types":"./dist/index.d.ts","import":"./dist/index.js"}},"scripts":{"build":"tsc","dev":"tsc --watch","test":"vitest run","test:watch":"vitest","lint":"tsc --noEmit","prepublishOnly":"npm run build"},"keywords":["ai","agent","multi-agent","orchestration","llm","claude","openai","ollama","mcp","tool-use","agent-framework"],"author":"","license":"MIT","engines":{"node":">=18.0.0"},"dependencies":{"@anthropic-ai/sdk":"^0.52.0","openai":"^4.73.0","zod":"^3.23.0"},"devDependencies":{"typescript":"^5.6.0","vitest":"^2.1.0","@types/node":"^22.0.0"}}
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@ -78,4 +78,3 @@ export async function createAdapter(
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}
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}
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}
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@ -56,172 +56,15 @@ import type {
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// Internal helpers — framework → Ollama (same as OpenAI)
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// ---------------------------------------------------------------------------
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/**
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* Convert a framework {@link LLMToolDef} to an Ollama {@link ChatCompletionTool}.
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*/
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function toOpenAITool(tool: LLMToolDef): ChatCompletionTool {
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return {
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type: 'function',
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function: {
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name: tool.name,
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description: tool.description,
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parameters: tool.inputSchema as Record<string, unknown>,
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},
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}
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}
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function hasToolResults(msg: LLMMessage): boolean {
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return msg.content.some((b) => b.type === 'tool_result')
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}
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function toOpenAIMessages(messages: LLMMessage[]): ChatCompletionMessageParam[] {
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const result: ChatCompletionMessageParam[] = []
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for (const msg of messages) {
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if (msg.role === 'assistant') {
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result.push(toOpenAIAssistantMessage(msg))
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} else {
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if (!hasToolResults(msg)) {
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result.push(toOpenAIUserMessage(msg))
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} else {
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const nonToolBlocks = msg.content.filter((b) => b.type !== 'tool_result')
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if (nonToolBlocks.length > 0) {
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result.push(toOpenAIUserMessage({ role: 'user', content: nonToolBlocks }))
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}
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for (const block of msg.content) {
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if (block.type === 'tool_result') {
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const toolMsg: ChatCompletionToolMessageParam = {
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role: 'tool',
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tool_call_id: block.tool_use_id,
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content: block.content,
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}
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result.push(toolMsg)
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}
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}
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}
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}
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}
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return result
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}
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function toOpenAIUserMessage(msg: LLMMessage): ChatCompletionUserMessageParam {
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if (msg.content.length === 1 && msg.content[0]?.type === 'text') {
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return { role: 'user', content: msg.content[0].text }
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}
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type ContentPart = OpenAI.Chat.ChatCompletionContentPartText | OpenAI.Chat.ChatCompletionContentPartImage
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const parts: ContentPart[] = []
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for (const block of msg.content) {
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if (block.type === 'text') {
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parts.push({ type: 'text', text: block.text })
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} else if (block.type === 'image') {
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parts.push({
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type: 'image_url',
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image_url: {
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url: `data:${block.source.media_type};base64,${block.source.data}`,
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},
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})
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}
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}
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return { role: 'user', content: parts }
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}
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function toOpenAIAssistantMessage(msg: LLMMessage): ChatCompletionAssistantMessageParam {
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const toolCalls: ChatCompletionMessageToolCall[] = []
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const textParts: string[] = []
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for (const block of msg.content) {
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if (block.type === 'tool_use') {
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toolCalls.push({
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id: block.id,
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type: 'function',
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function: {
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name: block.name,
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arguments: JSON.stringify(block.input),
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},
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})
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} else if (block.type === 'text') {
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textParts.push(block.text)
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}
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}
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const assistantMsg: ChatCompletionAssistantMessageParam = {
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role: 'assistant',
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content: textParts.length > 0 ? textParts.join('') : null,
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}
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if (toolCalls.length > 0) {
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assistantMsg.tool_calls = toolCalls
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}
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return assistantMsg
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}
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// ---------------------------------------------------------------------------
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// Internal helpers — Ollama → framework (same as OpenAI)
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// ---------------------------------------------------------------------------
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function fromOpenAICompletion(completion: ChatCompletion): LLMResponse {
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const choice = completion.choices[0]
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if (choice === undefined) {
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throw new Error('Ollama returned a completion with no choices')
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}
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const content: ContentBlock[] = []
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const message = choice.message
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if (message.content !== null && message.content !== undefined) {
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const textBlock: TextBlock = { type: 'text', text: message.content }
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content.push(textBlock)
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}
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for (const toolCall of message.tool_calls ?? []) {
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let parsedInput: Record<string, unknown> = {}
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try {
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const parsed: unknown = JSON.parse(toolCall.function.arguments)
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if (parsed !== null && typeof parsed === 'object' && !Array.isArray(parsed)) {
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parsedInput = parsed as Record<string, unknown>
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}
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} catch {
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// Malformed arguments from the model — surface as empty object.
