refactor: apply minimal tfcode branding

- Rename packages/opencode → packages/tfcode (directory only)
- Rename bin/opencode → bin/tfcode (CLI binary)
- Rename .opencode → .tfcode (config directory)
- Update package.json name and bin field
- Update config directory path references (.tfcode)
- Keep internal code references as 'opencode' for easy upstream sync
- Keep @opencode-ai/* workspace package names

This minimal branding approach allows clean merges from upstream
opencode repository while providing tfcode branding for users.

packages/tfcode/test/session/compaction.test.ts

@@ -0,0 +1,423 @@
+import { describe, expect, test } from "bun:test"
+import path from "path"
+import { SessionCompaction } from "../../src/session/compaction"
+import { Token } from "../../src/util/token"
+import { Instance } from "../../src/project/instance"
+import { Log } from "../../src/util/log"
+import { tmpdir } from "../fixture/fixture"
+import { Session } from "../../src/session"
+import type { Provider } from "../../src/provider/provider"
+
+Log.init({ print: false })
+
+function createModel(opts: {
+  context: number
+  output: number
+  input?: number
+  cost?: Provider.Model["cost"]
+  npm?: string
+}): Provider.Model {
+  return {
+    id: "test-model",
+    providerID: "test",
+    name: "Test",
+    limit: {
+      context: opts.context,
+      input: opts.input,
+      output: opts.output,
+    },
+    cost: opts.cost ?? { input: 0, output: 0, cache: { read: 0, write: 0 } },
+    capabilities: {
+      toolcall: true,
+      attachment: false,
+      reasoning: false,
+      temperature: true,
+      input: { text: true, image: false, audio: false, video: false },
+      output: { text: true, image: false, audio: false, video: false },
+    },
+    api: { npm: opts.npm ?? "@ai-sdk/anthropic" },
+    options: {},
+  } as Provider.Model
+}
+
+describe("session.compaction.isOverflow", () => {
+  test("returns true when token count exceeds usable context", async () => {
+    await using tmp = await tmpdir()
+    await Instance.provide({
+      directory: tmp.path,
+      fn: async () => {
+        const model = createModel({ context: 100_000, output: 32_000 })
+        const tokens = { input: 75_000, output: 5_000, reasoning: 0, cache: { read: 0, write: 0 } }
+        expect(await SessionCompaction.isOverflow({ tokens, model })).toBe(true)
+      },
+    })
+  })
+
+  test("returns false when token count within usable context", async () => {
+    await using tmp = await tmpdir()
+    await Instance.provide({
+      directory: tmp.path,
+      fn: async () => {
+        const model = createModel({ context: 200_000, output: 32_000 })
+        const tokens = { input: 100_000, output: 10_000, reasoning: 0, cache: { read: 0, write: 0 } }
+        expect(await SessionCompaction.isOverflow({ tokens, model })).toBe(false)
+      },
+    })
+  })
+
+  test("includes cache.read in token count", async () => {
+    await using tmp = await tmpdir()
+    await Instance.provide({
+      directory: tmp.path,
+      fn: async () => {
+        const model = createModel({ context: 100_000, output: 32_000 })
+        const tokens = { input: 60_000, output: 10_000, reasoning: 0, cache: { read: 10_000, write: 0 } }
+        expect(await SessionCompaction.isOverflow({ tokens, model })).toBe(true)
+      },
+    })
+  })
+
+  test("respects input limit for input caps", async () => {
+    await using tmp = await tmpdir()
+    await Instance.provide({
+      directory: tmp.path,
+      fn: async () => {
+        const model = createModel({ context: 400_000, input: 272_000, output: 128_000 })
+        const tokens = { input: 271_000, output: 1_000, reasoning: 0, cache: { read: 2_000, write: 0 } }
+        expect(await SessionCompaction.isOverflow({ tokens, model })).toBe(true)
+      },
+    })
+  })
+
+  test("returns false when input/output are within input caps", async () => {
+    await using tmp = await tmpdir()
+    await Instance.provide({
+      directory: tmp.path,
+      fn: async () => {
+        const model = createModel({ context: 400_000, input: 272_000, output: 128_000 })
+        const tokens = { input: 200_000, output: 20_000, reasoning: 0, cache: { read: 10_000, write: 0 } }
+        expect(await SessionCompaction.isOverflow({ tokens, model })).toBe(false)
+      },
+    })
+  })
+
+  test("returns false when output within limit with input caps", async () => {
+    await using tmp = await tmpdir()
+    await Instance.provide({
+      directory: tmp.path,
+      fn: async () => {
+        const model = createModel({ context: 200_000, input: 120_000, output: 10_000 })
+        const tokens = { input: 50_000, output: 9_999, reasoning: 0, cache: { read: 0, write: 0 } }
+        expect(await SessionCompaction.isOverflow({ tokens, model })).toBe(false)
+      },
+    })
+  })
+
+  // ─── Bug reproduction tests ───────────────────────────────────────────
+  // These tests demonstrate that when limit.input is set, isOverflow()
+  // does not subtract any headroom for the next model response. This means
+  // compaction only triggers AFTER we've already consumed the full input
+  // budget, leaving zero room for the next API call's output tokens.
+  //
+  // Compare: without limit.input, usable = context - output (reserves space).
+  // With limit.input, usable = limit.input (reserves nothing).
+  //
+  // Related issues: #10634, #8089, #11086, #12621
+  // Open PRs: #6875, #12924
+
+  test("BUG: no headroom when limit.input is set — compaction should trigger near boundary but does not", async () => {
+    await using tmp = await tmpdir()
+    await Instance.provide({
+      directory: tmp.path,
+      fn: async () => {
+        // Simulate Claude with prompt caching: input limit = 200K, output limit = 32K
+        const model = createModel({ context: 200_000, input: 200_000, output: 32_000 })
+
+        // We've used 198K tokens total. Only 2K under the input limit.
+        // On the next turn, the full conversation (198K) becomes input,
+        // plus the model needs room to generate output — this WILL overflow.
+        const tokens = { input: 180_000, output: 15_000, reasoning: 0, cache: { read: 3_000, write: 0 } }
+        // count = 180K + 3K + 15K = 198K
+        // usable = limit.input = 200K (no output subtracted!)
+        // 198K > 200K = false → no compaction triggered
+
+        // WITHOUT limit.input: usable = 200K - 32K = 168K, and 198K > 168K = true ✓
+        // WITH limit.input: usable = 200K, and 198K > 200K = false ✗
+
+        // With 198K used and only 2K headroom, the next turn will overflow.
+        // Compaction MUST trigger here.
+        expect(await SessionCompaction.isOverflow({ tokens, model })).toBe(true)
+      },
+    })
+  })
+
+  test("BUG: without limit.input, same token count correctly triggers compaction", async () => {
+    await using tmp = await tmpdir()
+    await Instance.provide({
+      directory: tmp.path,
+      fn: async () => {
+        // Same model but without limit.input — uses context - output instead
+        const model = createModel({ context: 200_000, output: 32_000 })
+
+        // Same token usage as above
+        const tokens = { input: 180_000, output: 15_000, reasoning: 0, cache: { read: 3_000, write: 0 } }
+        // count = 198K
+        // usable = context - output = 200K - 32K = 168K
+        // 198K > 168K = true → compaction correctly triggered
+
+        const result = await SessionCompaction.isOverflow({ tokens, model })
+        expect(result).toBe(true) // ← Correct: headroom is reserved
+      },
+    })
+  })
+
+  test("BUG: asymmetry — limit.input model allows 30K more usage before compaction than equivalent model without it", async () => {
+    await using tmp = await tmpdir()
+    await Instance.provide({
+      directory: tmp.path,
+      fn: async () => {
+        // Two models with identical context/output limits, differing only in limit.input
+        const withInputLimit = createModel({ context: 200_000, input: 200_000, output: 32_000 })
+        const withoutInputLimit = createModel({ context: 200_000, output: 32_000 })
+
+        // 170K total tokens — well above context-output (168K) but below input limit (200K)
+        const tokens = { input: 166_000, output: 10_000, reasoning: 0, cache: { read: 5_000, write: 0 } }
+
+        const withLimit = await SessionCompaction.isOverflow({ tokens, model: withInputLimit })
+        const withoutLimit = await SessionCompaction.isOverflow({ tokens, model: withoutInputLimit })
+
+        // Both models have identical real capacity — they should agree:
+        expect(withLimit).toBe(true) // should compact (170K leaves no room for 32K output)
+        expect(withoutLimit).toBe(true) // correctly compacts (170K > 168K)
+      },
+    })
+  })
+
+  test("returns false when model context limit is 0", async () => {
+    await using tmp = await tmpdir()
+    await Instance.provide({
+      directory: tmp.path,
+      fn: async () => {
+        const model = createModel({ context: 0, output: 32_000 })
+        const tokens = { input: 100_000, output: 10_000, reasoning: 0, cache: { read: 0, write: 0 } }
+        expect(await SessionCompaction.isOverflow({ tokens, model })).toBe(false)
+      },
+    })
+  })
+
+  test("returns false when compaction.auto is disabled", async () => {
+    await using tmp = await tmpdir({
+      init: async (dir) => {
+        await Bun.write(
+          path.join(dir, "opencode.json"),
+          JSON.stringify({
+            compaction: { auto: false },
+          }),
+        )
+      },
+    })
+    await Instance.provide({
+      directory: tmp.path,
+      fn: async () => {
+        const model = createModel({ context: 100_000, output: 32_000 })
+        const tokens = { input: 75_000, output: 5_000, reasoning: 0, cache: { read: 0, write: 0 } }
+        expect(await SessionCompaction.isOverflow({ tokens, model })).toBe(false)
+      },
+    })
+  })
+})
+
+describe("util.token.estimate", () => {
+  test("estimates tokens from text (4 chars per token)", () => {
+    const text = "x".repeat(4000)
+    expect(Token.estimate(text)).toBe(1000)
+  })
+
+  test("estimates tokens from larger text", () => {
+    const text = "y".repeat(20_000)
+    expect(Token.estimate(text)).toBe(5000)
+  })
+
+  test("returns 0 for empty string", () => {
+    expect(Token.estimate("")).toBe(0)
+  })
+})
+
+describe("session.getUsage", () => {
+  test("normalizes standard usage to token format", () => {
+    const model = createModel({ context: 100_000, output: 32_000 })
+    const result = Session.getUsage({
+      model,
+      usage: {
+        inputTokens: 1000,
+        outputTokens: 500,
+        totalTokens: 1500,
+      },
+    })
+
+    expect(result.tokens.input).toBe(1000)
+    expect(result.tokens.output).toBe(500)
+    expect(result.tokens.reasoning).toBe(0)
+    expect(result.tokens.cache.read).toBe(0)
+    expect(result.tokens.cache.write).toBe(0)
+  })
+
+  test("extracts cached tokens to cache.read", () => {
+    const model = createModel({ context: 100_000, output: 32_000 })
+    const result = Session.getUsage({
+      model,
+      usage: {
+        inputTokens: 1000,
+        outputTokens: 500,
+        totalTokens: 1500,
+        cachedInputTokens: 200,
+      },
+    })
+
+    expect(result.tokens.input).toBe(800)
+    expect(result.tokens.cache.read).toBe(200)
+  })
+
+  test("handles anthropic cache write metadata", () => {
+    const model = createModel({ context: 100_000, output: 32_000 })
+    const result = Session.getUsage({
+      model,
+      usage: {
+        inputTokens: 1000,
+        outputTokens: 500,
+        totalTokens: 1500,
+      },
+      metadata: {
+        anthropic: {
+          cacheCreationInputTokens: 300,
+        },
+      },
+    })
+
+    expect(result.tokens.cache.write).toBe(300)
+  })
+
+  test("does not subtract cached tokens for anthropic provider", () => {
+    const model = createModel({ context: 100_000, output: 32_000 })
+    const result = Session.getUsage({
+      model,
+      usage: {
+        inputTokens: 1000,
+        outputTokens: 500,
+        totalTokens: 1500,
+        cachedInputTokens: 200,
+      },
+      metadata: {
+        anthropic: {},
+      },
+    })
+
+    expect(result.tokens.input).toBe(1000)
+    expect(result.tokens.cache.read).toBe(200)
+  })
+
+  test("handles reasoning tokens", () => {
+    const model = createModel({ context: 100_000, output: 32_000 })
+    const result = Session.getUsage({
+      model,
+      usage: {
+        inputTokens: 1000,
+        outputTokens: 500,
+        totalTokens: 1500,
+        reasoningTokens: 100,
+      },
+    })
+
+    expect(result.tokens.reasoning).toBe(100)
+  })
+
+  test("handles undefined optional values gracefully", () => {
+    const model = createModel({ context: 100_000, output: 32_000 })
+    const result = Session.getUsage({
+      model,
+      usage: {
+        inputTokens: 0,
+        outputTokens: 0,
+        totalTokens: 0,
+      },
+    })
+
+    expect(result.tokens.input).toBe(0)
+    expect(result.tokens.output).toBe(0)
+    expect(result.tokens.reasoning).toBe(0)
+    expect(result.tokens.cache.read).toBe(0)
+    expect(result.tokens.cache.write).toBe(0)
+    expect(Number.isNaN(result.cost)).toBe(false)
+  })
+
+  test("calculates cost correctly", () => {
+    const model = createModel({
+      context: 100_000,
+      output: 32_000,
+      cost: {
+        input: 3,
+        output: 15,
+        cache: { read: 0.3, write: 3.75 },
+      },
+    })
+    const result = Session.getUsage({
+      model,
+      usage: {
+        inputTokens: 1_000_000,
+        outputTokens: 100_000,
+        totalTokens: 1_100_000,
+      },
+    })
+
+    expect(result.cost).toBe(3 + 1.5)
+  })
+
+  test.each(["@ai-sdk/anthropic", "@ai-sdk/amazon-bedrock", "@ai-sdk/google-vertex/anthropic"])(
+    "computes total from components for %s models",
+    (npm) => {
+      const model = createModel({ context: 100_000, output: 32_000, npm })
+      const usage = {
+        inputTokens: 1000,
+        outputTokens: 500,
+        // These providers typically report total as input + output only,
+        // excluding cache read/write.
+        totalTokens: 1500,
+        cachedInputTokens: 200,
+      }
+      if (npm === "@ai-sdk/amazon-bedrock") {
+        const result = Session.getUsage({
+          model,
+          usage,
+          metadata: {
+            bedrock: {
+              usage: {
+                cacheWriteInputTokens: 300,
+              },
+            },
+          },
+        })
+
+        expect(result.tokens.input).toBe(1000)
+        expect(result.tokens.cache.read).toBe(200)
+        expect(result.tokens.cache.write).toBe(300)
+        expect(result.tokens.total).toBe(2000)
+        return
+      }
+
+      const result = Session.getUsage({
+        model,
+        usage,
+        metadata: {
+          anthropic: {
+            cacheCreationInputTokens: 300,
+          },
+        },
+      })
+
+      expect(result.tokens.input).toBe(1000)
+      expect(result.tokens.cache.read).toBe(200)
+      expect(result.tokens.cache.write).toBe(300)
+      expect(result.tokens.total).toBe(2000)
+    },
+  )
+})

