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393 lines
12 KiB
JSON
393 lines
12 KiB
JSON
[
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{
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"t": 1500,
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"event": "user_message",
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"text": "make me a simple flowchart. "
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},
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{
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"t": 8383,
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"event": "thinking",
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"duration": 800
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},
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{
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"t": 10022,
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"event": "stream_text",
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"text": "\n\n```mermaid\nflowchart TD\n A[Start] --> B{Decision?}\n B -->|Yes| C[Do Something]\n B -->|No| D[Do Something Else]\n C --> E[Process Result]\n D --> E\n E --> F{Another Check?}\n F -->|Yes| B\n F -->|No| G[End]\n```",
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"speed": 80
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},
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{
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"t": 11500,
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"event": "token_usage",
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"input": 447,
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"output": 96,
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"cache_read": 6487,
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"cache_creation": 8560
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},
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{
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"t": 11500,
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"event": "done"
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},
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{
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"t": 14500,
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"event": "user_message",
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"text": "epxlore this codebase, and then make just a simple diagram of the memory arhcitecutre"
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},
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{
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"t": 18318,
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"event": "thinking",
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"duration": 800
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},
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{
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"t": 18931,
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"event": "stream_text",
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"text": "\n\nLet me explore the memory-related code in this codebase first.",
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"speed": 80
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},
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{
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"t": 19085,
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"event": "token_usage",
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"input": 37,
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"output": 156,
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"cache_read": 15047,
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"cache_creation": 541
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},
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{
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"t": 19085,
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"event": "tool_start",
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"name": "batch",
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"input": {
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"tool_calls": [
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{
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"parameters": {
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"include": "*.rs",
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"path": "src/",
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"pattern": "mod memory|pub struct Memory|memory"
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},
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"tool": "file_grep"
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},
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{
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"parameters": {
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"path": "src/"
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},
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"tool": "ls"
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},
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{
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"parameters": {
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"pattern": "src/**/*memory*"
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},
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"tool": "file_glob"
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}
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]
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}
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},
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{
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"t": 19238,
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"event": "tool_done",
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"name": "batch",
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"output": "--- [1] file_grep ---\nFound 100 matches for 'mod memory|pub struct Memory|memory'\n\nprovider/anthropic.rs:\n 1055: // Dynamic content - NOT cached (date, git status, memory)\n\nprovider/openrouter.rs:\n 400: /// In-memory cache\n 612: /// In-memory cache of per-model endpoint data\n 979: // Check in-memory cache first\n 1024: // Update in-memory cache\n 1036: // Clear in-memory cache\n 1058: // Check in-memory cache\n 1111: // Update in-memo...",
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"is_error": false
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},
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{
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"t": 22045,
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"event": "thinking",
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"duration": 800
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},
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{
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"t": 22659,
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"event": "token_usage",
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"input": 814,
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"output": 211,
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"cache_read": 15588,
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"cache_creation": 188
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},
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{
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"t": 22659,
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"event": "tool_start",
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"name": "batch",
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"input": {
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"tool_calls": [
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{
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"parameters": {
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"file_path": "src/memory.rs",
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"limit": 120
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},
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"tool": "file_read"
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},
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{
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"parameters": {
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"file_path": "src/memory_graph.rs",
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"limit": 120
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},
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"tool": "file_read"
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},
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{
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"parameters": {
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"file_path": "src/memory_agent.rs",
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"limit": 120
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},
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"tool": "file_read"
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},
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{
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"parameters": {
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"file_path": "src/tool/memory.rs",
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"limit": 120
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},
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"tool": "file_read"
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},
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{
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"parameters": {