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}
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const toolUseBlock: ToolUseBlock = {
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type: 'tool_use',
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id: toolCall.id,
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name: toolCall.function.name,
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input: parsedInput,
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}
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content.push(toolUseBlock)
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}
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const stopReason = normalizeFinishReason(choice.finish_reason ?? 'stop')
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return {
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id: completion.id,
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content,
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model: completion.model,
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stop_reason: stopReason,
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usage: {
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input_tokens: completion.usage?.prompt_tokens ?? 0,
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output_tokens: completion.usage?.completion_tokens ?? 0,
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},
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}
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}
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function normalizeFinishReason(reason: string): string {
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switch (reason) {
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case 'stop': return 'end_turn'
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case 'tool_calls': return 'tool_use'
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case 'length': return 'max_tokens'
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case 'content_filter': return 'content_filter'
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default: return reason
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}
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}
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import {
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toOpenAITool,
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hasToolResults,
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toOpenAIMessages,
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toOpenAIUserMessage,
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toOpenAIAssistantMessage,
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fromOpenAICompletion,
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normalizeFinishReason,
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} from './openai-common.js'
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// ---------------------------------------------------------------------------
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// Adapter implementation
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@ -240,7 +83,8 @@ export class OllamaAdapter implements LLMAdapter {
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constructor() {
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this.#client = new OpenAI({
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baseURL: process.env.OLLAMA_BASE_URL ?? 'http://localhost:11434',
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baseURL: process.env.OLLAMA_BASE_URL ?? 'http://localhost:11434/v1',
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apiKey: 'ollama', // dummy API key
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})
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}
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@ -0,0 +1,231 @@
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/**
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* @fileoverview Shared OpenAI wire-format helpers for Ollama and OpenAI adapters.
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*
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* These functions convert between the framework's internal types and the
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* OpenAI/Ollama Chat Completions wire format. Both adapters should import
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* from here rather than duplicating the conversion logic.
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*/
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import type {
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ChatCompletion,
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ChatCompletionAssistantMessageParam,
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ChatCompletionChunk,
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ChatCompletionMessageParam,
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ChatCompletionMessageToolCall,
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ChatCompletionTool,
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ChatCompletionToolMessageParam,
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ChatCompletionUserMessageParam,
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} from 'openai/resources/chat/completions/index.js'
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import type {
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ContentBlock,
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LLMMessage,
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LLMResponse,
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LLMToolDef,
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StreamEvent,
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TextBlock,
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ToolUseBlock,
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} from '../types.js'
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/**
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* Convert a framework {@link LLMToolDef} to an OpenAI/Ollama {@link ChatCompletionTool}.
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*/
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export function toOpenAITool(tool: LLMToolDef): ChatCompletionTool {
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return {
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type: 'function',
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function: {
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name: tool.name,
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description: tool.description,
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parameters: tool.inputSchema as Record<string, unknown>,
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},
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}
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}
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/**
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* Determine whether a framework message contains any `tool_result` content
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* blocks, which must be serialised as separate OpenAI/Ollama `tool`-role messages.
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*/
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export function hasToolResults(msg: LLMMessage): boolean {
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return msg.content.some((b) => b.type === 'tool_result')
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}
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/**
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* Convert a single framework {@link LLMMessage} into one or more OpenAI/Ollama
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* {@link ChatCompletionMessageParam} entries.
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*/
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export function toOpenAIMessages(messages: LLMMessage[]): ChatCompletionMessageParam[] {
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const result: ChatCompletionMessageParam[] = []
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for (const msg of messages) {
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if (msg.role === 'assistant') {
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result.push(toOpenAIAssistantMessage(msg))
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} else {
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if (!hasToolResults(msg)) {
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result.push(toOpenAIUserMessage(msg))
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} else {
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const nonToolBlocks = msg.content.filter((b) => b.type !== 'tool_result')
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if (nonToolBlocks.length > 0) {
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result.push(toOpenAIUserMessage({ role: 'user', content: nonToolBlocks }))
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}
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for (const block of msg.content) {
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if (block.type === 'tool_result') {
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const toolMsg: ChatCompletionToolMessageParam = {
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role: 'tool',
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tool_call_id: block.tool_use_id,
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content: block.content,
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}
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result.push(toolMsg)
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}
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}
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}
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}
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}
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return result
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}
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export function toOpenAIUserMessage(msg: LLMMessage): ChatCompletionUserMessageParam {
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if (msg.content.length === 1 && msg.content[0]?.type === 'text') {
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return { role: 'user', content: msg.content[0].text }
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}
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const parts: Array<{ type: 'text', text: string } | { type: 'image_url', image_url: { url: string } }> = []
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for (const block of msg.content) {
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if (block.type === 'text') {
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parts.push({ type: 'text', text: block.text })
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} else if (block.type === 'image') {
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parts.push({
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type: 'image_url',
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image_url: {
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url: `data:${block.source.media_type};base64,${block.source.data}`,
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},
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})
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}
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}
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return { role: 'user', content: parts }
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}
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export function toOpenAIAssistantMessage(msg: LLMMessage): ChatCompletionAssistantMessageParam {
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const toolCalls: ChatCompletionMessageToolCall[] = []
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const textParts: string[] = []
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for (const block of msg.content) {
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if (block.type === 'tool_use') {
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toolCalls.push({
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id: block.id,
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type: 'function',
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function: {
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name: block.name,
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arguments: JSON.stringify(block.input),
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},
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})
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} else if (block.type === 'text') {
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textParts.push(block.text)
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}
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}
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const assistantMsg: ChatCompletionAssistantMessageParam = {
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role: 'assistant',
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content: textParts.length > 0 ? textParts.join('') : null,
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}
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if (toolCalls.length > 0) {
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assistantMsg.tool_calls = toolCalls
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}
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return assistantMsg
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}
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/**
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* Convert an OpenAI/Ollama {@link ChatCompletion} into a framework {@link LLMResponse}.