packages/tfcode/test/session/instruction.test.ts

@@ -0,0 +1,170 @@
+import { afterEach, beforeEach, describe, expect, test } from "bun:test"
+import path from "path"
+import { InstructionPrompt } from "../../src/session/instruction"
+import { Instance } from "../../src/project/instance"
+import { Global } from "../../src/global"
+import { tmpdir } from "../fixture/fixture"
+
+describe("InstructionPrompt.resolve", () => {
+  test("returns empty when AGENTS.md is at project root (already in systemPaths)", async () => {
+    await using tmp = await tmpdir({
+      init: async (dir) => {
+        await Bun.write(path.join(dir, "AGENTS.md"), "# Root Instructions")
+        await Bun.write(path.join(dir, "src", "file.ts"), "const x = 1")
+      },
+    })
+    await Instance.provide({
+      directory: tmp.path,
+      fn: async () => {
+        const system = await InstructionPrompt.systemPaths()
+        expect(system.has(path.join(tmp.path, "AGENTS.md"))).toBe(true)
+
+        const results = await InstructionPrompt.resolve([], path.join(tmp.path, "src", "file.ts"), "test-message-1")
+        expect(results).toEqual([])
+      },
+    })
+  })
+
+  test("returns AGENTS.md from subdirectory (not in systemPaths)", async () => {
+    await using tmp = await tmpdir({
+      init: async (dir) => {
+        await Bun.write(path.join(dir, "subdir", "AGENTS.md"), "# Subdir Instructions")
+        await Bun.write(path.join(dir, "subdir", "nested", "file.ts"), "const x = 1")
+      },
+    })
+    await Instance.provide({
+      directory: tmp.path,
+      fn: async () => {
+        const system = await InstructionPrompt.systemPaths()
+        expect(system.has(path.join(tmp.path, "subdir", "AGENTS.md"))).toBe(false)
+
+        const results = await InstructionPrompt.resolve(
+          [],
+          path.join(tmp.path, "subdir", "nested", "file.ts"),
+          "test-message-2",
+        )
+        expect(results.length).toBe(1)
+        expect(results[0].filepath).toBe(path.join(tmp.path, "subdir", "AGENTS.md"))
+      },
+    })
+  })
+
+  test("doesn't reload AGENTS.md when reading it directly", async () => {
+    await using tmp = await tmpdir({
+      init: async (dir) => {
+        await Bun.write(path.join(dir, "subdir", "AGENTS.md"), "# Subdir Instructions")
+        await Bun.write(path.join(dir, "subdir", "nested", "file.ts"), "const x = 1")
+      },
+    })
+    await Instance.provide({
+      directory: tmp.path,
+      fn: async () => {
+        const filepath = path.join(tmp.path, "subdir", "AGENTS.md")
+        const system = await InstructionPrompt.systemPaths()
+        expect(system.has(filepath)).toBe(false)
+
+        const results = await InstructionPrompt.resolve([], filepath, "test-message-2")
+        expect(results).toEqual([])
+      },
+    })
+  })
+})
+
+describe("InstructionPrompt.systemPaths OPENCODE_CONFIG_DIR", () => {
+  let originalConfigDir: string | undefined
+
+  beforeEach(() => {
+    originalConfigDir = process.env["OPENCODE_CONFIG_DIR"]
+  })
+
+  afterEach(() => {
+    if (originalConfigDir === undefined) {
+      delete process.env["OPENCODE_CONFIG_DIR"]
+    } else {
+      process.env["OPENCODE_CONFIG_DIR"] = originalConfigDir
+    }
+  })
+
+  test("prefers OPENCODE_CONFIG_DIR AGENTS.md over global when both exist", async () => {
+    await using profileTmp = await tmpdir({
+      init: async (dir) => {
+        await Bun.write(path.join(dir, "AGENTS.md"), "# Profile Instructions")
+      },
+    })
+    await using globalTmp = await tmpdir({
+      init: async (dir) => {
+        await Bun.write(path.join(dir, "AGENTS.md"), "# Global Instructions")
+      },
+    })
+    await using projectTmp = await tmpdir()
+
+    process.env["OPENCODE_CONFIG_DIR"] = profileTmp.path
+    const originalGlobalConfig = Global.Path.config
+    ;(Global.Path as { config: string }).config = globalTmp.path
+
+    try {
+      await Instance.provide({
+        directory: projectTmp.path,
+        fn: async () => {
+          const paths = await InstructionPrompt.systemPaths()
+          expect(paths.has(path.join(profileTmp.path, "AGENTS.md"))).toBe(true)
+          expect(paths.has(path.join(globalTmp.path, "AGENTS.md"))).toBe(false)
+        },
+      })
+    } finally {
+      ;(Global.Path as { config: string }).config = originalGlobalConfig
+    }
+  })
+
+  test("falls back to global AGENTS.md when OPENCODE_CONFIG_DIR has no AGENTS.md", async () => {
+    await using profileTmp = await tmpdir()
+    await using globalTmp = await tmpdir({
+      init: async (dir) => {
+        await Bun.write(path.join(dir, "AGENTS.md"), "# Global Instructions")
+      },
+    })
+    await using projectTmp = await tmpdir()
+
+    process.env["OPENCODE_CONFIG_DIR"] = profileTmp.path
+    const originalGlobalConfig = Global.Path.config
+    ;(Global.Path as { config: string }).config = globalTmp.path
+
+    try {
+      await Instance.provide({
+        directory: projectTmp.path,
+        fn: async () => {
+          const paths = await InstructionPrompt.systemPaths()
+          expect(paths.has(path.join(profileTmp.path, "AGENTS.md"))).toBe(false)
+          expect(paths.has(path.join(globalTmp.path, "AGENTS.md"))).toBe(true)
+        },
+      })
+    } finally {
+      ;(Global.Path as { config: string }).config = originalGlobalConfig
+    }
+  })
+
+  test("uses global AGENTS.md when OPENCODE_CONFIG_DIR is not set", async () => {
+    await using globalTmp = await tmpdir({
+      init: async (dir) => {
+        await Bun.write(path.join(dir, "AGENTS.md"), "# Global Instructions")
+      },
+    })
+    await using projectTmp = await tmpdir()
+
+    delete process.env["OPENCODE_CONFIG_DIR"]
+    const originalGlobalConfig = Global.Path.config
+    ;(Global.Path as { config: string }).config = globalTmp.path
+
+    try {
+      await Instance.provide({
+        directory: projectTmp.path,
+        fn: async () => {
+          const paths = await InstructionPrompt.systemPaths()
+          expect(paths.has(path.join(globalTmp.path, "AGENTS.md"))).toBe(true)
+        },
+      })
+    } finally {
+      ;(Global.Path as { config: string }).config = originalGlobalConfig
+    }
+  })
+})

packages/tfcode/test/session/llm.test.ts

@@ -0,0 +1,759 @@
+import { afterAll, beforeAll, beforeEach, describe, expect, test } from "bun:test"
+import path from "path"
+import { tool, type ModelMessage } from "ai"
+import z from "zod"
+import { LLM } from "../../src/session/llm"
+import { Global } from "../../src/global"
+import { Instance } from "../../src/project/instance"
+import { Provider } from "../../src/provider/provider"
+import { ProviderTransform } from "../../src/provider/transform"
+import { ModelsDev } from "../../src/provider/models"
+import { ProviderID, ModelID } from "../../src/provider/schema"
+import { Filesystem } from "../../src/util/filesystem"
+import { tmpdir } from "../fixture/fixture"
+import type { Agent } from "../../src/agent/agent"
+import type { MessageV2 } from "../../src/session/message-v2"
+import { SessionID, MessageID } from "../../src/session/schema"
+
+describe("session.llm.hasToolCalls", () => {
+  test("returns false for empty messages array", () => {
+    expect(LLM.hasToolCalls([])).toBe(false)
+  })
+
+  test("returns false for messages with only text content", () => {
+    const messages: ModelMessage[] = [
+      {
+        role: "user",
+        content: [{ type: "text", text: "Hello" }],
+      },
+      {
+        role: "assistant",
+        content: [{ type: "text", text: "Hi there" }],
+      },
+    ]
+    expect(LLM.hasToolCalls(messages)).toBe(false)
+  })
+
+  test("returns true when messages contain tool-call", () => {
+    const messages = [
+      {
+        role: "user",
+        content: [{ type: "text", text: "Run a command" }],
+      },
+      {
+        role: "assistant",
+        content: [
+          {
+            type: "tool-call",
+            toolCallId: "call-123",
+            toolName: "bash",
+          },
+        ],
+      },
+    ] as ModelMessage[]
+    expect(LLM.hasToolCalls(messages)).toBe(true)
+  })
+
+  test("returns true when messages contain tool-result", () => {
+    const messages = [
+      {
+        role: "tool",
+        content: [
+          {
+            type: "tool-result",
+            toolCallId: "call-123",
+            toolName: "bash",
+          },
+        ],
+      },
+    ] as ModelMessage[]
+    expect(LLM.hasToolCalls(messages)).toBe(true)
+  })
+
+  test("returns false for messages with string content", () => {
+    const messages: ModelMessage[] = [
+      {
+        role: "user",
+        content: "Hello world",
+      },
+      {
+        role: "assistant",
+        content: "Hi there",
+      },
+    ]
+    expect(LLM.hasToolCalls(messages)).toBe(false)
+  })
+
+  test("returns true when tool-call is mixed with text content", () => {
+    const messages = [
+      {
+        role: "assistant",
+        content: [
+          { type: "text", text: "Let me run that command" },
+          {
+            type: "tool-call",
+            toolCallId: "call-456",
+            toolName: "read",
+          },
+        ],
+      },
+    ] as ModelMessage[]
+    expect(LLM.hasToolCalls(messages)).toBe(true)
+  })
+})
+
+type Capture = {
+  url: URL
+  headers: Headers
+  body: Record<string, unknown>
+}
+
+const state = {
+  server: null as ReturnType<typeof Bun.serve> | null,
+  queue: [] as Array<{ path: string; response: Response; resolve: (value: Capture) => void }>,
+}
+
+function deferred<T>() {
+  const result = {} as { promise: Promise<T>; resolve: (value: T) => void }
+  result.promise = new Promise((resolve) => {
+    result.resolve = resolve
+  })
+  return result
+}
+
+function waitRequest(pathname: string, response: Response) {
+  const pending = deferred<Capture>()
+  state.queue.push({ path: pathname, response, resolve: pending.resolve })
+  return pending.promise
+}
+
+beforeAll(() => {
+  state.server = Bun.serve({
+    port: 0,
+    async fetch(req) {
+      const next = state.queue.shift()
+      if (!next) {
+        return new Response("unexpected request", { status: 500 })
+      }
+
+      const url = new URL(req.url)
+      const body = (await req.json()) as Record<string, unknown>
+      next.resolve({ url, headers: req.headers, body })
+
+      if (!url.pathname.endsWith(next.path)) {
+        return new Response("not found", { status: 404 })
+      }
+
+      return next.response
+    },
+  })
+})
+
+beforeEach(() => {
+  state.queue.length = 0
+})
+
+afterAll(() => {
+  state.server?.stop()
+})
+
+function createChatStream(text: string) {
+  const payload =
+    [
+      `data: ${JSON.stringify({
+        id: "chatcmpl-1",
+        object: "chat.completion.chunk",
+        choices: [{ delta: { role: "assistant" } }],
+      })}`,
+      `data: ${JSON.stringify({
+        id: "chatcmpl-1",
+        object: "chat.completion.chunk",
+        choices: [{ delta: { content: text } }],
+      })}`,
+      `data: ${JSON.stringify({
+        id: "chatcmpl-1",
+        object: "chat.completion.chunk",
+        choices: [{ delta: {}, finish_reason: "stop" }],
+      })}`,
+      "data: [DONE]",
+    ].join("\n\n") + "\n\n"
+
+  const encoder = new TextEncoder()
+  return new ReadableStream<Uint8Array>({
+    start(controller) {
+      controller.enqueue(encoder.encode(payload))
+      controller.close()
+    },
+  })
+}
+
+async function loadFixture(providerID: string, modelID: string) {
+  const fixturePath = path.join(import.meta.dir, "../tool/fixtures/models-api.json")
+  const data = await Filesystem.readJson<Record<string, ModelsDev.Provider>>(fixturePath)
+  const provider = data[providerID]
+  if (!provider) {
+    throw new Error(`Missing provider in fixture: ${providerID}`)
+  }
+  const model = provider.models[modelID]
+  if (!model) {
+    throw new Error(`Missing model in fixture: ${modelID}`)
+  }
+  return { provider, model }
+}
+
+function createEventStream(chunks: unknown[], includeDone = false) {
+  const lines = chunks.map((chunk) => `data: ${typeof chunk === "string" ? chunk : JSON.stringify(chunk)}`)
+  if (includeDone) {
+    lines.push("data: [DONE]")
+  }
+  const payload = lines.join("\n\n") + "\n\n"
+  const encoder = new TextEncoder()
+  return new ReadableStream<Uint8Array>({
+    start(controller) {
+      controller.enqueue(encoder.encode(payload))
+      controller.close()
+    },
+  })
+}
+
+function createEventResponse(chunks: unknown[], includeDone = false) {
+  return new Response(createEventStream(chunks, includeDone), {
+    status: 200,
+    headers: { "Content-Type": "text/event-stream" },
+  })
+}
+
+describe("session.llm.stream", () => {
+  test("sends temperature, tokens, and reasoning options for openai-compatible models", 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 provider = fixture.provider
+    const model = fixture.model
+
+    const request = waitRequest(
+      "/chat/completions",
+      new Response(createChatStream("Hello"), {
+        status: 200,
+        headers: { "Content-Type": "text/event-stream" },
+      }),
+    )
+
+    await using tmp = await tmpdir({
+      init: async (dir) => {
+        await Bun.write(
+          path.join(dir, "opencode.json"),
+          JSON.stringify({
+            $schema: "https://opencode.ai/config.json",
+            enabled_providers: [providerID],
+            provider: {
+              [providerID]: {
+                options: {
+                  apiKey: "test-key",
+                  baseURL: `${server.url.origin}/v1`,
+                },
+              },
+            },
+          }),
+        )
+      },
+    })
+
+    await Instance.provide({
+      directory: tmp.path,
+      fn: async () => {
+        const resolved = await Provider.getModel(ProviderID.make(providerID), ModelID.make(model.id))
+        const sessionID = SessionID.make("session-test-1")
+        const agent = {
+          name: "test",
+          mode: "primary",
+          options: {},
+          permission: [{ permission: "*", pattern: "*", action: "allow" }],
+          temperature: 0.4,
+          topP: 0.8,
+        } satisfies Agent.Info
+
+        const user = {
+          id: MessageID.make("user-1"),
+          sessionID,
+          role: "user",
+          time: { created: Date.now() },
+          agent: agent.name,
+          model: { providerID: ProviderID.make(providerID), modelID: resolved.id },
+          variant: "high",
+        } satisfies MessageV2.User
+
+        const stream = await LLM.stream({
+          user,
+          sessionID,
+          model: resolved,
+          agent,
+          system: ["You are a helpful assistant."],
+          abort: new AbortController().signal,
+          messages: [{ role: "user", content: "Hello" }],
+          tools: {},
+        })
+
+        for await (const _ of stream.fullStream) {
+        }
+
+        const capture = await request
+        const body = capture.body
+        const headers = capture.headers
+        const url = capture.url
+
+        expect(url.pathname.startsWith("/v1/")).toBe(true)
+        expect(url.pathname.endsWith("/chat/completions")).toBe(true)
+        expect(headers.get("Authorization")).toBe("Bearer test-key")
+
+        expect(body.model).toBe(resolved.api.id)
+        expect(body.temperature).toBe(0.4)
+        expect(body.top_p).toBe(0.8)
+        expect(body.stream).toBe(true)
+
+        const maxTokens = (body.max_tokens as number | undefined) ?? (body.max_output_tokens as number | undefined)
+        const expectedMaxTokens = ProviderTransform.maxOutputTokens(resolved)
+        expect(maxTokens).toBe(expectedMaxTokens)
+
+        const reasoning = (body.reasoningEffort as string | undefined) ?? (body.reasoning_effort as string | undefined)
+        expect(reasoning).toBe("high")
+      },
+    })
+  })
+
+  test("keeps tools enabled by prompt permissions", 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 model = fixture.model
+
+    const request = waitRequest(
+      "/chat/completions",
+      new Response(createChatStream("Hello"), {
+        status: 200,
+        headers: { "Content-Type": "text/event-stream" },
+      }),
+    )
+
+    await using tmp = await tmpdir({
+      init: async (dir) => {
+        await Bun.write(
+          path.join(dir, "opencode.json"),
+          JSON.stringify({
+            $schema: "https://opencode.ai/config.json",
+            enabled_providers: [providerID],
+            provider: {
+              [providerID]: {
+                options: {
+                  apiKey: "test-key",
+                  baseURL: `${server.url.origin}/v1`,
+                },
+              },
+            },
+          }),
+        )
+      },
+    })
+
+    await Instance.provide({
+      directory: tmp.path,
+      fn: async () => {
+        const resolved = await Provider.getModel(ProviderID.make(providerID), ModelID.make(model.id))
+        const sessionID = SessionID.make("session-test-tools")
+        const agent = {
+          name: "test",
+          mode: "primary",
+          options: {},
+          permission: [{ permission: "question", pattern: "*", action: "deny" }],
+        } satisfies Agent.Info
+
+        const user = {
+          id: MessageID.make("user-tools"),
+          sessionID,
+          role: "user",
+          time: { created: Date.now() },
+          agent: agent.name,
+          model: { providerID: ProviderID.make(providerID), modelID: resolved.id },
+          tools: { question: true },
+        } satisfies MessageV2.User
+
+        const stream = await LLM.stream({
+          user,
+          sessionID,
+          model: resolved,
+          agent,
+          permission: [{ permission: "question", pattern: "*", action: "allow" }],
+          system: ["You are a helpful assistant."],
+          abort: new AbortController().signal,
+          messages: [{ role: "user", content: "Hello" }],
+          tools: {
+            question: tool({
+              description: "Ask a question",
+              inputSchema: z.object({}),
+              execute: async () => ({ output: "" }),
+            }),
+          },
+        })
+
+        for await (const _ of stream.fullStream) {
+        }
+
+        const capture = await request
+        const tools = capture.body.tools as Array<{ function?: { name?: string } }> | undefined
+        expect(tools?.some((item) => item.function?.name === "question")).toBe(true)
+      },
+    })
+  })
+
+  test("sends responses API payload for OpenAI models", async () => {
+    const server = state.server
+    if (!server) {
+      throw new Error("Server not initialized")
+    }
+
+    const source = await loadFixture("openai", "gpt-5.2")
+    const model = source.model
+
+    const responseChunks = [
+      {
+        type: "response.created",
+        response: {
+          id: "resp-1",
+          created_at: Math.floor(Date.now() / 1000),
+          model: model.id,
+          service_tier: null,
+        },
+      },
+      {
+        type: "response.output_text.delta",
+        item_id: "item-1",
+        delta: "Hello",
+        logprobs: null,
+      },
+      {
+        type: "response.completed",
+        response: {
+          incomplete_details: null,
+          usage: {
+            input_tokens: 1,
+            input_tokens_details: null,
+            output_tokens: 1,
+            output_tokens_details: null,
+          },
+          service_tier: null,
+        },
+      },
+    ]
+    const request = waitRequest("/responses", createEventResponse(responseChunks, true))
+
+    await using tmp = await tmpdir({
+      init: async (dir) => {
+        await Bun.write(
+          path.join(dir, "opencode.json"),
+          JSON.stringify({
+            $schema: "https://opencode.ai/config.json",
+            enabled_providers: ["openai"],
+            provider: {
+              openai: {
+                name: "OpenAI",
+                env: ["OPENAI_API_KEY"],
+                npm: "@ai-sdk/openai",
+                api: "https://api.openai.com/v1",
+                models: {
+                  [model.id]: model,
+                },
+                options: {
+                  apiKey: "test-openai-key",
+                  baseURL: `${server.url.origin}/v1`,
+                },
+              },
+            },
+          }),
+        )
+      },
+    })
+
+    await Instance.provide({
+      directory: tmp.path,
+      fn: async () => {
+        const resolved = await Provider.getModel(ProviderID.openai, ModelID.make(model.id))
+        const sessionID = SessionID.make("session-test-2")
+        const agent = {
+          name: "test",
+          mode: "primary",
+          options: {},
+          permission: [{ permission: "*", pattern: "*", action: "allow" }],
+          temperature: 0.2,
+        } satisfies Agent.Info
+
+        const user = {
+          id: MessageID.make("user-2"),
+          sessionID,
+          role: "user",
+          time: { created: Date.now() },
+          agent: agent.name,
+          model: { providerID: ProviderID.make("openai"), modelID: resolved.id },
+          variant: "high",
+        } satisfies MessageV2.User
+
+        const stream = await LLM.stream({
+          user,
+          sessionID,
+          model: resolved,
+          agent,
+          system: ["You are a helpful assistant."],
+          abort: new AbortController().signal,
+          messages: [{ role: "user", content: "Hello" }],
+          tools: {},
+        })
+
+        for await (const _ of stream.fullStream) {
+        }
+
+        const capture = await request
+        const body = capture.body
+
+        expect(capture.url.pathname.endsWith("/responses")).toBe(true)
+        expect(body.model).toBe(resolved.api.id)
+        expect(body.stream).toBe(true)
+        expect((body.reasoning as { effort?: string } | undefined)?.effort).toBe("high")
+
+        const maxTokens = body.max_output_tokens as number | undefined
+        const expectedMaxTokens = ProviderTransform.maxOutputTokens(resolved)
+        expect(maxTokens).toBe(expectedMaxTokens)
+      },
+    })
+  })
+
+  test("sends messages API payload for Anthropic models", async () => {
+    const server = state.server
+    if (!server) {
+      throw new Error("Server not initialized")
+    }
+
+    const providerID = "anthropic"
+    const modelID = "claude-3-5-sonnet-20241022"
+    const fixture = await loadFixture(providerID, modelID)
+    const provider = fixture.provider
+    const model = fixture.model
+
+    const chunks = [
+      {
+        type: "message_start",
+        message: {
+          id: "msg-1",
+          model: model.id,
+          usage: {
+            input_tokens: 3,
+            cache_creation_input_tokens: null,
+            cache_read_input_tokens: null,
+          },
+        },
+      },
+      {
+        type: "content_block_start",
+        index: 0,
+        content_block: { type: "text", text: "" },
+      },
+      {
+        type: "content_block_delta",
+        index: 0,
+        delta: { type: "text_delta", text: "Hello" },
+      },
+      { type: "content_block_stop", index: 0 },
+      {
+        type: "message_delta",
+        delta: { stop_reason: "end_turn", stop_sequence: null, container: null },
+        usage: {
+          input_tokens: 3,
+          output_tokens: 2,
+          cache_creation_input_tokens: null,
+          cache_read_input_tokens: null,
+        },
+      },
+      { type: "message_stop" },
+    ]
+    const request = waitRequest("/messages", createEventResponse(chunks))
+
+    await using tmp = await tmpdir({
+      init: async (dir) => {
+        await Bun.write(
+          path.join(dir, "opencode.json"),
+          JSON.stringify({
+            $schema: "https://opencode.ai/config.json",
+            enabled_providers: [providerID],
+            provider: {
+              [providerID]: {
+                options: {
+                  apiKey: "test-anthropic-key",
+                  baseURL: `${server.url.origin}/v1`,
+                },
+              },
+            },
+          }),
+        )
+      },
+    })
+
+    await Instance.provide({
+      directory: tmp.path,
+      fn: async () => {
+        const resolved = await Provider.getModel(ProviderID.make(providerID), ModelID.make(model.id))
+        const sessionID = SessionID.make("session-test-3")
+        const agent = {
+          name: "test",
+          mode: "primary",
+          options: {},
+          permission: [{ permission: "*", pattern: "*", action: "allow" }],
+          temperature: 0.4,
+          topP: 0.9,
+        } satisfies Agent.Info
+
+        const user = {
+          id: MessageID.make("user-3"),
+          sessionID,
+          role: "user",
+          time: { created: Date.now() },
+          agent: agent.name,
+          model: { providerID: ProviderID.make(providerID), modelID: resolved.id },
+        } satisfies MessageV2.User
+
+        const stream = await LLM.stream({
+          user,
+          sessionID,
+          model: resolved,
+          agent,
+          system: ["You are a helpful assistant."],
+          abort: new AbortController().signal,
+          messages: [{ role: "user", content: "Hello" }],
+          tools: {},
+        })
+
+        for await (const _ of stream.fullStream) {
+        }
+
+        const capture = await request
+        const body = capture.body
+
+        expect(capture.url.pathname.endsWith("/messages")).toBe(true)
+        expect(body.model).toBe(resolved.api.id)
+        expect(body.max_tokens).toBe(ProviderTransform.maxOutputTokens(resolved))
+        expect(body.temperature).toBe(0.4)
+        expect(body.top_p).toBe(0.9)
+      },
+    })
+  })
+
+  test("sends Google API payload for Gemini models", async () => {
+    const server = state.server
+    if (!server) {
+      throw new Error("Server not initialized")
+    }
+
+    const providerID = "google"
+    const modelID = "gemini-2.5-flash"
+    const fixture = await loadFixture(providerID, modelID)
+    const provider = fixture.provider
+    const model = fixture.model
+    const pathSuffix = `/v1beta/models/${model.id}:streamGenerateContent`
+
+    const chunks = [
+      {
+        candidates: [
+          {
+            content: {
+              parts: [{ text: "Hello" }],
+            },
+            finishReason: "STOP",
+          },
+        ],
+        usageMetadata: {
+          promptTokenCount: 1,
+          candidatesTokenCount: 1,
+          totalTokenCount: 2,
+        },
+      },
+    ]
+    const request = waitRequest(pathSuffix, createEventResponse(chunks))
+
+    await using tmp = await tmpdir({
+      init: async (dir) => {
+        await Bun.write(
+          path.join(dir, "opencode.json"),
+          JSON.stringify({
+            $schema: "https://opencode.ai/config.json",
+            enabled_providers: [providerID],
+            provider: {
+              [providerID]: {
+                options: {
+                  apiKey: "test-google-key",
+                  baseURL: `${server.url.origin}/v1beta`,
+                },
+              },
+            },
+          }),
+        )
+      },
+    })
+
+    await Instance.provide({
+      directory: tmp.path,
+      fn: async () => {
+        const resolved = await Provider.getModel(ProviderID.make(providerID), ModelID.make(model.id))
+        const sessionID = SessionID.make("session-test-4")
+        const agent = {
+          name: "test",
+          mode: "primary",
+          options: {},
+          permission: [{ permission: "*", pattern: "*", action: "allow" }],
+          temperature: 0.3,
+          topP: 0.8,
+        } satisfies Agent.Info
+
+        const user = {
+          id: MessageID.make("user-4"),
+          sessionID,
+          role: "user",
+          time: { created: Date.now() },
+          agent: agent.name,
+          model: { providerID: ProviderID.make(providerID), modelID: resolved.id },
+        } satisfies MessageV2.User
+
+        const stream = await LLM.stream({
+          user,
+          sessionID,
+          model: resolved,
+          agent,
+          system: ["You are a helpful assistant."],
+          abort: new AbortController().signal,
+          messages: [{ role: "user", content: "Hello" }],
+          tools: {},
+        })
+
+        for await (const _ of stream.fullStream) {
+        }
+
+        const capture = await request
+        const body = capture.body
+        const config = body.generationConfig as
+          | { temperature?: number; topP?: number; maxOutputTokens?: number }
+          | undefined
+
+        expect(capture.url.pathname).toBe(pathSuffix)
+        expect(config?.temperature).toBe(0.3)
+        expect(config?.topP).toBe(0.8)
+        expect(config?.maxOutputTokens).toBe(ProviderTransform.maxOutputTokens(resolved))
+      },
+    })
+  })
+})

packages/tfcode/test/session/message-v2.test.ts

@@ -0,0 +1,930 @@
+import { describe, expect, test } from "bun:test"
+import { APICallError } from "ai"
+import { MessageV2 } from "../../src/session/message-v2"
+import type { Provider } from "../../src/provider/provider"
+import { ModelID, ProviderID } from "../../src/provider/schema"
+import { SessionID, MessageID, PartID } from "../../src/session/schema"
+import { Question } from "../../src/question"
+
+const sessionID = SessionID.make("session")
+const providerID = ProviderID.make("test")
+const model: Provider.Model = {
+  id: ModelID.make("test-model"),
+  providerID,
+  api: {
+    id: "test-model",
+    url: "https://example.com",
+    npm: "@ai-sdk/openai",
+  },
+  name: "Test Model",
+  capabilities: {
+    temperature: true,
+    reasoning: false,
+    attachment: false,
+    toolcall: true,
+    input: {
+      text: true,
+      audio: false,
+      image: false,
+      video: false,
+      pdf: false,
+    },
+    output: {
+      text: true,
+      audio: false,
+      image: false,
+      video: false,
+      pdf: false,
+    },
+    interleaved: false,
+  },
+  cost: {
+    input: 0,
+    output: 0,
+    cache: {
+      read: 0,
+      write: 0,
+    },
+  },
+  limit: {
+    context: 0,
+    input: 0,
+    output: 0,
+  },
+  status: "active",
+  options: {},
+  headers: {},
+  release_date: "2026-01-01",
+}
+
+function userInfo(id: string): MessageV2.User {
+  return {
+    id,
+    sessionID,
+    role: "user",
+    time: { created: 0 },
+    agent: "user",
+    model: { providerID, modelID: ModelID.make("test") },
+    tools: {},
+    mode: "",
+  } as unknown as MessageV2.User
+}
+
+function assistantInfo(
+  id: string,
+  parentID: string,
+  error?: MessageV2.Assistant["error"],
+  meta?: { providerID: string; modelID: string },
+): MessageV2.Assistant {
+  const infoModel = meta ?? { providerID: model.providerID, modelID: model.api.id }
+  return {
+    id,
+    sessionID,
+    role: "assistant",
+    time: { created: 0 },
+    error,
+    parentID,
+    modelID: infoModel.modelID,
+    providerID: infoModel.providerID,
+    mode: "",
+    agent: "agent",
+    path: { cwd: "/", root: "/" },
+    cost: 0,
+    tokens: {
+      input: 0,
+      output: 0,
+      reasoning: 0,
+      cache: { read: 0, write: 0 },
+    },
+  } as unknown as MessageV2.Assistant
+}
+
+function basePart(messageID: string, id: string) {
+  return {
+    id: PartID.make(id),
+    sessionID,
+    messageID: MessageID.make(messageID),
+  }
+}
+
+describe("session.message-v2.toModelMessage", () => {
+  test("filters out messages with no parts", () => {
+    const input: MessageV2.WithParts[] = [
+      {
+        info: userInfo("m-empty"),
+        parts: [],
+      },
+      {
+        info: userInfo("m-user"),
+        parts: [
+          {
+            ...basePart("m-user", "p1"),
+            type: "text",
+            text: "hello",
+          },
+        ] as MessageV2.Part[],
+      },
+    ]
+
+    expect(MessageV2.toModelMessages(input, model)).toStrictEqual([
+      {
+        role: "user",
+        content: [{ type: "text", text: "hello" }],
+      },
+    ])
+  })
+
+  test("filters out messages with only ignored parts", () => {
+    const messageID = "m-user"
+
+    const input: MessageV2.WithParts[] = [
+      {
+        info: userInfo(messageID),
+        parts: [
+          {
+            ...basePart(messageID, "p1"),
+            type: "text",
+            text: "ignored",
+            ignored: true,
+          },
+        ] as MessageV2.Part[],
+      },
+    ]
+
+    expect(MessageV2.toModelMessages(input, model)).toStrictEqual([])
+  })
+
+  test("includes synthetic text parts", () => {
+    const messageID = "m-user"
+
+    const input: MessageV2.WithParts[] = [
+      {
+        info: userInfo(messageID),
+        parts: [
+          {
+            ...basePart(messageID, "p1"),
+            type: "text",
+            text: "hello",
+            synthetic: true,
+          },
+        ] as MessageV2.Part[],
+      },
+      {
+        info: assistantInfo("m-assistant", messageID),
+        parts: [
+          {
+            ...basePart("m-assistant", "a1"),
+            type: "text",
+            text: "assistant",
+            synthetic: true,
+          },
+        ] as MessageV2.Part[],
+      },
+    ]
+
+    expect(MessageV2.toModelMessages(input, model)).toStrictEqual([
+      {
+        role: "user",
+        content: [{ type: "text", text: "hello" }],
+      },
+      {
+        role: "assistant",
+        content: [{ type: "text", text: "assistant" }],
+      },
+    ])
+  })
+
+  test("converts user text/file parts and injects compaction/subtask prompts", () => {
+    const messageID = "m-user"
+
+    const input: MessageV2.WithParts[] = [
+      {
+        info: userInfo(messageID),
+        parts: [
+          {
+            ...basePart(messageID, "p1"),
+            type: "text",
+            text: "hello",
+          },
+          {
+            ...basePart(messageID, "p2"),
+            type: "text",
+            text: "ignored",
+            ignored: true,
+          },
+          {
+            ...basePart(messageID, "p3"),
+            type: "file",
+            mime: "image/png",
+            filename: "img.png",
+            url: "https://example.com/img.png",
+          },
+          {
+            ...basePart(messageID, "p4"),
+            type: "file",
+            mime: "text/plain",
+            filename: "note.txt",
+            url: "https://example.com/note.txt",
+          },
+          {
+            ...basePart(messageID, "p5"),
+            type: "file",
+            mime: "application/x-directory",
+            filename: "dir",
+            url: "https://example.com/dir",
+          },
+          {
+            ...basePart(messageID, "p6"),
+            type: "compaction",
+            auto: true,
+          },
+          {
+            ...basePart(messageID, "p7"),
+            type: "subtask",
+            prompt: "prompt",
+            description: "desc",
+            agent: "agent",
+          },
+        ] as MessageV2.Part[],
+      },
+    ]
+
+    expect(MessageV2.toModelMessages(input, model)).toStrictEqual([
+      {
+        role: "user",
+        content: [
+          { type: "text", text: "hello" },
+          {
+            type: "file",
+            mediaType: "image/png",
+            filename: "img.png",
+            data: "https://example.com/img.png",
+          },
+          { type: "text", text: "What did we do so far?" },
+          { type: "text", text: "The following tool was executed by the user" },
+        ],
+      },
+    ])
+  })
+
+  test("converts assistant tool completion into tool-call + tool-result messages with attachments", () => {
+    const userID = "m-user"
+    const assistantID = "m-assistant"
+
+    const input: MessageV2.WithParts[] = [
+      {
+        info: userInfo(userID),
+        parts: [
+          {
+            ...basePart(userID, "u1"),
+            type: "text",
+            text: "run tool",
+          },
+        ] as MessageV2.Part[],
+      },
+      {
+        info: assistantInfo(assistantID, userID),
+        parts: [
+          {
+            ...basePart(assistantID, "a1"),
+            type: "text",
+            text: "done",
+            metadata: { openai: { assistant: "meta" } },
+          },
+          {
+            ...basePart(assistantID, "a2"),
+            type: "tool",
+            callID: "call-1",
+            tool: "bash",
+            state: {
+              status: "completed",
+              input: { cmd: "ls" },
+              output: "ok",
+              title: "Bash",
+              metadata: {},
+              time: { start: 0, end: 1 },
+              attachments: [
+                {
+                  ...basePart(assistantID, "file-1"),
+                  type: "file",
+                  mime: "image/png",
+                  filename: "attachment.png",
+                  url: "data:image/png;base64,Zm9v",
+                },
+              ],
+            },
+            metadata: { openai: { tool: "meta" } },
+          },
+        ] as MessageV2.Part[],
+      },
+    ]
+
+    expect(MessageV2.toModelMessages(input, model)).toStrictEqual([
+      {
+        role: "user",
+        content: [{ type: "text", text: "run tool" }],
+      },
+      {
+        role: "assistant",
+        content: [
+          { type: "text", text: "done", providerOptions: { openai: { assistant: "meta" } } },
+          {
+            type: "tool-call",
+            toolCallId: "call-1",
+            toolName: "bash",
+            input: { cmd: "ls" },
+            providerExecuted: undefined,
+            providerOptions: { openai: { tool: "meta" } },
+          },
+        ],
+      },
+      {
+        role: "tool",
+        content: [
+          {
+            type: "tool-result",
+            toolCallId: "call-1",
+            toolName: "bash",
+            output: {
+              type: "content",
+              value: [
+                { type: "text", text: "ok" },
+                { type: "media", mediaType: "image/png", data: "Zm9v" },
+              ],
+            },
+            providerOptions: { openai: { tool: "meta" } },
+          },
+        ],
+      },
+    ])
+  })
+
+  test("omits provider metadata when assistant model differs", () => {
+    const userID = "m-user"
+    const assistantID = "m-assistant"
+
+    const input: MessageV2.WithParts[] = [
+      {
+        info: userInfo(userID),
+        parts: [
+          {
+            ...basePart(userID, "u1"),
+            type: "text",
+            text: "run tool",
+          },
+        ] as MessageV2.Part[],
+      },
+      {
+        info: assistantInfo(assistantID, userID, undefined, { providerID: "other", modelID: "other" }),
+        parts: [
+          {
+            ...basePart(assistantID, "a1"),
+            type: "text",
+            text: "done",
+            metadata: { openai: { assistant: "meta" } },
+          },
+          {
+            ...basePart(assistantID, "a2"),
+            type: "tool",
+            callID: "call-1",
+            tool: "bash",
+            state: {
+              status: "completed",
+              input: { cmd: "ls" },
+              output: "ok",
+              title: "Bash",
+              metadata: {},
+              time: { start: 0, end: 1 },
+            },
+            metadata: { openai: { tool: "meta" } },
+          },
+        ] as MessageV2.Part[],
+      },
+    ]
+
+    expect(MessageV2.toModelMessages(input, model)).toStrictEqual([
+      {
+        role: "user",
+        content: [{ type: "text", text: "run tool" }],
+      },
+      {
+        role: "assistant",
+        content: [
+          { type: "text", text: "done" },
+          {
+            type: "tool-call",
+            toolCallId: "call-1",
+            toolName: "bash",
+            input: { cmd: "ls" },
+            providerExecuted: undefined,
+          },
+        ],
+      },
+      {
+        role: "tool",
+        content: [
+          {
+            type: "tool-result",
+            toolCallId: "call-1",
+            toolName: "bash",
+            output: { type: "text", value: "ok" },
+          },
+        ],
+      },
+    ])
+  })
+
+  test("replaces compacted tool output with placeholder", () => {
+    const userID = "m-user"
+    const assistantID = "m-assistant"
+
+    const input: MessageV2.WithParts[] = [
+      {
+        info: userInfo(userID),
+        parts: [
+          {
+            ...basePart(userID, "u1"),
+            type: "text",
+            text: "run tool",
+          },
+        ] as MessageV2.Part[],
+      },
+      {
+        info: assistantInfo(assistantID, userID),
+        parts: [
+          {
+            ...basePart(assistantID, "a1"),
+            type: "tool",
+            callID: "call-1",
+            tool: "bash",
+            state: {
+              status: "completed",
+              input: { cmd: "ls" },
+              output: "this should be cleared",
+              title: "Bash",
+              metadata: {},
+              time: { start: 0, end: 1, compacted: 1 },
+            },
+          },
+        ] as MessageV2.Part[],
+      },
+    ]
+
+    expect(MessageV2.toModelMessages(input, model)).toStrictEqual([
+      {
+        role: "user",
+        content: [{ type: "text", text: "run tool" }],
+      },
+      {
+        role: "assistant",
+        content: [
+          {
+            type: "tool-call",
+            toolCallId: "call-1",
+            toolName: "bash",
+            input: { cmd: "ls" },
+            providerExecuted: undefined,
+          },
+        ],
+      },
+      {
+        role: "tool",
+        content: [
+          {
+            type: "tool-result",
+            toolCallId: "call-1",
+            toolName: "bash",
+            output: { type: "text", value: "[Old tool result content cleared]" },
+          },
+        ],
+      },
+    ])
+  })
+
+  test("converts assistant tool error into error-text tool result", () => {
+    const userID = "m-user"
+    const assistantID = "m-assistant"
+
+    const input: MessageV2.WithParts[] = [
+      {
+        info: userInfo(userID),
+        parts: [
+          {
+            ...basePart(userID, "u1"),
+            type: "text",
+            text: "run tool",
+          },
+        ] as MessageV2.Part[],
+      },
+      {
+        info: assistantInfo(assistantID, userID),
+        parts: [
+          {
+            ...basePart(assistantID, "a1"),
+            type: "tool",
+            callID: "call-1",
+            tool: "bash",
+            state: {
+              status: "error",
+              input: { cmd: "ls" },
+              error: "nope",
+              time: { start: 0, end: 1 },
+              metadata: {},
+            },
+            metadata: { openai: { tool: "meta" } },
+          },
+        ] as MessageV2.Part[],
+      },
+    ]
+
+    expect(MessageV2.toModelMessages(input, model)).toStrictEqual([
+      {
+        role: "user",
+        content: [{ type: "text", text: "run tool" }],
+      },
+      {
+        role: "assistant",
+        content: [
+          {
+            type: "tool-call",
+            toolCallId: "call-1",
+            toolName: "bash",
+            input: { cmd: "ls" },
+            providerExecuted: undefined,
+            providerOptions: { openai: { tool: "meta" } },
+          },
+        ],
+      },
+      {
+        role: "tool",
+        content: [
+          {
+            type: "tool-result",
+            toolCallId: "call-1",
+            toolName: "bash",
+            output: { type: "error-text", value: "nope" },
+            providerOptions: { openai: { tool: "meta" } },
+          },
+        ],
+      },
+    ])
+  })
+
+  test("filters assistant messages with non-abort errors", () => {
+    const assistantID = "m-assistant"
+
+    const input: MessageV2.WithParts[] = [
+      {
+        info: assistantInfo(
+          assistantID,
+          "m-parent",
+          new MessageV2.APIError({ message: "boom", isRetryable: true }).toObject() as MessageV2.APIError,
+        ),
+        parts: [
+          {
+            ...basePart(assistantID, "a1"),
+            type: "text",
+            text: "should not render",
+          },
+        ] as MessageV2.Part[],
+      },
+    ]
+
+    expect(MessageV2.toModelMessages(input, model)).toStrictEqual([])
+  })
+
+  test("includes aborted assistant messages only when they have non-step-start/reasoning content", () => {
+    const assistantID1 = "m-assistant-1"
+    const assistantID2 = "m-assistant-2"
+
+    const aborted = new MessageV2.AbortedError({ message: "aborted" }).toObject() as MessageV2.Assistant["error"]
+
+    const input: MessageV2.WithParts[] = [
+      {
+        info: assistantInfo(assistantID1, "m-parent", aborted),
+        parts: [
+          {
+            ...basePart(assistantID1, "a1"),
+            type: "reasoning",
+            text: "thinking",
+            time: { start: 0 },
+          },
+          {
+            ...basePart(assistantID1, "a2"),
+            type: "text",
+            text: "partial answer",
+          },
+        ] as MessageV2.Part[],
+      },
+      {
+        info: assistantInfo(assistantID2, "m-parent", aborted),
+        parts: [
+          {
+            ...basePart(assistantID2, "b1"),
+            type: "step-start",
+          },
+          {
+            ...basePart(assistantID2, "b2"),
+            type: "reasoning",
+            text: "thinking",
+            time: { start: 0 },
+          },
+        ] as MessageV2.Part[],
+      },
+    ]
+
+    expect(MessageV2.toModelMessages(input, model)).toStrictEqual([
+      {
+        role: "assistant",
+        content: [
+          { type: "reasoning", text: "thinking", providerOptions: undefined },
+          { type: "text", text: "partial answer" },
+        ],
+      },
+    ])
+  })
+
+  test("splits assistant messages on step-start boundaries", () => {
+    const assistantID = "m-assistant"
+
+    const input: MessageV2.WithParts[] = [
+      {
+        info: assistantInfo(assistantID, "m-parent"),
+        parts: [
+          {
+            ...basePart(assistantID, "p1"),
+            type: "text",
+            text: "first",
+          },
+          {
+            ...basePart(assistantID, "p2"),
+            type: "step-start",
+          },
+          {
+            ...basePart(assistantID, "p3"),
+            type: "text",
+            text: "second",
+          },
+        ] as MessageV2.Part[],
+      },
+    ]
+
+    expect(MessageV2.toModelMessages(input, model)).toStrictEqual([
+      {
+        role: "assistant",
+        content: [{ type: "text", text: "first" }],
+      },
+      {
+        role: "assistant",
+        content: [{ type: "text", text: "second" }],
+      },
+    ])
+  })
+
+  test("drops messages that only contain step-start parts", () => {
+    const assistantID = "m-assistant"
+
+    const input: MessageV2.WithParts[] = [
+      {
+        info: assistantInfo(assistantID, "m-parent"),
+        parts: [
+          {
+            ...basePart(assistantID, "p1"),
+            type: "step-start",
+          },
+        ] as MessageV2.Part[],
+      },
+    ]
+
+    expect(MessageV2.toModelMessages(input, model)).toStrictEqual([])
+  })
+
+  test("converts pending/running tool calls to error results to prevent dangling tool_use", () => {
+    const userID = "m-user"
+    const assistantID = "m-assistant"
+
+    const input: MessageV2.WithParts[] = [
+      {
+        info: userInfo(userID),
+        parts: [
+          {
+            ...basePart(userID, "u1"),
+            type: "text",
+            text: "run tool",
+          },
+        ] as MessageV2.Part[],
+      },
+      {
+        info: assistantInfo(assistantID, userID),
+        parts: [
+          {
+            ...basePart(assistantID, "a1"),
+            type: "tool",
+            callID: "call-pending",
+            tool: "bash",
+            state: {
+              status: "pending",
+              input: { cmd: "ls" },
+              raw: "",
+            },
+          },
+          {
+            ...basePart(assistantID, "a2"),
+            type: "tool",
+            callID: "call-running",
+            tool: "read",
+            state: {
+              status: "running",
+              input: { path: "/tmp" },
+              time: { start: 0 },
+            },
+          },
+        ] as MessageV2.Part[],
+      },
+    ]
+
+    const result = MessageV2.toModelMessages(input, model)
+
+    expect(result).toStrictEqual([
+      {
+        role: "user",
+        content: [{ type: "text", text: "run tool" }],
+      },
+      {
+        role: "assistant",
+        content: [
+          {
+            type: "tool-call",
+            toolCallId: "call-pending",
+            toolName: "bash",
+            input: { cmd: "ls" },
+            providerExecuted: undefined,
+          },
+          {
+            type: "tool-call",
+            toolCallId: "call-running",
+            toolName: "read",
+            input: { path: "/tmp" },
+            providerExecuted: undefined,
+          },
+        ],
+      },
+      {
+        role: "tool",
+        content: [
+          {
+            type: "tool-result",
+            toolCallId: "call-pending",
+            toolName: "bash",
+            output: { type: "error-text", value: "[Tool execution was interrupted]" },
+          },
+          {
+            type: "tool-result",
+            toolCallId: "call-running",
+            toolName: "read",
+            output: { type: "error-text", value: "[Tool execution was interrupted]" },
+          },
+        ],
+      },
+    ])
+  })
+})
+
+describe("session.message-v2.fromError", () => {
+  test("serializes context_length_exceeded as ContextOverflowError", () => {
+    const input = {
+      type: "error",
+      error: {
+        code: "context_length_exceeded",
+      },
+    }
+    const result = MessageV2.fromError(input, { providerID })
+
+    expect(result).toStrictEqual({
+      name: "ContextOverflowError",
+      data: {
+        message: "Input exceeds context window of this model",
+        responseBody: JSON.stringify(input),
+      },
+    })
+  })
+
+  test("serializes response error codes", () => {
+    const cases = [
+      {
+        code: "insufficient_quota",
+        message: "Quota exceeded. Check your plan and billing details.",
+      },
+      {
+        code: "usage_not_included",
+        message: "To use Codex with your ChatGPT plan, upgrade to Plus: https://chatgpt.com/explore/plus.",
+      },
+      {
+        code: "invalid_prompt",
+        message: "Invalid prompt from test",
+      },
+    ]
+
+    cases.forEach((item) => {
+      const input = {
+        type: "error",
+        error: {
+          code: item.code,
+          message: item.code === "invalid_prompt" ? item.message : undefined,
+        },
+      }
+      const result = MessageV2.fromError(input, { providerID })
+
+      expect(result).toStrictEqual({
+        name: "APIError",
+        data: {
+          message: item.message,
+          isRetryable: false,
+          responseBody: JSON.stringify(input),
+        },
+      })
+    })
+  })
+
+  test("detects context overflow from APICallError provider messages", () => {
+    const cases = [
+      "prompt is too long: 213462 tokens > 200000 maximum",
+      "Your input exceeds the context window of this model",
+      "The input token count (1196265) exceeds the maximum number of tokens allowed (1048575)",
+      "Please reduce the length of the messages or completion",
+      "400 status code (no body)",
+      "413 status code (no body)",
+    ]
+
+    cases.forEach((message) => {
+      const error = new APICallError({
+        message,
+        url: "https://example.com",
+        requestBodyValues: {},
+        statusCode: 400,
+        responseHeaders: { "content-type": "application/json" },
+        isRetryable: false,
+      })
+      const result = MessageV2.fromError(error, { providerID })
+      expect(MessageV2.ContextOverflowError.isInstance(result)).toBe(true)
+    })
+  })
+
+  test("detects context overflow from context_length_exceeded code in response body", () => {
+    const error = new APICallError({
+      message: "Request failed",
+      url: "https://example.com",
+      requestBodyValues: {},
+      statusCode: 422,
+      responseHeaders: { "content-type": "application/json" },
+      responseBody: JSON.stringify({
+        error: {
+          message: "Some message",
+          type: "invalid_request_error",
+          code: "context_length_exceeded",
+        },
+      }),
+      isRetryable: false,
+    })
+    const result = MessageV2.fromError(error, { providerID })
+    expect(MessageV2.ContextOverflowError.isInstance(result)).toBe(true)
+  })
+
+  test("does not classify 429 no body as context overflow", () => {
+    const result = MessageV2.fromError(
+      new APICallError({
+        message: "429 status code (no body)",
+        url: "https://example.com",
+        requestBodyValues: {},
+        statusCode: 429,
+        responseHeaders: { "content-type": "application/json" },
+        isRetryable: false,
+      }),
+      { providerID },
+    )
+    expect(MessageV2.ContextOverflowError.isInstance(result)).toBe(false)
+    expect(MessageV2.APIError.isInstance(result)).toBe(true)
+  })
+
+  test("serializes unknown inputs", () => {
+    const result = MessageV2.fromError(123, { providerID })
+
+    expect(result).toStrictEqual({
+      name: "UnknownError",
+      data: {
+        message: "123",
+      },
+    })
+  })
+
+  test("serializes tagged errors with their message", () => {
+    const result = MessageV2.fromError(new Question.RejectedError(), { providerID })
+
+    expect(result).toStrictEqual({
+      name: "UnknownError",
+      data: {
+        message: "The user dismissed this question",
+      },
+    })
+  })
+})

packages/tfcode/test/session/messages-pagination.test.ts

@@ -0,0 +1,115 @@
+import { describe, expect, test } from "bun:test"
+import path from "path"
+import { Instance } from "../../src/project/instance"
+import { Session } from "../../src/session"
+import { MessageV2 } from "../../src/session/message-v2"
+import { MessageID, PartID, type SessionID } from "../../src/session/schema"
+import { Log } from "../../src/util/log"
+
+const root = path.join(__dirname, "../..")
+Log.init({ print: false })
+
+async function fill(sessionID: SessionID, count: number, time = (i: number) => Date.now() + i) {
+  const ids = [] as MessageID[]
+  for (let i = 0; i < count; i++) {
+    const id = MessageID.ascending()
+    ids.push(id)
+    await Session.updateMessage({
+      id,
+      sessionID,
+      role: "user",
+      time: { created: time(i) },
+      agent: "test",
+      model: { providerID: "test", modelID: "test" },
+      tools: {},
+      mode: "",
+    } as unknown as MessageV2.Info)
+    await Session.updatePart({
+      id: PartID.ascending(),
+      sessionID,
+      messageID: id,
+      type: "text",
+      text: `m${i}`,
+    })
+  }
+  return ids
+}
+
+describe("session message pagination", () => {
+  test("pages backward with opaque cursors", async () => {
+    await Instance.provide({
+      directory: root,
+      fn: async () => {
+        const session = await Session.create({})
+        const ids = await fill(session.id, 6)
+
+        const a = await MessageV2.page({ sessionID: session.id, limit: 2 })
+        expect(a.items.map((item) => item.info.id)).toEqual(ids.slice(-2))
+        expect(a.items.every((item) => item.parts.length === 1)).toBe(true)
+        expect(a.more).toBe(true)
+        expect(a.cursor).toBeTruthy()
+
+        const b = await MessageV2.page({ sessionID: session.id, limit: 2, before: a.cursor! })
+        expect(b.items.map((item) => item.info.id)).toEqual(ids.slice(-4, -2))
+        expect(b.more).toBe(true)
+        expect(b.cursor).toBeTruthy()
+
+        const c = await MessageV2.page({ sessionID: session.id, limit: 2, before: b.cursor! })
+        expect(c.items.map((item) => item.info.id)).toEqual(ids.slice(0, 2))
+        expect(c.more).toBe(false)
+        expect(c.cursor).toBeUndefined()
+
+        await Session.remove(session.id)
+      },
+    })
+  })
+
+  test("keeps stream order newest first", async () => {
+    await Instance.provide({
+      directory: root,
+      fn: async () => {
+        const session = await Session.create({})
+        const ids = await fill(session.id, 5)
+
+        const items = await Array.fromAsync(MessageV2.stream(session.id))
+        expect(items.map((item) => item.info.id)).toEqual(ids.slice().reverse())
+
+        await Session.remove(session.id)
+      },
+    })
+  })
+
+  test("accepts cursors generated from fractional timestamps", async () => {
+    await Instance.provide({
+      directory: root,
+      fn: async () => {
+        const session = await Session.create({})
+        const ids = await fill(session.id, 4, (i) => 1000.5 + i)
+
+        const a = await MessageV2.page({ sessionID: session.id, limit: 2 })
+        const b = await MessageV2.page({ sessionID: session.id, limit: 2, before: a.cursor! })
+
+        expect(a.items.map((item) => item.info.id)).toEqual(ids.slice(-2))
+        expect(b.items.map((item) => item.info.id)).toEqual(ids.slice(0, 2))
+
+        await Session.remove(session.id)
+      },
+    })
+  })
+
+  test("scopes get by session id", async () => {
+    await Instance.provide({
+      directory: root,
+      fn: async () => {
+        const a = await Session.create({})
+        const b = await Session.create({})
+        const [id] = await fill(a.id, 1)
+
+        await expect(MessageV2.get({ sessionID: b.id, messageID: id })).rejects.toMatchObject({ name: "NotFoundError" })
+
+        await Session.remove(a.id)
+        await Session.remove(b.id)
+      },
+    })
+  })
+})

packages/tfcode/test/session/prompt.test.ts

@@ -0,0 +1,212 @@
+import path from "path"
+import { describe, expect, test } from "bun:test"
+import { fileURLToPath } from "url"
+import { Instance } from "../../src/project/instance"
+import { ModelID, ProviderID } from "../../src/provider/schema"
+import { Session } from "../../src/session"
+import { MessageV2 } from "../../src/session/message-v2"
+import { SessionPrompt } from "../../src/session/prompt"
+import { Log } from "../../src/util/log"
+import { tmpdir } from "../fixture/fixture"
+
+Log.init({ print: false })
+
+describe("session.prompt missing file", () => {
+  test("does not fail the prompt when a file part is missing", async () => {
+    await using tmp = await tmpdir({
+      git: true,
+      config: {
+        agent: {
+          build: {
+            model: "openai/gpt-5.2",
+          },
+        },
+      },
+    })
+
+    await Instance.provide({
+      directory: tmp.path,
+      fn: async () => {
+        const session = await Session.create({})
+
+        const missing = path.join(tmp.path, "does-not-exist.ts")
+        const msg = await SessionPrompt.prompt({
+          sessionID: session.id,
+          agent: "build",
+          noReply: true,
+          parts: [
+            { type: "text", text: "please review @does-not-exist.ts" },
+            {
+              type: "file",
+              mime: "text/plain",
+              url: `file://${missing}`,
+              filename: "does-not-exist.ts",
+            },
+          ],
+        })
+
+        if (msg.info.role !== "user") throw new Error("expected user message")
+
+        const hasFailure = msg.parts.some(
+          (part) => part.type === "text" && part.synthetic && part.text.includes("Read tool failed to read"),
+        )
+        expect(hasFailure).toBe(true)
+
+        await Session.remove(session.id)
+      },
+    })
+  })
+
+  test("keeps stored part order stable when file resolution is async", async () => {
+    await using tmp = await tmpdir({
+      git: true,
+      config: {
+        agent: {
+          build: {
+            model: "openai/gpt-5.2",
+          },
+        },
+      },
+    })
+
+    await Instance.provide({
+      directory: tmp.path,
+      fn: async () => {
+        const session = await Session.create({})
+
+        const missing = path.join(tmp.path, "still-missing.ts")
+        const msg = await SessionPrompt.prompt({
+          sessionID: session.id,
+          agent: "build",
+          noReply: true,
+          parts: [
+            {
+              type: "file",
+              mime: "text/plain",
+              url: `file://${missing}`,
+              filename: "still-missing.ts",
+            },
+            { type: "text", text: "after-file" },
+          ],
+        })
+
+        if (msg.info.role !== "user") throw new Error("expected user message")
+
+        const stored = await MessageV2.get({
+          sessionID: session.id,
+          messageID: msg.info.id,
+        })
+        const text = stored.parts.filter((part) => part.type === "text").map((part) => part.text)
+
+        expect(text[0]?.startsWith("Called the Read tool with the following input:")).toBe(true)
+        expect(text[1]?.includes("Read tool failed to read")).toBe(true)
+        expect(text[2]).toBe("after-file")
+
+        await Session.remove(session.id)
+      },
+    })
+  })
+})
+
+describe("session.prompt special characters", () => {
+  test("handles filenames with # character", async () => {
+    await using tmp = await tmpdir({
+      git: true,
+      init: async (dir) => {
+        await Bun.write(path.join(dir, "file#name.txt"), "special content\n")
+      },
+    })
+
+    await Instance.provide({
+      directory: tmp.path,
+      fn: async () => {
+        const session = await Session.create({})
+        const template = "Read @file#name.txt"
+        const parts = await SessionPrompt.resolvePromptParts(template)
+        const fileParts = parts.filter((part) => part.type === "file")
+
+        expect(fileParts.length).toBe(1)
+        expect(fileParts[0].filename).toBe("file#name.txt")
+        expect(fileParts[0].url).toContain("%23")
+
+        const decodedPath = fileURLToPath(fileParts[0].url)
+        expect(decodedPath).toBe(path.join(tmp.path, "file#name.txt"))
+
+        const message = await SessionPrompt.prompt({
+          sessionID: session.id,
+          parts,
+          noReply: true,
+        })
+        const stored = await MessageV2.get({ sessionID: session.id, messageID: message.info.id })
+        const textParts = stored.parts.filter((part) => part.type === "text")
+        const hasContent = textParts.some((part) => part.text.includes("special content"))
+        expect(hasContent).toBe(true)
+
+        await Session.remove(session.id)
+      },
+    })
+  })
+})
+
+describe("session.prompt agent variant", () => {
+  test("applies agent variant only when using agent model", async () => {
+    const prev = process.env.OPENAI_API_KEY
+    process.env.OPENAI_API_KEY = "test-openai-key"
+
+    try {
+      await using tmp = await tmpdir({
+        git: true,
+        config: {
+          agent: {
+            build: {
+              model: "openai/gpt-5.2",
+              variant: "xhigh",
+            },
+          },
+        },
+      })
+
+      await Instance.provide({
+        directory: tmp.path,
+        fn: async () => {
+          const session = await Session.create({})
+
+          const other = await SessionPrompt.prompt({
+            sessionID: session.id,
+            agent: "build",
+            model: { providerID: ProviderID.make("opencode"), modelID: ModelID.make("kimi-k2.5-free") },
+            noReply: true,
+            parts: [{ type: "text", text: "hello" }],
+          })
+          if (other.info.role !== "user") throw new Error("expected user message")
+          expect(other.info.variant).toBeUndefined()
+
+          const match = await SessionPrompt.prompt({
+            sessionID: session.id,
+            agent: "build",
+            noReply: true,
+            parts: [{ type: "text", text: "hello again" }],
+          })
+          if (match.info.role !== "user") throw new Error("expected user message")
+          expect(match.info.model).toEqual({ providerID: ProviderID.make("openai"), modelID: ModelID.make("gpt-5.2") })
+          expect(match.info.variant).toBe("xhigh")
+
+          const override = await SessionPrompt.prompt({
+            sessionID: session.id,
+            agent: "build",
+            noReply: true,
+            variant: "high",
+            parts: [{ type: "text", text: "hello third" }],
+          })
+          if (override.info.role !== "user") throw new Error("expected user message")
+          expect(override.info.variant).toBe("high")
+
+          await Session.remove(session.id)
+        },
+      })
+    } finally {
+      if (prev === undefined) delete process.env.OPENAI_API_KEY
+      else process.env.OPENAI_API_KEY = prev
+    }
+  })
+})

packages/tfcode/test/session/retry.test.ts

@@ -0,0 +1,192 @@
+import { describe, expect, test } from "bun:test"
+import type { NamedError } from "@opencode-ai/util/error"
+import { APICallError } from "ai"
+import { setTimeout as sleep } from "node:timers/promises"
+import { SessionRetry } from "../../src/session/retry"
+import { MessageV2 } from "../../src/session/message-v2"
+import { ProviderID } from "../../src/provider/schema"
+
+const providerID = ProviderID.make("test")
+
+function apiError(headers?: Record<string, string>): MessageV2.APIError {
+  return new MessageV2.APIError({
+    message: "boom",
+    isRetryable: true,
+    responseHeaders: headers,
+  }).toObject() as MessageV2.APIError
+}
+
+function wrap(message: unknown): ReturnType<NamedError["toObject"]> {
+  return { data: { message } } as ReturnType<NamedError["toObject"]>
+}
+
+describe("session.retry.delay", () => {
+  test("caps delay at 30 seconds when headers missing", () => {
+    const error = apiError()
+    const delays = Array.from({ length: 10 }, (_, index) => SessionRetry.delay(index + 1, error))
+    expect(delays).toStrictEqual([2000, 4000, 8000, 16000, 30000, 30000, 30000, 30000, 30000, 30000])
+  })
+
+  test("prefers retry-after-ms when shorter than exponential", () => {
+    const error = apiError({ "retry-after-ms": "1500" })
+    expect(SessionRetry.delay(4, error)).toBe(1500)
+  })
+
+  test("uses retry-after seconds when reasonable", () => {
+    const error = apiError({ "retry-after": "30" })
+    expect(SessionRetry.delay(3, error)).toBe(30000)
+  })
+
+  test("accepts http-date retry-after values", () => {
+    const date = new Date(Date.now() + 20000).toUTCString()
+    const error = apiError({ "retry-after": date })
+    const d = SessionRetry.delay(1, error)
+    expect(d).toBeGreaterThanOrEqual(19000)
+    expect(d).toBeLessThanOrEqual(20000)
+  })
+
+  test("ignores invalid retry hints", () => {
+    const error = apiError({ "retry-after": "not-a-number" })
+    expect(SessionRetry.delay(1, error)).toBe(2000)
+  })
+
+  test("ignores malformed date retry hints", () => {
+    const error = apiError({ "retry-after": "Invalid Date String" })
+    expect(SessionRetry.delay(1, error)).toBe(2000)
+  })
+
+  test("ignores past date retry hints", () => {
+    const pastDate = new Date(Date.now() - 5000).toUTCString()
+    const error = apiError({ "retry-after": pastDate })
+    expect(SessionRetry.delay(1, error)).toBe(2000)
+  })
+
+  test("uses retry-after values even when exceeding 10 minutes with headers", () => {
+    const error = apiError({ "retry-after": "50" })
+    expect(SessionRetry.delay(1, error)).toBe(50000)
+
+    const longError = apiError({ "retry-after-ms": "700000" })
+    expect(SessionRetry.delay(1, longError)).toBe(700000)
+  })
+
+  test("sleep caps delay to max 32-bit signed integer to avoid TimeoutOverflowWarning", async () => {
+    const controller = new AbortController()
+
+    const warnings: string[] = []
+    const originalWarn = process.emitWarning
+    process.emitWarning = (warning: string | Error) => {
+      warnings.push(typeof warning === "string" ? warning : warning.message)
+    }
+
+    const promise = SessionRetry.sleep(2_560_914_000, controller.signal)
+    controller.abort()
+
+    try {
+      await promise
+    } catch {}
+
+    process.emitWarning = originalWarn
+    expect(warnings.some((w) => w.includes("TimeoutOverflowWarning"))).toBe(false)
+  })
+})
+
+describe("session.retry.retryable", () => {
+  test("maps too_many_requests json messages", () => {
+    const error = wrap(JSON.stringify({ type: "error", error: { type: "too_many_requests" } }))
+    expect(SessionRetry.retryable(error)).toBe("Too Many Requests")
+  })
+
+  test("maps overloaded provider codes", () => {
+    const error = wrap(JSON.stringify({ code: "resource_exhausted" }))
+    expect(SessionRetry.retryable(error)).toBe("Provider is overloaded")
+  })
+
+  test("handles json messages without code", () => {
+    const error = wrap(JSON.stringify({ error: { message: "no_kv_space" } }))
+    expect(SessionRetry.retryable(error)).toBe(`{"error":{"message":"no_kv_space"}}`)
+  })
+
+  test("does not throw on numeric error codes", () => {
+    const error = wrap(JSON.stringify({ type: "error", error: { code: 123 } }))
+    const result = SessionRetry.retryable(error)
+    expect(result).toBeUndefined()
+  })
+
+  test("returns undefined for non-json message", () => {
+    const error = wrap("not-json")
+    expect(SessionRetry.retryable(error)).toBeUndefined()
+  })
+
+  test("does not retry context overflow errors", () => {
+    const error = new MessageV2.ContextOverflowError({
+      message: "Input exceeds context window of this model",
+      responseBody: '{"error":{"code":"context_length_exceeded"}}',
+    }).toObject() as ReturnType<NamedError["toObject"]>
+
+    expect(SessionRetry.retryable(error)).toBeUndefined()
+  })
+})
+
+describe("session.message-v2.fromError", () => {
+  test.concurrent(
+    "converts ECONNRESET socket errors to retryable APIError",
+    async () => {
+      using server = Bun.serve({
+        port: 0,
+        idleTimeout: 8,
+        async fetch(req) {
+          return new Response(
+            new ReadableStream({
+              async pull(controller) {
+                controller.enqueue("Hello,")
+                await sleep(10000)
+                controller.enqueue(" World!")
+                controller.close()
+              },
+            }),
+            { headers: { "Content-Type": "text/plain" } },
+          )
+        },
+      })
+
+      const error = await fetch(new URL("/", server.url.origin))
+        .then((res) => res.text())
+        .catch((e) => e)
+
+      const result = MessageV2.fromError(error, { providerID })
+
+      expect(MessageV2.APIError.isInstance(result)).toBe(true)
+      expect((result as MessageV2.APIError).data.isRetryable).toBe(true)
+      expect((result as MessageV2.APIError).data.message).toBe("Connection reset by server")
+      expect((result as MessageV2.APIError).data.metadata?.code).toBe("ECONNRESET")
+      expect((result as MessageV2.APIError).data.metadata?.message).toInclude("socket connection")
+    },
+    15_000,
+  )
+
+  test("ECONNRESET socket error is retryable", () => {
+    const error = new MessageV2.APIError({
+      message: "Connection reset by server",
+      isRetryable: true,
+      metadata: { code: "ECONNRESET", message: "The socket connection was closed unexpectedly" },
+    }).toObject() as MessageV2.APIError
+
+    const retryable = SessionRetry.retryable(error)
+    expect(retryable).toBeDefined()
+    expect(retryable).toBe("Connection reset by server")
+  })
+
+  test("marks OpenAI 404 status codes as retryable", () => {
+    const error = new APICallError({
+      message: "boom",
+      url: "https://api.openai.com/v1/chat/completions",
+      requestBodyValues: {},
+      statusCode: 404,
+      responseHeaders: { "content-type": "application/json" },
+      responseBody: '{"error":"boom"}',
+      isRetryable: false,
+    })
+    const result = MessageV2.fromError(error, { providerID: ProviderID.make("openai") }) as MessageV2.APIError
+    expect(result.data.isRetryable).toBe(true)
+  })
+})

packages/tfcode/test/session/revert-compact.test.ts

@@ -0,0 +1,286 @@
+import { describe, expect, test, beforeEach, afterEach } from "bun:test"
+import path from "path"
+import { Session } from "../../src/session"
+import { ModelID, ProviderID } from "../../src/provider/schema"
+import { SessionRevert } from "../../src/session/revert"
+import { SessionCompaction } from "../../src/session/compaction"
+import { MessageV2 } from "../../src/session/message-v2"
+import { Log } from "../../src/util/log"
+import { Instance } from "../../src/project/instance"
+import { MessageID, PartID } from "../../src/session/schema"
+import { tmpdir } from "../fixture/fixture"
+
+const projectRoot = path.join(__dirname, "../..")
+Log.init({ print: false })
+
+describe("revert + compact workflow", () => {
+  test("should properly handle compact command after revert", async () => {
+    await using tmp = await tmpdir({ git: true })
+    await Instance.provide({
+      directory: tmp.path,
+      fn: async () => {
+        // Create a session
+        const session = await Session.create({})
+        const sessionID = session.id
+
+        // Create a user message
+        const userMsg1 = await Session.updateMessage({
+          id: MessageID.ascending(),
+          role: "user",
+          sessionID,
+          agent: "default",
+          model: {
+            providerID: ProviderID.make("openai"),
+            modelID: ModelID.make("gpt-4"),
+          },
+          time: {
+            created: Date.now(),
+          },
+        })
+
+        // Add a text part to the user message
+        await Session.updatePart({
+          id: PartID.ascending(),
+          messageID: userMsg1.id,
+          sessionID,
+          type: "text",
+          text: "Hello, please help me",
+        })
+
+        // Create an assistant response message
+        const assistantMsg1: MessageV2.Assistant = {
+          id: MessageID.ascending(),
+          role: "assistant",
+          sessionID,
+          mode: "default",
+          agent: "default",
+          path: {
+            cwd: tmp.path,
+            root: tmp.path,
+          },
+          cost: 0,
+          tokens: {
+            output: 0,
+            input: 0,
+            reasoning: 0,
+            cache: { read: 0, write: 0 },
+          },
+          modelID: ModelID.make("gpt-4"),
+          providerID: ProviderID.make("openai"),
+          parentID: userMsg1.id,
+          time: {
+            created: Date.now(),
+          },
+          finish: "end_turn",
+        }
+        await Session.updateMessage(assistantMsg1)
+
+        // Add a text part to the assistant message
+        await Session.updatePart({
+          id: PartID.ascending(),
+          messageID: assistantMsg1.id,
+          sessionID,
+          type: "text",
+          text: "Sure, I'll help you!",
+        })
+
+        // Create another user message
+        const userMsg2 = await Session.updateMessage({
+          id: MessageID.ascending(),
+          role: "user",
+          sessionID,
+          agent: "default",
+          model: {
+            providerID: ProviderID.make("openai"),
+            modelID: ModelID.make("gpt-4"),
+          },
+          time: {
+            created: Date.now(),
+          },
+        })
+
+        await Session.updatePart({
+          id: PartID.ascending(),
+          messageID: userMsg2.id,
+          sessionID,
+          type: "text",
+          text: "What's the capital of France?",
+        })
+
+        // Create another assistant response
+        const assistantMsg2: MessageV2.Assistant = {
+          id: MessageID.ascending(),
+          role: "assistant",
+          sessionID,
+          mode: "default",
+          agent: "default",
+          path: {
+            cwd: tmp.path,
+            root: tmp.path,
+          },
+          cost: 0,
+          tokens: {
+            output: 0,
+            input: 0,
+            reasoning: 0,
+            cache: { read: 0, write: 0 },
+          },
+          modelID: ModelID.make("gpt-4"),
+          providerID: ProviderID.make("openai"),
+          parentID: userMsg2.id,
+          time: {
+            created: Date.now(),
+          },
+          finish: "end_turn",
+        }
+        await Session.updateMessage(assistantMsg2)
+
+        await Session.updatePart({
+          id: PartID.ascending(),
+          messageID: assistantMsg2.id,
+          sessionID,
+          type: "text",
+          text: "The capital of France is Paris.",
+        })
+
+        // Verify messages before revert
+        let messages = await Session.messages({ sessionID })
+        expect(messages.length).toBe(4) // 2 user + 2 assistant messages
+        const messageIds = messages.map((m) => m.info.id)
+        expect(messageIds).toContain(userMsg1.id)
+        expect(messageIds).toContain(userMsg2.id)
+        expect(messageIds).toContain(assistantMsg1.id)
+        expect(messageIds).toContain(assistantMsg2.id)
+
+        // Revert the last user message (userMsg2)
+        await SessionRevert.revert({
+          sessionID,
+          messageID: userMsg2.id,
+        })
+
+        // Check that revert state is set
+        let sessionInfo = await Session.get(sessionID)
+        expect(sessionInfo.revert).toBeDefined()
+        const revertMessageID = sessionInfo.revert?.messageID
+        expect(revertMessageID).toBeDefined()
+
+        // Messages should still be in the list (not removed yet, just marked for revert)
+        messages = await Session.messages({ sessionID })
+        expect(messages.length).toBe(4)
+
+        // Now clean up the revert state (this is what the compact endpoint should do)
+        await SessionRevert.cleanup(sessionInfo)
+
+        // After cleanup, the reverted messages (those after the revert point) should be removed
+        messages = await Session.messages({ sessionID })
+        const remainingIds = messages.map((m) => m.info.id)
+        // The revert point is somewhere in the message chain, so we should have fewer messages
+        expect(messages.length).toBeLessThan(4)
+        // userMsg2 and assistantMsg2 should be removed (they come after the revert point)
+        expect(remainingIds).not.toContain(userMsg2.id)
+        expect(remainingIds).not.toContain(assistantMsg2.id)
+
+        // Revert state should be cleared
+        sessionInfo = await Session.get(sessionID)
+        expect(sessionInfo.revert).toBeUndefined()
+
+        // Clean up
+        await Session.remove(sessionID)
+      },
+    })
+  })
+
+  test("should properly clean up revert state before creating compaction message", async () => {
+    await using tmp = await tmpdir({ git: true })
+    await Instance.provide({
+      directory: tmp.path,
+      fn: async () => {
+        // Create a session
+        const session = await Session.create({})
+        const sessionID = session.id
+
+        // Create initial messages
+        const userMsg = await Session.updateMessage({
+          id: MessageID.ascending(),
+          role: "user",
+          sessionID,
+          agent: "default",
+          model: {
+            providerID: ProviderID.make("openai"),
+            modelID: ModelID.make("gpt-4"),
+          },
+          time: {
+            created: Date.now(),
+          },
+        })
+
+        await Session.updatePart({
+          id: PartID.ascending(),
+          messageID: userMsg.id,
+          sessionID,
+          type: "text",
+          text: "Hello",
+        })
+
+        const assistantMsg: MessageV2.Assistant = {
+          id: MessageID.ascending(),
+          role: "assistant",
+          sessionID,
+          mode: "default",
+          agent: "default",
+          path: {
+            cwd: tmp.path,
+            root: tmp.path,
+          },
+          cost: 0,
+          tokens: {
+            output: 0,
+            input: 0,
+            reasoning: 0,
+            cache: { read: 0, write: 0 },
+          },
+          modelID: ModelID.make("gpt-4"),
+          providerID: ProviderID.make("openai"),
+          parentID: userMsg.id,
+          time: {
+            created: Date.now(),
+          },
+          finish: "end_turn",
+        }
+        await Session.updateMessage(assistantMsg)
+
+        await Session.updatePart({
+          id: PartID.ascending(),
+          messageID: assistantMsg.id,
+          sessionID,
+          type: "text",
+          text: "Hi there!",
+        })
+
+        // Revert the user message
+        await SessionRevert.revert({
+          sessionID,
+          messageID: userMsg.id,
+        })
+
+        // Check that revert state is set
+        let sessionInfo = await Session.get(sessionID)
+        expect(sessionInfo.revert).toBeDefined()
+
+        // Simulate what the compact endpoint does: cleanup revert before creating compaction
+        await SessionRevert.cleanup(sessionInfo)
+
+        // Verify revert state is cleared
+        sessionInfo = await Session.get(sessionID)
+        expect(sessionInfo.revert).toBeUndefined()
+
+        // Verify messages are properly cleaned up
+        const messages = await Session.messages({ sessionID })
+        expect(messages.length).toBe(0) // All messages should be reverted
+
+        // Clean up
+        await Session.remove(sessionID)
+      },
+    })
+  })
+})

packages/tfcode/test/session/session.test.ts

@@ -0,0 +1,142 @@
+import { describe, expect, test } from "bun:test"
+import path from "path"
+import { Session } from "../../src/session"
+import { Bus } from "../../src/bus"
+import { Log } from "../../src/util/log"
+import { Instance } from "../../src/project/instance"
+import { MessageV2 } from "../../src/session/message-v2"
+import { MessageID, PartID } from "../../src/session/schema"
+
+const projectRoot = path.join(__dirname, "../..")
+Log.init({ print: false })
+
+describe("session.started event", () => {
+  test("should emit session.started event when session is created", async () => {
+    await Instance.provide({
+      directory: projectRoot,
+      fn: async () => {
+        let eventReceived = false
+        let receivedInfo: Session.Info | undefined
+
+        const unsub = Bus.subscribe(Session.Event.Created, (event) => {
+          eventReceived = true
+          receivedInfo = event.properties.info as Session.Info
+        })
+
+        const session = await Session.create({})
+
+        await new Promise((resolve) => setTimeout(resolve, 100))
+
+        unsub()
+
+        expect(eventReceived).toBe(true)
+        expect(receivedInfo).toBeDefined()
+        expect(receivedInfo?.id).toBe(session.id)
+        expect(receivedInfo?.projectID).toBe(session.projectID)
+        expect(receivedInfo?.directory).toBe(session.directory)
+        expect(receivedInfo?.title).toBe(session.title)
+
+        await Session.remove(session.id)
+      },
+    })
+  })
+
+  test("session.started event should be emitted before session.updated", async () => {
+    await Instance.provide({
+      directory: projectRoot,
+      fn: async () => {
+        const events: string[] = []
+
+        const unsubStarted = Bus.subscribe(Session.Event.Created, () => {
+          events.push("started")
+        })
+
+        const unsubUpdated = Bus.subscribe(Session.Event.Updated, () => {
+          events.push("updated")
+        })
+
+        const session = await Session.create({})
+
+        await new Promise((resolve) => setTimeout(resolve, 100))
+
+        unsubStarted()
+        unsubUpdated()
+
+        expect(events).toContain("started")
+        expect(events).toContain("updated")
+        expect(events.indexOf("started")).toBeLessThan(events.indexOf("updated"))
+
+        await Session.remove(session.id)
+      },
+    })
+  })
+})
+
+describe("step-finish token propagation via Bus event", () => {
+  test(
+    "non-zero tokens propagate through PartUpdated event",
+    async () => {
+      await Instance.provide({
+        directory: projectRoot,
+        fn: async () => {
+          const session = await Session.create({})
+
+          const messageID = MessageID.ascending()
+          await Session.updateMessage({
+            id: messageID,
+            sessionID: session.id,
+            role: "user",
+            time: { created: Date.now() },
+            agent: "user",
+            model: { providerID: "test", modelID: "test" },
+            tools: {},
+            mode: "",
+          } as unknown as MessageV2.Info)
+
+          let received: MessageV2.Part | undefined
+          const unsub = Bus.subscribe(MessageV2.Event.PartUpdated, (event) => {
+            received = event.properties.part
+          })
+
+          const tokens = {
+            total: 1500,
+            input: 500,
+            output: 800,
+            reasoning: 200,
+            cache: { read: 100, write: 50 },
+          }
+
+          const partInput = {
+            id: PartID.ascending(),
+            messageID,
+            sessionID: session.id,
+            type: "step-finish" as const,
+            reason: "stop",
+            cost: 0.005,
+            tokens,
+          }
+
+          await Session.updatePart(partInput)
+
+          await new Promise((resolve) => setTimeout(resolve, 100))
+
+          expect(received).toBeDefined()
+          expect(received!.type).toBe("step-finish")
+          const finish = received as MessageV2.StepFinishPart
+          expect(finish.tokens.input).toBe(500)
+          expect(finish.tokens.output).toBe(800)
+          expect(finish.tokens.reasoning).toBe(200)
+          expect(finish.tokens.total).toBe(1500)
+          expect(finish.tokens.cache.read).toBe(100)
+          expect(finish.tokens.cache.write).toBe(50)
+          expect(finish.cost).toBe(0.005)
+          expect(received).not.toBe(partInput)
+
+          unsub()
+          await Session.remove(session.id)
+        },
+      })
+    },
+    { timeout: 30000 },
+  )
+})

packages/tfcode/test/session/structured-output-integration.test.ts

@@ -0,0 +1,233 @@
+import { describe, expect, test } from "bun:test"
+import path from "path"
+import { Session } from "../../src/session"
+import { SessionPrompt } from "../../src/session/prompt"
+import { Log } from "../../src/util/log"
+import { Instance } from "../../src/project/instance"
+import { MessageV2 } from "../../src/session/message-v2"
+
+const projectRoot = path.join(__dirname, "../..")
+Log.init({ print: false })
+
+// Skip tests if no API key is available
+const hasApiKey = !!process.env.ANTHROPIC_API_KEY
+
+// Helper to run test within Instance context
+async function withInstance<T>(fn: () => Promise<T>): Promise<T> {
+  return Instance.provide({
+    directory: projectRoot,
+    fn,
+  })
+}
+
+describe("StructuredOutput Integration", () => {
+  test.skipIf(!hasApiKey)(
+    "produces structured output with simple schema",
+    async () => {
+      await withInstance(async () => {
+        const session = await Session.create({ title: "Structured Output Test" })
+
+        const result = await SessionPrompt.prompt({
+          sessionID: session.id,
+          parts: [
+            {
+              type: "text",
+              text: "What is 2 + 2? Provide a simple answer.",
+            },
+          ],
+          format: {
+            type: "json_schema",
+            schema: {
+              type: "object",
+              properties: {
+                answer: { type: "number", description: "The numerical answer" },
+                explanation: { type: "string", description: "Brief explanation" },
+              },
+              required: ["answer"],
+            },
+            retryCount: 0,
+          },
+        })
+
+        // Verify structured output was captured (only on assistant messages)
+        expect(result.info.role).toBe("assistant")
+        if (result.info.role === "assistant") {
+          expect(result.info.structured).toBeDefined()
+          expect(typeof result.info.structured).toBe("object")
+
+          const output = result.info.structured as any
+          expect(output.answer).toBe(4)
+
+          // Verify no error was set
+          expect(result.info.error).toBeUndefined()
+        }
+
+        // Clean up
+        // Note: Not removing session to avoid race with background SessionSummary.summarize
+      })
+    },
+    60000,
+  )
+
+  test.skipIf(!hasApiKey)(
+    "produces structured output with nested objects",
+    async () => {
+      await withInstance(async () => {
+        const session = await Session.create({ title: "Nested Schema Test" })
+
+        const result = await SessionPrompt.prompt({
+          sessionID: session.id,
+          parts: [
+            {
+              type: "text",
+              text: "Tell me about Anthropic company in a structured format.",
+            },
+          ],
+          format: {
+            type: "json_schema",
+            schema: {
+              type: "object",
+              properties: {
+                company: {
+                  type: "object",
+                  properties: {
+                    name: { type: "string" },
+                    founded: { type: "number" },
+                  },
+                  required: ["name", "founded"],
+                },
+                products: {
+                  type: "array",
+                  items: { type: "string" },
+                },
+              },
+              required: ["company"],
+            },
+            retryCount: 0,
+          },
+        })
+
+        // Verify structured output was captured (only on assistant messages)
+        expect(result.info.role).toBe("assistant")
+        if (result.info.role === "assistant") {
+          expect(result.info.structured).toBeDefined()
+          const output = result.info.structured as any
+
+          expect(output.company).toBeDefined()
+          expect(output.company.name).toBe("Anthropic")
+          expect(typeof output.company.founded).toBe("number")
+
+          if (output.products) {
+            expect(Array.isArray(output.products)).toBe(true)
+          }
+
+          // Verify no error was set
+          expect(result.info.error).toBeUndefined()
+        }
+
+        // Clean up
+        // Note: Not removing session to avoid race with background SessionSummary.summarize
+      })
+    },
+    60000,
+  )
+
+  test.skipIf(!hasApiKey)(
+    "works with text outputFormat (default)",
+    async () => {
+      await withInstance(async () => {
+        const session = await Session.create({ title: "Text Output Test" })
+
+        const result = await SessionPrompt.prompt({
+          sessionID: session.id,
+          parts: [
+            {
+              type: "text",
+              text: "Say hello.",
+            },
+          ],
+          format: {
+            type: "text",
+          },
+        })
+
+        // Verify no structured output (text mode) and no error
+        expect(result.info.role).toBe("assistant")
+        if (result.info.role === "assistant") {
+          expect(result.info.structured).toBeUndefined()
+          expect(result.info.error).toBeUndefined()
+        }
+
+        // Verify we got a response with parts
+        expect(result.parts.length).toBeGreaterThan(0)
+
+        // Clean up
+        // Note: Not removing session to avoid race with background SessionSummary.summarize
+      })
+    },
+    60000,
+  )
+
+  test.skipIf(!hasApiKey)(
+    "stores outputFormat on user message",
+    async () => {
+      await withInstance(async () => {
+        const session = await Session.create({ title: "OutputFormat Storage Test" })
+
+        await SessionPrompt.prompt({
+          sessionID: session.id,
+          parts: [
+            {
+              type: "text",
+              text: "What is 1 + 1?",
+            },
+          ],
+          format: {
+            type: "json_schema",
+            schema: {
+              type: "object",
+              properties: {
+                result: { type: "number" },
+              },
+              required: ["result"],
+            },
+            retryCount: 3,
+          },
+        })
+
+        // Get all messages from session
+        const messages = await Session.messages({ sessionID: session.id })
+        const userMessage = messages.find((m) => m.info.role === "user")
+
+        // Verify outputFormat was stored on user message
+        expect(userMessage).toBeDefined()
+        if (userMessage?.info.role === "user") {
+          expect(userMessage.info.format).toBeDefined()
+          expect(userMessage.info.format?.type).toBe("json_schema")
+          if (userMessage.info.format?.type === "json_schema") {
+            expect(userMessage.info.format.retryCount).toBe(3)
+          }
+        }
+
+        // Clean up
+        // Note: Not removing session to avoid race with background SessionSummary.summarize
+      })
+    },
+    60000,
+  )
+
+  test("unit test: StructuredOutputError is properly structured", () => {
+    const error = new MessageV2.StructuredOutputError({
+      message: "Failed to produce valid structured output after 3 attempts",
+      retries: 3,
+    })
+
+    expect(error.name).toBe("StructuredOutputError")
+    expect(error.data.message).toContain("3 attempts")
+    expect(error.data.retries).toBe(3)
+
+    const obj = error.toObject()
+    expect(obj.name).toBe("StructuredOutputError")
+    expect(obj.data.retries).toBe(3)
+  })
+})