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"file_path": "src/tool/remember.rs",
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"limit": 80
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},
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"tool": "file_read"
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}
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]
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}
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},
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{
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"t": 22812,
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"event": "tool_done",
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"name": "batch",
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"output": "--- [1] file_read ---\n 1\t//! Memory system for cross-session learning\n 2\t//!\n 3\t//! Provides persistent memory that survives across sessions, organized by:\n 4\t//! - Project (per working directory)\n 5\t//! - Global (user-level preferences)\n 6\t//!\n 7\t//! Integrates with the Haiku sidecar for relevance verification and extraction.\n 8\t\n 9\tuse crate::memory_graph::{EdgeKind, MemoryGraph, GRAPH_VERSION};\n 10\tuse crate::sidecar::HaikuSidecar;\n 11\tuse crate::storage;\n ...",
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"is_error": false
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},
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{
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"t": 25459,
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"event": "thinking",
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"duration": 800
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},
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{
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"t": 26072,
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"event": "token_usage",
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"input": 1840,
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"output": 252,
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"cache_read": 15776,
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"cache_creation": 1025
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},
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{
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"t": 26072,
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"event": "tool_start",
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"name": "batch",
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"input": {
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"tool_calls": [
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{
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"parameters": {
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"file_path": "src/memory.rs",
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"limit": 200,
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"offset": 120
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},
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"tool": "file_read"
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},
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{
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"parameters": {
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"file_path": "src/memory_graph.rs",
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"limit": 150,
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"offset": 120
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},
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"tool": "file_read"
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},
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{
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"parameters": {
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"file_path": "src/memory_agent.rs",
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"limit": 150,
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"offset": 120
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},
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"tool": "file_read"
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},
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{
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"parameters": {
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"include": "*.rs",
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"path": "src/",
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"pattern": "pub struct.*Embed|mod embedding|pub fn embed"
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},
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"tool": "file_grep"
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},
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{
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"parameters": {
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"include": "*.rs",
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"path": "src/",
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"pattern": "HaikuSidecar|mod sidecar"
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},
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"tool": "file_grep"
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}
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]
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}
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},
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{
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"t": 26225,
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"event": "tool_done",
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"name": "batch",
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"output": "--- [1] file_read ---\n 121\t .map(str::trim)\n 122\t .filter(|line| !line.is_empty())\n 123\t .collect::<Vec<_>>()\n 124\t .join(\"\\n\")\n 125\t .to_lowercase()\n 126\t}\n 127\t\n 128\t/// Take pending memory if available and fresh\n 129\tpub fn take_pending_memory() -> Option<PendingMemory> {\n 130\t if let Ok(mut guard) = PENDING_MEMORY.lock() {\n 131\t if let Some(pending) = guard.take() {\n 132\t if !pending.is_fresh() {\n 133\t ret...",
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"is_error": false
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},
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{
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"t": 28363,
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"event": "thinking",
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"duration": 800
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},
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{
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"t": 28977,
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"event": "token_usage",
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"input": 1606,
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"output": 194,
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"cache_read": 16801,
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"cache_creation": 2092
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},
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{
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"t": 28977,
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"event": "tool_start",
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"name": "batch",
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"input": {
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"tool_calls": [
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{
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"parameters": {
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"file_path": "src/embedding.rs",
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"limit": 80
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},
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"tool": "file_read"
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},
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{
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"parameters": {
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"file_path": "src/sidecar.rs",
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"limit": 80
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},
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"tool": "file_read"
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},
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{
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"parameters": {
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"file_path": "src/memory.rs",
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"limit": 200,
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"offset": 300
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},
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"tool": "file_read"
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},
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{
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"parameters": {
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"include": "*.rs",
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"path": "src/",
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"pattern": "struct MemoryManager|impl MemoryManager"
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},
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"tool": "file_grep"
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}
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]
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}
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},