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*/
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export function fromOpenAICompletion(completion: ChatCompletion): LLMResponse {
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const choice = completion.choices[0]
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if (choice === undefined) {
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throw new Error('Completion returned with no choices')
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}
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const content: ContentBlock[] = []
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const message = choice.message
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if (message.content !== null && message.content !== undefined) {
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const textBlock: TextBlock = { type: 'text', text: message.content }
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content.push(textBlock)
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}
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for (const toolCall of message.tool_calls ?? []) {
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let parsedInput: Record<string, unknown> = {}
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try {
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const parsed: unknown = JSON.parse(toolCall.function.arguments)
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if (parsed !== null && typeof parsed === 'object' && !Array.isArray(parsed)) {
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parsedInput = parsed as Record<string, unknown>
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}
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} catch {
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// Malformed arguments from the model — surface as empty object.
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}
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const toolUseBlock: ToolUseBlock = {
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type: 'tool_use',
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id: toolCall.id,
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name: toolCall.function.name,
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input: parsedInput,
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}
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content.push(toolUseBlock)
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}
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const stopReason = normalizeFinishReason(choice.finish_reason ?? 'stop')
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return {
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id: completion.id,
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content,
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model: completion.model,
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stop_reason: stopReason,
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usage: {
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input_tokens: completion.usage?.prompt_tokens ?? 0,
|
||||
output_tokens: completion.usage?.completion_tokens ?? 0,
|
||||
},
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Normalize an OpenAI/Ollama `finish_reason` string to the framework's canonical
|
||||
* stop-reason vocabulary.
|
||||
*/
|
||||
export function normalizeFinishReason(reason: string): string {
|
||||
switch (reason) {
|
||||
case 'stop': return 'end_turn'
|
||||
case 'tool_calls': return 'tool_use'
|
||||
case 'length': return 'max_tokens'
|
||||
case 'content_filter': return 'content_filter'
|
||||
default: return reason
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Prepend a system message when `systemPrompt` is provided, then append the
|
||||
* converted conversation messages.
|
||||
*/
|
||||
export function buildOpenAIMessageList(
|
||||
messages: LLMMessage[],
|
||||
systemPrompt: string | undefined,
|
||||
): ChatCompletionMessageParam[] {
|
||||
const result: ChatCompletionMessageParam[] = []
|
||||
|
||||
if (systemPrompt !== undefined && systemPrompt.length > 0) {
|
||||
result.push({ role: 'system', content: systemPrompt })
|
||||
}
|
||||
|
||||
result.push(...toOpenAIMessages(messages))
|
||||
return result
|
||||
}
|
||||
|
||||
// Re-export types that consumers of this module commonly need alongside the helpers.
|
||||
export type {
|
||||
ContentBlock,
|
||||
LLMMessage,
|
||||
LLMToolDef,
|
||||
ToolUseBlock,
|
||||
}
|
||||
|
|
@ -186,7 +186,7 @@ export interface ToolDefinition<TInput = Record<string, unknown>> {
|
|||
export interface AgentConfig {
|
||||
readonly name: string
|
||||
readonly model: string
|
||||
provider?: 'anthropic' | 'openai' | 'ollama'
|
||||
readonly provider?: 'anthropic' | 'ollama' | 'openai'
|
||||
readonly systemPrompt?: string
|
||||
/** Names of tools (from the tool registry) available to this agent. */
|
||||
readonly tools?: readonly string[]
|
||||
|
|
@ -285,7 +285,7 @@ export interface OrchestratorEvent {
|
|||
export interface OrchestratorConfig {
|
||||
readonly maxConcurrency?: number
|
||||
readonly defaultModel?: string
|
||||
defaultProvider?: 'anthropic' | 'openai' | 'ollama'
|
||||
readonly defaultProvider?: 'anthropic' | 'ollama' | 'openai'
|
||||
onProgress?: (event: OrchestratorEvent) => void
|
||||
}
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue