The base commit

Author: habedi

docs/README.md

@@ -1,3 +1,135 @@
 ## Project Documentation
 
 Add the documentation here.
+
+---
+
+Of course. Building a tool like "Octo" is a fantastic project. Based on my analysis of its architecture and my own
+experience, here are some ideas for building a similar tool, broken down into core concepts, advanced features, and
+product-level thinking.
+
+### 1\. Core Architecture & Foundational Ideas
+
+This is the minimum viable product (MVP) you'd need to get a functioning agent.
+
+* **The Agent Loop:** The heart of any agent is its operational cycle. The classic model is a variation of a REPL
+  (`Read-Eval-Print Loop`):
+  $$[Input] \rightarrow [Think] \rightarrow [Act] \rightarrow [Observe]$$
+
+    * **Input:** The user provides a prompt.
+    * **Think:** The agent (your code) sends the prompt and conversation history to an LLM. The LLM's "thinking"
+      process might involve generating a plan or deciding which tool to use.
+    * **Act:** Your code parses the LLM's response. If it's a tool call, you execute it. If it's a text response, you
+      display it.
+    * **Observe:** The result of the action (tool output or error) is formatted and added to the history. The loop
+      then repeats with this new context.
+
+* **A Modular Tool System:** This is non-negotiable. Don't hard-code your tools. Create a `Tool` interface and a
+  registry. "Octo" does this very well. A simple version could look like this:
+
+  ```typescript
+  interface Tool {
+    name: string;
+    description: string; // Crucial for the LLM to know when to use it
+    argumentsSchema: t.Type<any>; // Using 'structural' or 'zod' for schemas
+    execute(args: any): Promise<string>;
+  }
+
+  const toolRegistry: Map<string, Tool> = new Map();
+  ```
+
+  This allows you to add new tools like `git_diff` or `run_tests` just by defining a new object that fits the
+  interface.
+
+* **Rich History Management:** Your history isn't just a list of strings. It's a structured log of events. "Octo's"
+  `HistoryItem` type is a good example. You should explicitly differentiate between:
+
+    * `UserMessage`
+    * `AssistantMessage` (the LLM's text response)
+    * `AssistantToolRequest` (the LLM's decision to call a tool)
+    * `ToolResult` (the output from your code running the tool)
+    * `SystemNotification` (e.g., "File `x.ts` was modified externally.")
+
+### 2\. Enhancing the Core - "Leveling Up"
+
+These are features that move from a simple proof-of-concept to a robust and reliable tool.
+
+* **LLM Abstraction Layer:** "Octo" uses an IR for this. Your goal is to write code against your own generic
+  `LLMProvider` interface, not directly against the OpenAI or Anthropic SDKs.
+
+  ```typescript
+  interface LLMProvider {
+    generateResponse(history: LlmIR[], tools: Tool[]): AsyncGenerator<ResponseChunk>;
+  }
+  ```
+
+  This lets you swap models mid-conversation, test new providers, or even integrate local models running via Ollama or
+  llama.cpp with minimal friction.
+
+* **Context Window Management:** This is a critical, practical problem. A long conversation will exceed the LLM's
+  context limit.
+
+    * **Simple:** Use a "sliding window" approach like "Octo" does in `windowing.ts`. Keep only the last N tokens of
+      the conversation.
+    * **Advanced:** Implement a summarization strategy. For older parts of the conversation, use a cheaper/faster LLM
+      to create a summary and replace the original messages with it.
+    * **RAG (Retrieval-Augmented Generation):** For providing context about a large codebase, don't stuff entire files
+      into the prompt. Use vector embeddings (e.g., with `pgvector` or a library like `llamaindex`) to find the most relevant
+      code snippets for the user's current query and inject only those into the prompt.
+
+* **Self-Correction and Autofix:** "Octo's" use of a separate model to fix malformed JSON is brilliant. Expand on
+  this:
+
+    * **JSON Repair:** This is the most common use case. LLMs often produce JSON with trailing commas or missing
+      brackets.
+    * **Code Syntax Repair:** If a tool generates code (`edit` or `create`), you can have a "linter" step that uses an
+      LLM to fix basic syntax errors before writing to disk.
+    * **Search String Repair:** "Octo" does this for its `diff` edits. This is a great feature to prevent frustrating
+      "search text not found" errors.
+
+### 3\. Advanced Concepts & "Next Frontier" Ideas
+
+These are more speculative ideas that could give your tool a unique edge.
+
+* **Multi-Step Planning:** Instead of having the LLM emit one tool call at a time, prompt it to produce a full plan of
+  action as a JSON object (e.g., a list of steps with dependencies). Your agent then becomes an executor for this plan,
+  running the tools in sequence and feeding the results back for the next step. This dramatically increases autonomy.
+
+* **Sandboxed Execution Environment:** Running `bash` commands from an LLM directly on your machine is a massive
+  security risk.
+
+    * Use Docker to spin up a container for each session or command. The agent can only modify files inside the
+      container's volume mount.
+    * Explore WebAssembly (Wasm) as a secure, lightweight sandboxing target for running code or tools.
+
+* **GUI / Rich Interface:** While "Octo" is a great CLI app, a simple web UI or a VS Code extension could provide huge
+  value.
+
+    * Visualize the agent's plan as a graph.
+    * Provide rich diff viewers for proposed changes.
+    * Allow the user to directly edit the agent's proposed tool arguments before execution.
+
+### 4\. Technical Stack & Library Choices
+
+* **Language:** **TypeScript**. For a project of this complexity, type safety is not optional.
+* **CLI Framework:** **Ink** (like Octo) is great for rich, interactive UIs. For a more traditional CLI,
+  **Commander.js** or **Yargs** are standard.
+* **Schema & Validation:** **Zod** is the current industry standard and is excellent for parsing and validating
+  unpredictable LLM outputs. `structural` is also a fine choice.
+* **LLM Interaction:** The **Vercel AI SDK (`ai`)** is a strong starting point. It has built-in helpers for streaming,
+  tool usage, and supports multiple providers.
+
+### 5\. Product & SaaS Ideas
+
+If you're thinking of this as more than a personal project:
+
+* **The "Bring-Your-Own-Key" (BYOK) Model:** This is the easiest way to start. Users provide their own API keys, and
+  your tool is just the client-side orchestrator. You can sell the tool itself as a one-time purchase or a subscription.
+* **The Full SaaS Model:** You manage the API keys and bill users for usage (with a markup). This is more complex but
+  offers more value. You could provide premium features:
+    * **Hosted Sandboxes:** Users run their code in your secure, cloud-based environments.
+    * **Team Collaboration:** Shared sessions, toolsets, and prompts.
+    * **Specialized Fine-Tuned Models:** Offer your own fine-tuned "autofix" or planning models as a premium feature.
+
+Start with the core loop and a solid, modular tool system. The `FileTracker` and `autofix` ideas from "Octo" are
+high-impact features I'd prioritize next. Good luck.

env.example

@@ -1,3 +1,2 @@
-# Backend Configuration
-ENVIRONMENT=development
-PORT=3000
+OPENAI_API_KEY="OPENAI_AP_KEY"
+GOOGLE_API_KEY="GOOGLE_API_KEY"

nodemon.json

@@ -0,0 +1,6 @@
+{
+  "watch": ["src"],
+  "ext": "ts",
+  "ignore": ["src/**/*.test.ts", "dist", "node_modules", ".git", ".vscode"],
+  "exec": "tsx src/cli.ts"
+}

package.json

@@ -1,48 +1,46 @@
 {
-  "name": "template-typescript-project",
+  "name": "abi-coding-assistant",
   "version": "0.1.0",
-  "description": "A template for strict TypeScript projects.",
-  "main": "dist/backend/server.js",
+  "description": "An AI coding assistant.",
+  "main": "dist/cli.js",
   "type": "module",
   "scripts": {
-    "start": "node dist/backend/server.js",
-    "predev": "npm run build",
-    "dev": "concurrently \"tsc -w\" \"cpx \\\"src/frontend/public/**\\\" \\\"dist/frontend/public\\\" -w\" \"tsx watch src/backend/server.ts\" \"serve dist/frontend/public -l 5000\"",
-    "build": "rm -rf ./dist && tsc && cpx \"src/frontend/public/**\" \"dist/frontend/public\"",
+    "start": "node dist/cli.js",
+    "dev": "nodemon",
+    "build": "rm -rf ./dist && tsc",
     "test": "vitest run",
     "coverage": "vitest run --coverage",
     "test:watch": "vitest",
     "lint": "eslint . --ext .ts",
     "format": "prettier . --write",
     "typecheck": "tsc --noEmit"
   },
-  "keywords": [],
+  "keywords": [
+    "ai",
+    "typescript",
+    "cli"
+  ],
   "author": "",
   "license": "MIT",
   "dependencies": {
+    "@ai-sdk/openai": "^0.0.33",
+    "ai": "^3.2.16",
     "dotenv": "^16.4.5",
-    "express": "^4.19.2",
-    "express-async-errors": "^3.1.1"
+    "zod": "^3.23.8"
   },
   "devDependencies": {
-    "@types/express": "^4.17.21",
     "@types/node": "^20.14.9",
-    "@types/supertest": "^6.0.2",
     "@typescript-eslint/eslint-plugin": "^7.15.0",
     "@typescript-eslint/parser": "^7.15.0",
     "@vitest/coverage-v8": "^3.2.4",
-    "concurrently": "^9.2.0",
-    "cpx": "^1.5.0",
     "eslint": "^8.57.0",
     "eslint-config-prettier": "^9.1.0",
+    "nodemon": "^3.1.10",
     "prettier": "^3.3.2",
-    "serve": "^14.2.4",
-    "supertest": "^7.0.0",
     "tsx": "^4.16.2",
     "typescript": "^5.5.3",
     "vite-tsconfig-paths": "^4.3.2",
-    "vitest": "^3.2.4",
-    "zod": "^3.23.8"
+    "vitest": "^3.2.4"
   },
   "engines": {
     "node": ">=20.0.0"