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{
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"t": 29130,
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"event": "tool_done",
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"name": "batch",
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"output": "--- [1] file_read ---\n 1\t//! Local embedding generation using all-MiniLM-L6-v2\n 2\t//!\n 3\t//! Provides fast, free, consistent embeddings for memory similarity search.\n 4\t//! Uses tract for pure-Rust ONNX inference (no external dependencies).\n 5\t\n 6\tuse anyhow::{Context, Result};\n 7\tuse std::path::PathBuf;\n 8\tuse std::sync::{Arc, Mutex, OnceLock};\n 9\tuse std::time::{Duration, Instant};\n 10\tuse tokenizers::Tokenizer;\n 11\tuse tract_hir::prelude::*;\n 12\tuse tract_o...",
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"is_error": false
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},
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{
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"t": 31410,
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"event": "thinking",
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"duration": 800
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},
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{
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"t": 32024,
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"event": "token_usage",
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"input": 1190,
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},
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{
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"t": 32024,
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"event": "tool_start",
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"name": "batch",
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"input": {
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"tool_calls": [
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{
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"parameters": {
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"file_path": "src/memory.rs",
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"limit": 150,
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"offset": 870
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},
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"tool": "file_read"
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},
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{
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"parameters": {
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"file_path": "src/memory_agent.rs",
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"limit": 200,
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"offset": 150
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},
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"tool": "file_read"
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},
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{
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"parameters": {
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"include": "*.rs",
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"path": "src/",
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"pattern": "MemoryStore|struct.*Store"
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},
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"tool": "file_grep"
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}
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]
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}
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},
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{
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"t": 32177,
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"event": "tool_done",
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"name": "batch",
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"output": "--- [1] file_read ---\n 871\t project_dir: Option<PathBuf>,\n 872\t /// When true, use isolated test storage instead of real memory\n 873\t test_mode: bool,\n 874\t}\n 875\t\n 876\timpl MemoryManager {\n 877\t pub fn new() -> Self {\n 878\t Self {\n 879\t project_dir: None,\n 880\t test_mode: false,\n 881\t }\n 882\t }\n 883\t\n 884\t /// Create a memory manager in test mode (isolated storage)\n 885\t pub fn new_test() -> Self {\n 886\t Self {\n ...",
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"is_error": false
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},
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{
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"t": 33733,
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"event": "thinking",
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"duration": 800
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},
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{
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"t": 34346,
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"event": "token_usage",
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"input": 1077,
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"output": 93,
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"cache_read": 20693,
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"cache_creation": 1357
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},
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{
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"t": 34346,
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"event": "tool_start",
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"name": "read",
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"input": {
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"file_path": "src/memory.rs",
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"limit": 80,
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"offset": 570
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}
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},
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{
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"t": 34500,
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"event": "tool_done",
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"name": "read",
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"output": " 571\t \"entity\" => MemoryCategory::Entity,\n 572\t \"correction\" => MemoryCategory::Correction,\n 573\t other => MemoryCategory::Custom(other.to_string()),\n 574\t })\n 575\t }\n 576\t}\n 577\t\n 578\t#[derive(Debug, Clone, Serialize, Deserialize, Default)]\n 579\tpub struct MemoryStore {\n 580\t pub entries: Vec<MemoryEntry>,\n 581\t #[serde(default)]\n 582\t pub metadata: HashMap<String, String>,\n 583\t}\n 584\t\n 585\timpl MemoryStore {\n 586\t pub f...",
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"is_error": false
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},
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{
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"t": 37500,
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"event": "user_message",
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"text": "a very simplifed version of it "
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},
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{
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"t": 47652,
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"event": "thinking",
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"duration": 800
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},
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{
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"t": 48497,
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"event": "stream_text",
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"text": "\n\n```mermaid\nflowchart TD\n A[\"\ud83e\udde0 Memory Tool\\n(agent-facing API)\"]\n B[\"\ud83d\udcdd Remember Tool\\n(simple notes)\"]\n\n A --> C[\"MemoryManager\\n(project + global scope)\"]\n B --> D[\"NoteStore\\n(JSON file per project)\"]\n\n C --> E[\"MemoryStore\\n(entries, categories, tags)\"]\n C --> F[\"MemoryGraph\\n(edges, clusters, BFS traversal)\"]\n\n E --> G[\"\ud83d\udcbe JSON Files\\n~/.jcode/memory/\"]\n F --> G\n\n H[\"\ud83e\udd16 Memory Agent\\n(background async)\"] --> C\n H --> I[\"Embedder\\n(all-MiniLM-L6-v2, local ONNX)\"]\n H --> J[\"Haiku Sidecar\\n(relevance checks via LLM)\"]\n\n I -->|similarity search| F\n J -->|decide what's relevant| K[\"PENDING_MEMORY\\n(injected into system prompt)\"]\n\n K -->|surfaces memories| L[\"Main Agent\"]\n L -->|context updates| H\n\n style A fill:#4a9eff,color:#fff\n style B fill:#4a9eff,color:#fff\n style H fill:#ff9f43,color:#fff\n style G fill:#2ecc71,color:#fff\n style L fill:#9b59b6,color:#fff\n```\n\nThe main agent talks to the **Memory Agent** in the background. The memory agent uses **local embeddings** for similarity search and a **Haiku sidecar** (cheap LLM) to judge relevance, then injects relevant memories into the system prompt for the main agent to see.",
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"speed": 80
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},
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{
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"t": 52500,
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"event": "token_usage",
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{
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"t": 52500,
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"event": "done"
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}
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] |