feat: agents integration

packages/tfcode/src/agent/agent.ts

@@ -286,18 +286,18 @@ export namespace Agent {
 
           return {
             name: t.name,
-            description: t.description,
+            description: t.description ?? undefined,
             mode: "primary" as const,
             permission: Permission.fromConfig({ "*": "allow" }),
             native: false,
-            prompt: t.interpolation_string,
+            prompt: t.interpolation_string ?? undefined,
-            goals: t.goals,
+            goals: t.goals ?? undefined,
-            temperature: t.temperature,
+            temperature: t.temperature ?? undefined,
             model,
             options: {
               tf_agent_id: t.id,
               tf_auth_via: t.auth_via,
-              tf_max_tokens: t.max_tokens,
+              tf_max_tokens: t.max_tokens ?? undefined,
             },
           }
         })

packages/tfcode/src/cli/cmd/tui/context/local.tsx

@@ -34,6 +34,12 @@ export const { use: useLocal, provider: LocalProvider } = createSimpleContext({
       }
     }
 
+    const tfFallbackModel = createMemo(() => {
+      const m = { providerID: "toothfairyai", modelID: "mystica-15" }
+      if (isModelValid(m)) return m
+      return undefined
+    })
+
     const agent = iife(() => {
       const agents = createMemo(() => sync.data.agent.filter((x) => x.mode !== "subagent" && !x.hidden))
       const visibleAgents = createMemo(() => sync.data.agent.filter((x) => !x.hidden))
@@ -192,10 +198,12 @@ export const { use: useLocal, provider: LocalProvider } = createSimpleContext({
 
       const currentModel = createMemo(() => {
         const a = agent.current()
+        const isTFAgent = !!a.options?.tf_agent_id
         return (
           getFirstValidModel(
             () => modelStore.model[a.name],
             () => a.model,
+            () => (isTFAgent ? tfFallbackModel() : undefined),
             fallbackModel,
           ) ?? undefined
         )
@@ -297,6 +305,9 @@ export const { use: useLocal, provider: LocalProvider } = createSimpleContext({
             }
           })
         },
+        setDefault(model: { providerID: string; modelID: string }) {
+          setModelStore("model", agent.current().name, model)
+        },
         toggleFavorite(model: { providerID: string; modelID: string }) {
           batch(() => {
             if (!isModelValid(model)) {
@@ -381,18 +392,23 @@ export const { use: useLocal, provider: LocalProvider } = createSimpleContext({
     // Automatically update model when agent changes
     createEffect(() => {
       const value = agent.current()
+      const isTFAgent = !!value.options?.tf_agent_id
       if (value.model) {
-        if (isModelValid(value.model))
+        if (isModelValid(value.model)) {
           model.set({
             providerID: value.model.providerID,
             modelID: value.model.modelID,
           })
-        else
+        } else if (isTFAgent) {
+          // For TF agents with invalid model, force default to toothfairyai/mystica-15
+          model.setDefault({ providerID: "toothfairyai", modelID: "mystica-15" })
+        } else {
           toast.show({
             variant: "warning",
             message: `Agent ${value.name}'s configured model ${value.model.providerID}/${value.model.modelID} is not valid`,
             duration: 3000,
           })
+        }
       }
     })
 

packages/tfcode/src/session/llm.ts

@@ -116,11 +116,10 @@ export namespace LLM {
       mergeDeep(variant),
     )
 
-    // Remove TF-specific options for non-ToothFairyAI providers
+    // Remove TF-specific internal tracking fields (never passed to APIs)
-    if (input.model.providerID !== "toothfairyai") {
+    delete options.tf_agent_id
-      delete options.tf_agent_id
+    delete options.tf_auth_via
-      delete options.tf_auth_via
+    delete options.tf_max_tokens
-    }
 
     if (isOpenaiOauth) {
       options.instructions = system.join("\n")

packages/tfcode/test/agent/tf-agent.test.ts

@@ -1,6 +1,7 @@
 import { afterAll, beforeAll, beforeEach, afterEach, test, expect, describe } from "bun:test"
 import path from "path"
 import fs from "fs"
+import os from "os"
 import { tmpdir } from "../fixture/fixture"
 import { Instance } from "../../src/project/instance"
 import { Agent } from "../../src/agent/agent"
@@ -13,6 +14,8 @@ import { ProviderID, ModelID } from "../../src/provider/schema"
 import { SessionID, MessageID } from "../../src/session/schema"
 import type { MessageV2 } from "../../src/session/message-v2"
 
+const TF_TOOLS_PATH = path.join(os.homedir(), ".tfcode", "tools.json")
+
 // Server for capturing LLM requests
 const state = {
   server: null as ReturnType<typeof Bun.serve> | null,
@@ -75,17 +78,34 @@ async function loadFixture(providerID: string, modelID: string) {
 
 describe("ToothFairyAI Agent Loading", () => {
   let originalDataPath: string
-  
+  let originalToolsContent: string | null = null
+
   beforeEach(async () => {
     originalDataPath = Global.Path.data
     const testDataDir = path.join(path.dirname(originalDataPath), "tf-agent-test-data")
     ;(Global.Path as { data: string }).data = testDataDir
-    await fs.promises.mkdir(path.join(testDataDir, ".tfcode"), { recursive: true })
+
+    // Backup existing tools.json if it exists
+    try {
+      originalToolsContent = await Bun.file(TF_TOOLS_PATH).text()
+    } catch {
+      originalToolsContent = null
+    }
+    await fs.promises.mkdir(path.dirname(TF_TOOLS_PATH), { recursive: true })
   })
-  
+
   afterEach(async () => {
     await Instance.disposeAll()
     ;(Global.Path as { data: string }).data = originalDataPath
+
+    // Restore original tools.json
+    if (originalToolsContent !== null) {
+      await fs.promises.writeFile(TF_TOOLS_PATH, originalToolsContent)
+    } else {
+      try {
+        await fs.promises.unlink(TF_TOOLS_PATH)
+      } catch {}
+    }
   })
 
   describe("loadTFCoderAgents", () => {
@@ -126,8 +146,8 @@ describe("ToothFairyAI Agent Loading", () => {
         ],
         by_type: { coder_agent: 2 },
       }
-      
+
-      const toolsPath = path.join(Global.Path.data, ".tfcode", "tools.json")
+      const toolsPath = TF_TOOLS_PATH
       await fs.promises.writeFile(toolsPath, JSON.stringify(toolsData, null, 2))
 
       await using tmp = await tmpdir()
@@ -138,7 +158,7 @@ describe("ToothFairyAI Agent Loading", () => {
           const agents = await Agent.list()
           const codeReviewer = agents.find((a) => a.name === "Code Reviewer")
           const testWriter = agents.find((a) => a.name === "Test Writer")
-          
+
           expect(codeReviewer).toBeDefined()
           expect(codeReviewer?.description).toBe("Reviews code for quality")
           expect(codeReviewer?.prompt).toBe("You are a code reviewer. Review code thoroughly.")
@@ -150,7 +170,7 @@ describe("ToothFairyAI Agent Loading", () => {
           expect(codeReviewer?.options?.tf_max_tokens).toBe(4096)
           expect(String(codeReviewer?.model?.providerID)).toBe("toothfairyai")
           expect(String(codeReviewer?.model?.modelID)).toBe("claude-3-5-sonnet")
-          
+
           expect(testWriter).toBeDefined()
           expect(String(testWriter?.model?.providerID)).toBe("toothfairyai")
           expect(String(testWriter?.model?.modelID)).toBe("gpt-4")
@@ -176,8 +196,8 @@ describe("ToothFairyAI Agent Loading", () => {
           },
         ],
       }
-      
+
-      const toolsPath = path.join(Global.Path.data, ".tfcode", "tools.json")
+      const toolsPath = TF_TOOLS_PATH
       await fs.promises.writeFile(toolsPath, JSON.stringify(toolsData))
 
       await using tmp = await tmpdir()
@@ -210,8 +230,8 @@ describe("ToothFairyAI Agent Loading", () => {
           },
         ],
       }
-      
+
-      const toolsPath = path.join(Global.Path.data, ".tfcode", "tools.json")
+      const toolsPath = TF_TOOLS_PATH
       await fs.promises.writeFile(toolsPath, JSON.stringify(toolsData))
 
       await using tmp = await tmpdir()
@@ -243,8 +263,8 @@ describe("ToothFairyAI Agent Loading", () => {
           },
         ],
       }
-      
+
-      const toolsPath = path.join(Global.Path.data, ".tfcode", "tools.json")
+      const toolsPath = TF_TOOLS_PATH
       await fs.promises.writeFile(toolsPath, JSON.stringify(toolsData))
 
       await using tmp = await tmpdir()
@@ -254,8 +274,8 @@ describe("ToothFairyAI Agent Loading", () => {
         fn: async () => {
           const agent = await Agent.get("Minimal Agent")
           expect(agent).toBeDefined()
-          expect(agent?.prompt).toBeNull()
+          expect(agent?.prompt).toBeUndefined()
-          expect(agent?.goals).toBeNull()
+          expect(agent?.goals).toBeUndefined()
           expect(agent?.model).toBeUndefined()
         },
       })
@@ -266,7 +286,8 @@ describe("ToothFairyAI Agent Loading", () => {
 // Separate describe block for LLM stream tests
 describe("ToothFairyAI Agent Instructions in LLM Stream", () => {
   let originalDataPath: string
-  
+  let originalToolsContent: string | null = null
+
   beforeAll(() => {
     state.server = Bun.serve({
       port: 0,
@@ -281,32 +302,48 @@ describe("ToothFairyAI Agent Instructions in LLM Stream", () => {
       },
     })
   })
-  
+
   afterAll(() => {
     state.server?.stop()
   })
-  
+
   beforeEach(async () => {
     state.queue.length = 0
     originalDataPath = Global.Path.data
     const testDataDir = path.join(path.dirname(originalDataPath), "tf-agent-test-data")
     ;(Global.Path as { data: string }).data = testDataDir
-    await fs.promises.mkdir(path.join(testDataDir, ".tfcode"), { recursive: true })
+
+    // Backup existing tools.json if it exists
+    try {
+      originalToolsContent = await Bun.file(TF_TOOLS_PATH).text()
+    } catch {
+      originalToolsContent = null
+    }
+    await fs.promises.mkdir(path.dirname(TF_TOOLS_PATH), { recursive: true })
   })
-  
+
   afterEach(async () => {
     await Instance.disposeAll()
     ;(Global.Path as { data: string }).data = originalDataPath
+
+    // Restore original tools.json
+    if (originalToolsContent !== null) {
+      await fs.promises.writeFile(TF_TOOLS_PATH, originalToolsContent)
+    } else {
+      try {
+        await fs.promises.unlink(TF_TOOLS_PATH)
+      } catch {}
+    }
   })
-  
+
   test("includes highlighted TF agent instructions in system prompt", async () => {
     const server = state.server
     if (!server) throw new Error("Server not initialized")
-    
+
     const providerID = "alibaba"
     const modelID = "qwen-plus"
     const fixture = await loadFixture(providerID, modelID)
-    
+
     // Setup TF agent with this model
     const toolsData = {
       success: true,
@@ -317,7 +354,8 @@ describe("ToothFairyAI Agent Instructions in LLM Stream", () => {
           description: "Reviews code for quality and best practices",
           tool_type: "coder_agent",
           auth_via: "tf_agent",
-          interpolation_string: "You are a code reviewer. Always check for bugs, security issues, and suggest improvements.",
+          interpolation_string:
+            "You are a code reviewer. Always check for bugs, security issues, and suggest improvements.",
           goals: "Review all code thoroughly. Provide actionable feedback. Ensure code quality standards.",
           temperature: 0.3,
           max_tokens: 4096,
@@ -326,10 +364,9 @@ describe("ToothFairyAI Agent Instructions in LLM Stream", () => {
         },
       ],
     }
-    
+
-    const toolsPath = path.join(Global.Path.data, ".tfcode", "tools.json")
+    await fs.promises.writeFile(TF_TOOLS_PATH, JSON.stringify(toolsData, null, 2))
-    await fs.promises.writeFile(toolsPath, JSON.stringify(toolsData, null, 2))
+
-    
     const request = waitRequest(
       "/chat/completions",
       new Response(createChatStream("I'll review your code."), {
@@ -337,7 +374,7 @@ describe("ToothFairyAI Agent Instructions in LLM Stream", () => {
         headers: { "Content-Type": "text/event-stream" },
       }),
     )
-    
+
     await using tmp = await tmpdir({
       init: async (dir) => {
         await Bun.write(
@@ -357,13 +394,13 @@ describe("ToothFairyAI Agent Instructions in LLM Stream", () => {
         )
       },
     })
-    
+
     await Instance.provide({
       directory: tmp.path,
       fn: async () => {
         const agent = await Agent.get("Code Reviewer")
         expect(agent).toBeDefined()
-        
+
         const resolved = await Provider.getModel(ProviderID.make(providerID), ModelID.make(modelID))
         const sessionID = SessionID.make("test-session")
         const user: MessageV2.User = {
@@ -374,7 +411,7 @@ describe("ToothFairyAI Agent Instructions in LLM Stream", () => {
           agent: "Code Reviewer",