src/agent/history.ts

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+// src/agent/history.ts
+import { z } from "zod";
+import { toolSchemas } from "./tools/index.js";
+
+// A Zod schema for any possible tool call
+export const toolCallSchema = z.discriminatedUnion("name", toolSchemas);
+export type ToolCall = z.infer<typeof toolCallSchema>;
+
+// Types for our conversation history
+export type UserMessage = {
+    role: "user";
+    content: string;
+};
+
+export type AssistantMessage = {
+    role: "assistant";
+    content: string;
+    toolCalls?: ToolCall[];
+};
+
+export type ToolResult = {
+    role: "tool";
+    toolCallId: string;
+    toolName: ToolCall["name"];
+    result: any;
+};
+
+export type Message = UserMessage | AssistantMessage | ToolResult;

src/agent/llm.ts

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+// src/agent/llm.ts
+import { openai } from "@ai-sdk/openai";
+import { generateText } from "ai";
+import "dotenv/config"; // Load .env file
+import { toolSchemas } from "./tools/index.js";
+
+export async function getNextAssistantResponse(history: any[]) {
+    // Dynamically build the tools object for the API call
+    const tools: Record<string, any> = {};
+    for (const schema of toolSchemas) {
+        const toolName = schema.shape.name.value;
+        tools[toolName] = {
+            description: `A tool for ${toolName}`, // Generic description
+            parameters: schema.shape.arguments,
+        };
+    }
+
+    // A more specific description for our readFile tool
+    if (tools.readFile) {
+        tools.readFile.description = "Reads the content of a file at a given path.";
+    }
+
+    return generateText({
+        model: openai("gpt-4-turbo"),
+        system: `You are a helpful AI assistant named Abi. You can use tools to help the user.`,
+        messages: history,
+        tools,
+    });
+}

src/agent/main.ts

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+// src/agent/main.ts
+import type { Message, AssistantMessage, ToolCall } from "./history.js";
+import { getNextAssistantResponse } from "./llm.js";
+import { toolImplementations } from "./tools/index.js";
+
+export async function run(history: Message[]) {
+    console.log("🤖 Abi is thinking...");
+
+    const response = await getNextAssistantResponse(history);
+
+    // If we have tool calls, execute them
+    if (response.toolCalls && response.toolCalls.length > 0) {
+        const assistantMessage: AssistantMessage = {
+            role: "assistant",
+            content: "",
+            toolCalls: [], // We will populate this below
+        };
+        history.push(assistantMessage);
+
+        for (const toolCall of response.toolCalls) {
+            // Re-structure the tool call to match our internal schema
+            const toolCallForHistory: ToolCall = {
+                name: toolCall.toolName as any,
+                arguments: toolCall.args,
+            };
+            assistantMessage.toolCalls?.push(toolCallForHistory);
+
+            const tool = toolImplementations[toolCall.toolName as keyof typeof toolImplementations];
+
+            if (!tool) {
+                console.error(`Unknown tool: ${toolCall.toolName}`);
+                continue;
+            }
+
+            console.log(`› Calling tool: ${toolCall.toolName} with args:`, toolCall.args);
+            const result = await tool(toolCall.args as any);
+
+            // Add tool result to history
+            history.push({
+                role: "tool",
+                toolCallId: toolCall.toolCallId,
+                toolName: toolCall.toolName as any,
+                result,
+            });
+        }
+
+        // Run the loop again with the tool results to get the final text response
+        await run(history);
+
+    } else if (response.text) {
+        // We have a final text response
+        console.log("🤖 Abi:", response.text);
+        history.push({ role: "assistant", content: response.text });
+    }
+}

src/agent/tools/index.ts

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+// src/agent/tools/index.ts
+import { readFile, readFileSchema } from "./readFile.js";
+
+// An array of Zod schemas for all our tools.
+// When you add a new tool, add its schema here.
+export const toolSchemas = [readFileSchema] as const;
+
+// A map of tool names to their actual functions.
+// When you add a new tool, add its implementation here.
+export const toolImplementations = {
+    readFile: readFile,
+};

src/agent/tools/readFile.ts

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+// src/agent/tools/readFile.ts
+import { z } from "zod";
+import fs from "fs/promises";
+
+export const readFileSchema = z.object({
+    name: z.literal("readFile"),
+    arguments: z.object({
+        path: z.string().describe("The path to the file to read."),
+    }),
+});
+
+export async function readFile(args: z.infer<typeof readFileSchema>["arguments"]): Promise<string> {
+    try {
+        const fileContent = await fs.readFile(args.path, "utf-8");
+        return fileContent;
+    } catch (error: any) {
+        return `Error reading file: ${error.message}`;
+    }
+}