packages/tfcode/test/session/structured-output.test.ts

@@ -0,0 +1,386 @@
+import { describe, expect, test } from "bun:test"
+import { MessageV2 } from "../../src/session/message-v2"
+import { SessionPrompt } from "../../src/session/prompt"
+import { SessionID, MessageID } from "../../src/session/schema"
+
+describe("structured-output.OutputFormat", () => {
+  test("parses text format", () => {
+    const result = MessageV2.Format.safeParse({ type: "text" })
+    expect(result.success).toBe(true)
+    if (result.success) {
+      expect(result.data.type).toBe("text")
+    }
+  })
+
+  test("parses json_schema format with defaults", () => {
+    const result = MessageV2.Format.safeParse({
+      type: "json_schema",
+      schema: { type: "object", properties: { name: { type: "string" } } },
+    })
+    expect(result.success).toBe(true)
+    if (result.success) {
+      expect(result.data.type).toBe("json_schema")
+      if (result.data.type === "json_schema") {
+        expect(result.data.retryCount).toBe(2) // default value
+      }
+    }
+  })
+
+  test("parses json_schema format with custom retryCount", () => {
+    const result = MessageV2.Format.safeParse({
+      type: "json_schema",
+      schema: { type: "object" },
+      retryCount: 5,
+    })
+    expect(result.success).toBe(true)
+    if (result.success && result.data.type === "json_schema") {
+      expect(result.data.retryCount).toBe(5)
+    }
+  })
+
+  test("rejects invalid type", () => {
+    const result = MessageV2.Format.safeParse({ type: "invalid" })
+    expect(result.success).toBe(false)
+  })
+
+  test("rejects json_schema without schema", () => {
+    const result = MessageV2.Format.safeParse({ type: "json_schema" })
+    expect(result.success).toBe(false)
+  })
+
+  test("rejects negative retryCount", () => {
+    const result = MessageV2.Format.safeParse({
+      type: "json_schema",
+      schema: { type: "object" },
+      retryCount: -1,
+    })
+    expect(result.success).toBe(false)
+  })
+})
+
+describe("structured-output.StructuredOutputError", () => {
+  test("creates error with message and retries", () => {
+    const error = new MessageV2.StructuredOutputError({
+      message: "Failed to validate",
+      retries: 3,
+    })
+
+    expect(error.name).toBe("StructuredOutputError")
+    expect(error.data.message).toBe("Failed to validate")
+    expect(error.data.retries).toBe(3)
+  })
+
+  test("converts to object correctly", () => {
+    const error = new MessageV2.StructuredOutputError({
+      message: "Test error",
+      retries: 2,
+    })
+
+    const obj = error.toObject()
+    expect(obj.name).toBe("StructuredOutputError")
+    expect(obj.data.message).toBe("Test error")
+    expect(obj.data.retries).toBe(2)
+  })
+
+  test("isInstance correctly identifies error", () => {
+    const error = new MessageV2.StructuredOutputError({
+      message: "Test",
+      retries: 1,
+    })
+
+    expect(MessageV2.StructuredOutputError.isInstance(error)).toBe(true)
+    expect(MessageV2.StructuredOutputError.isInstance({ name: "other" })).toBe(false)
+  })
+})
+
+describe("structured-output.UserMessage", () => {
+  test("user message accepts outputFormat", () => {
+    const result = MessageV2.User.safeParse({
+      id: MessageID.ascending(),
+      sessionID: SessionID.descending(),
+      role: "user",
+      time: { created: Date.now() },
+      agent: "default",
+      model: { providerID: "anthropic", modelID: "claude-3" },
+      outputFormat: {
+        type: "json_schema",
+        schema: { type: "object" },
+      },
+    })
+    expect(result.success).toBe(true)
+  })
+
+  test("user message works without outputFormat (optional)", () => {
+    const result = MessageV2.User.safeParse({
+      id: MessageID.ascending(),
+      sessionID: SessionID.descending(),
+      role: "user",
+      time: { created: Date.now() },
+      agent: "default",
+      model: { providerID: "anthropic", modelID: "claude-3" },
+    })
+    expect(result.success).toBe(true)
+  })
+})
+
+describe("structured-output.AssistantMessage", () => {
+  const baseAssistantMessage = {
+    id: MessageID.ascending(),
+    sessionID: SessionID.descending(),
+    role: "assistant" as const,
+    parentID: MessageID.ascending(),
+    modelID: "claude-3",
+    providerID: "anthropic",
+    mode: "default",
+    agent: "default",
+    path: { cwd: "/test", root: "/test" },
+    cost: 0.001,
+    tokens: { input: 100, output: 50, reasoning: 0, cache: { read: 0, write: 0 } },
+    time: { created: Date.now() },
+  }
+
+  test("assistant message accepts structured", () => {
+    const result = MessageV2.Assistant.safeParse({
+      ...baseAssistantMessage,
+      structured: { company: "Anthropic", founded: 2021 },
+    })
+    expect(result.success).toBe(true)
+    if (result.success) {
+      expect(result.data.structured).toEqual({ company: "Anthropic", founded: 2021 })
+    }
+  })
+
+  test("assistant message works without structured_output (optional)", () => {
+    const result = MessageV2.Assistant.safeParse(baseAssistantMessage)
+    expect(result.success).toBe(true)
+  })
+})
+
+describe("structured-output.createStructuredOutputTool", () => {
+  test("creates tool with correct id", () => {
+    const tool = SessionPrompt.createStructuredOutputTool({
+      schema: { type: "object", properties: { name: { type: "string" } } },
+      onSuccess: () => {},
+    })
+
+    // AI SDK tool type doesn't expose id, but we set it internally
+    expect((tool as any).id).toBe("StructuredOutput")
+  })
+
+  test("creates tool with description", () => {
+    const tool = SessionPrompt.createStructuredOutputTool({
+      schema: { type: "object" },
+      onSuccess: () => {},
+    })
+
+    expect(tool.description).toContain("structured format")
+  })
+
+  test("creates tool with schema as inputSchema", () => {
+    const schema = {
+      type: "object",
+      properties: {
+        company: { type: "string" },
+        founded: { type: "number" },
+      },
+      required: ["company"],
+    }
+
+    const tool = SessionPrompt.createStructuredOutputTool({
+      schema,
+      onSuccess: () => {},
+    })
+
+    // AI SDK wraps schema in { jsonSchema: {...} }
+    expect(tool.inputSchema).toBeDefined()
+    const inputSchema = tool.inputSchema as any
+    expect(inputSchema.jsonSchema?.properties?.company).toBeDefined()
+    expect(inputSchema.jsonSchema?.properties?.founded).toBeDefined()
+  })
+
+  test("strips $schema property from inputSchema", () => {
+    const schema = {
+      $schema: "http://json-schema.org/draft-07/schema#",
+      type: "object",
+      properties: { name: { type: "string" } },
+    }
+
+    const tool = SessionPrompt.createStructuredOutputTool({
+      schema,
+      onSuccess: () => {},
+    })
+
+    // AI SDK wraps schema in { jsonSchema: {...} }
+    const inputSchema = tool.inputSchema as any
+    expect(inputSchema.jsonSchema?.$schema).toBeUndefined()
+  })
+
+  test("execute calls onSuccess with valid args", async () => {
+    let capturedOutput: unknown
+
+    const tool = SessionPrompt.createStructuredOutputTool({
+      schema: { type: "object", properties: { name: { type: "string" } } },
+      onSuccess: (output) => {
+        capturedOutput = output
+      },
+    })
+
+    expect(tool.execute).toBeDefined()
+    const testArgs = { name: "Test Company" }
+    const result = await tool.execute!(testArgs, {
+      toolCallId: "test-call-id",
+      messages: [],
+      abortSignal: undefined as any,
+    })
+
+    expect(capturedOutput).toEqual(testArgs)
+    expect(result.output).toBe("Structured output captured successfully.")
+    expect(result.metadata.valid).toBe(true)
+  })
+
+  test("AI SDK validates schema before execute - missing required field", async () => {
+    // Note: The AI SDK validates the input against the schema BEFORE calling execute()
+    // So invalid inputs never reach the tool's execute function
+    // This test documents the expected schema behavior
+    const tool = SessionPrompt.createStructuredOutputTool({
+      schema: {
+        type: "object",
+        properties: {
+          name: { type: "string" },
+          age: { type: "number" },
+        },
+        required: ["name", "age"],
+      },
+      onSuccess: () => {},
+    })
+
+    // The schema requires both 'name' and 'age'
+    expect(tool.inputSchema).toBeDefined()
+    const inputSchema = tool.inputSchema as any
+    expect(inputSchema.jsonSchema?.required).toContain("name")
+    expect(inputSchema.jsonSchema?.required).toContain("age")
+  })
+
+  test("AI SDK validates schema types before execute - wrong type", async () => {
+    // Note: The AI SDK validates the input against the schema BEFORE calling execute()
+    // So invalid inputs never reach the tool's execute function
+    // This test documents the expected schema behavior
+    const tool = SessionPrompt.createStructuredOutputTool({
+      schema: {
+        type: "object",
+        properties: {
+          count: { type: "number" },
+        },
+        required: ["count"],
+      },
+      onSuccess: () => {},
+    })
+
+    // The schema defines 'count' as a number
+    expect(tool.inputSchema).toBeDefined()
+    const inputSchema = tool.inputSchema as any
+    expect(inputSchema.jsonSchema?.properties?.count?.type).toBe("number")
+  })
+
+  test("execute handles nested objects", async () => {
+    let capturedOutput: unknown
+
+    const tool = SessionPrompt.createStructuredOutputTool({
+      schema: {
+        type: "object",
+        properties: {
+          user: {
+            type: "object",
+            properties: {
+              name: { type: "string" },
+              email: { type: "string" },
+            },
+            required: ["name"],
+          },
+        },
+        required: ["user"],
+      },
+      onSuccess: (output) => {
+        capturedOutput = output
+      },
+    })
+
+    // Valid nested object - AI SDK validates before calling execute()
+    const validResult = await tool.execute!(
+      { user: { name: "John", email: "john@test.com" } },
+      {
+        toolCallId: "test-call-id",
+        messages: [],
+        abortSignal: undefined as any,
+      },
+    )
+
+    expect(capturedOutput).toEqual({ user: { name: "John", email: "john@test.com" } })
+    expect(validResult.metadata.valid).toBe(true)
+
+    // Verify schema has correct nested structure
+    const inputSchema = tool.inputSchema as any
+    expect(inputSchema.jsonSchema?.properties?.user?.type).toBe("object")
+    expect(inputSchema.jsonSchema?.properties?.user?.properties?.name?.type).toBe("string")
+    expect(inputSchema.jsonSchema?.properties?.user?.required).toContain("name")
+  })
+
+  test("execute handles arrays", async () => {
+    let capturedOutput: unknown
+
+    const tool = SessionPrompt.createStructuredOutputTool({
+      schema: {
+        type: "object",
+        properties: {
+          tags: {
+            type: "array",
+            items: { type: "string" },
+          },
+        },
+        required: ["tags"],
+      },
+      onSuccess: (output) => {
+        capturedOutput = output
+      },
+    })
+
+    // Valid array - AI SDK validates before calling execute()
+    const validResult = await tool.execute!(
+      { tags: ["a", "b", "c"] },
+      {
+        toolCallId: "test-call-id",
+        messages: [],
+        abortSignal: undefined as any,
+      },
+    )
+
+    expect(capturedOutput).toEqual({ tags: ["a", "b", "c"] })
+    expect(validResult.metadata.valid).toBe(true)
+
+    // Verify schema has correct array structure
+    const inputSchema = tool.inputSchema as any
+    expect(inputSchema.jsonSchema?.properties?.tags?.type).toBe("array")
+    expect(inputSchema.jsonSchema?.properties?.tags?.items?.type).toBe("string")
+  })
+
+  test("toModelOutput returns text value", () => {
+    const tool = SessionPrompt.createStructuredOutputTool({
+      schema: { type: "object" },
+      onSuccess: () => {},
+    })
+
+    expect(tool.toModelOutput).toBeDefined()
+    const modelOutput = tool.toModelOutput!({
+      output: "Test output",
+      title: "Test",
+      metadata: { valid: true },
+    })
+
+    expect(modelOutput.type).toBe("text")
+    expect(modelOutput.value).toBe("Test output")
+  })
+
+  // Note: Retry behavior is handled by the AI SDK and the prompt loop, not the tool itself
+  // The tool simply calls onSuccess when execute() is called with valid args
+  // See prompt.ts loop() for actual retry logic
+})

packages/tfcode/test/session/system.test.ts

@@ -0,0 +1,59 @@
+import { describe, expect, test } from "bun:test"
+import path from "path"
+import { Agent } from "../../src/agent/agent"
+import { Instance } from "../../src/project/instance"
+import { SystemPrompt } from "../../src/session/system"
+import { tmpdir } from "../fixture/fixture"
+
+describe("session.system", () => {
+  test("skills output is sorted by name and stable across calls", async () => {
+    await using tmp = await tmpdir({
+      git: true,
+      init: async (dir) => {
+        for (const [name, description] of [
+          ["zeta-skill", "Zeta skill."],
+          ["alpha-skill", "Alpha skill."],
+          ["middle-skill", "Middle skill."],
+        ]) {
+          const skillDir = path.join(dir, ".opencode", "skill", name)
+          await Bun.write(
+            path.join(skillDir, "SKILL.md"),
+            `---
+name: ${name}
+description: ${description}
+---
+
+# ${name}
+`,
+          )
+        }
+      },
+    })
+
+    const home = process.env.OPENCODE_TEST_HOME
+    process.env.OPENCODE_TEST_HOME = tmp.path
+
+    try {
+      await Instance.provide({
+        directory: tmp.path,
+        fn: async () => {
+          const build = await Agent.get("build")
+          const first = await SystemPrompt.skills(build!)
+          const second = await SystemPrompt.skills(build!)
+
+          expect(first).toBe(second)
+
+          const alpha = first!.indexOf("<name>alpha-skill</name>")
+          const middle = first!.indexOf("<name>middle-skill</name>")
+          const zeta = first!.indexOf("<name>zeta-skill</name>")
+
+          expect(alpha).toBeGreaterThan(-1)
+          expect(middle).toBeGreaterThan(alpha)
+          expect(zeta).toBeGreaterThan(middle)
+        },
+      })
+    } finally {
+      process.env.OPENCODE_TEST_HOME = home
+    }
+  })
+})