           model: { providerID: ProviderID.make(providerID), modelID: ModelID.make(modelID) },
         }
-        
+
         const stream = await LLM.stream({
           user,
           sessionID,
@@ -385,37 +422,42 @@ describe("ToothFairyAI Agent Instructions in LLM Stream", () => {
           messages: [{ role: "user", content: "Hello" }],
           tools: {},
         })
-        
+
-        for await (const _ of stream.fullStream) {}
+        for await (const _ of stream.fullStream) {
-        
+        }
+
         const capture = await request
         const body = capture.body
         const messages = body.messages as Array<{ role: string; content: string }>
-        
+
         const systemMessage = messages.find((m) => m.role === "system")
         expect(systemMessage).toBeDefined()
-        
+
         const systemContent = systemMessage!.content
-        
+
         expect(systemContent).toContain("ULTRA IMPORTANT - AGENT CONFIGURATION")
         expect(systemContent).toContain('You are acting as the agent: "Code Reviewer"')
         expect(systemContent).toContain("Reviews code for quality and best practices")
-        expect(systemContent).toContain("AGENT \"Code Reviewer\" INSTRUCTIONS")
+        expect(systemContent).toContain('AGENT "Code Reviewer" INSTRUCTIONS')
-        expect(systemContent).toContain("You are a code reviewer. Always check for bugs, security issues, and suggest improvements.")
+        expect(systemContent).toContain(
-        expect(systemContent).toContain("AGENT \"Code Reviewer\" GOALS")
+          "You are a code reviewer. Always check for bugs, security issues, and suggest improvements.",
-        expect(systemContent).toContain("Review all code thoroughly. Provide actionable feedback. Ensure code quality standards.")
+        )
+        expect(systemContent).toContain('AGENT "Code Reviewer" GOALS')
+        expect(systemContent).toContain(
+          "Review all code thoroughly. Provide actionable feedback. Ensure code quality standards.",
+        )
       },
     })
   })
-  
+
   test("does NOT include highlighted instructions for native agents", async () => {
     const server = state.server
     if (!server) throw new Error("Server not initialized")
-    
+
     const providerID = "alibaba"
     const modelID = "qwen-plus"
     const fixture = await loadFixture(providerID, modelID)
-    
+
     const request = waitRequest(
       "/chat/completions",
       new Response(createChatStream("Hello"), {
@@ -423,7 +465,7 @@ describe("ToothFairyAI Agent Instructions in LLM Stream", () => {
         headers: { "Content-Type": "text/event-stream" },
       }),
     )
-    
+
     await using tmp = await tmpdir({
       init: async (dir) => {
         await Bun.write(
@@ -443,14 +485,14 @@ describe("ToothFairyAI Agent Instructions in LLM Stream", () => {
         )
       },
     })
-    
+
     await Instance.provide({
       directory: tmp.path,
       fn: async () => {
         const agent = await Agent.get("build")
         expect(agent).toBeDefined()
         expect(agent?.native).toBe(true)
-        
+
         const resolved = await Provider.getModel(ProviderID.make(providerID), ModelID.make(modelID))
         const sessionID = SessionID.make("test-session")
         const user: MessageV2.User = {
@@ -461,7 +503,7 @@ describe("ToothFairyAI Agent Instructions in LLM Stream", () => {
           agent: "build",
           model: { providerID: ProviderID.make(providerID), modelID: ModelID.make(modelID) },
         }
-        
+
         const stream = await LLM.stream({
           user,
           sessionID,
@@ -472,18 +514,19 @@ describe("ToothFairyAI Agent Instructions in LLM Stream", () => {
           messages: [{ role: "user", content: "Hello" }],
           tools: {},
         })
-        
+
-        for await (const _ of stream.fullStream) {}
+        for await (const _ of stream.fullStream) {
-        
+        }
+
         const capture = await request
         const body = capture.body
         const messages = body.messages as Array<{ role: string; content: string }>
-        
+
         const systemMessage = messages.find((m) => m.role === "system")
         expect(systemMessage).toBeDefined()
-        
+
         const systemContent = systemMessage!.content
-        
+
         expect(systemContent).not.toContain("ULTRA IMPORTANT - AGENT CONFIGURATION")
         expect(systemContent).not.toContain("You are acting as the agent:")
       },