Update README.MD and add nano-claude-code v3.0 + original-source-code/src
- README.MD: add original-source-code and nano-claude-code sections, update overview table (4 subprojects), add v3.0 news entry, expand comparison table with memory/multi-agent/skills dimensions - nano-claude-code v3.0: multi-agent package (multi_agent/), memory package (memory/), skill package (skill/) with built-in /commit and /review skills, context compression (compaction.py), tool registry plugin system, diff view, 17 slash commands, 18 built-in tools, 101 tests (~5000 lines total) - original-source-code/src: add raw TypeScript source tree (1884 files) Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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chauncygu
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nano-claude-code/memory/context.py
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nano-claude-code/memory/context.py
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"""Memory context building for system prompt injection.
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Provides:
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get_memory_context() — full context string for system prompt
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find_relevant_memories() — keyword (+ optional AI) relevance filtering
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truncate_index_content() — line + byte truncation with warning
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"""
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from __future__ import annotations
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from pathlib import Path
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from .store import (
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USER_MEMORY_DIR,
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INDEX_FILENAME,
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MAX_INDEX_LINES,
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MAX_INDEX_BYTES,
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get_memory_dir,
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get_index_content,
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load_entries,
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search_memory,
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)
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from .scan import scan_all_memories, format_memory_manifest, memory_freshness_text
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from .types import MEMORY_SYSTEM_PROMPT
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# ── Index truncation ───────────────────────────────────────────────────────
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def truncate_index_content(raw: str) -> str:
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"""Truncate MEMORY.md content to line AND byte limits, appending a warning.
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Matches Claude Code's truncateEntrypointContent:
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- Line-truncates first (natural boundary)
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- Then byte-truncates at the last newline before the cap
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- Appends which limit fired
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"""
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trimmed = raw.strip()
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content_lines = trimmed.split("\n")
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line_count = len(content_lines)
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byte_count = len(trimmed.encode())
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was_line_truncated = line_count > MAX_INDEX_LINES
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was_byte_truncated = byte_count > MAX_INDEX_BYTES
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if not was_line_truncated and not was_byte_truncated:
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return trimmed
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truncated = "\n".join(content_lines[:MAX_INDEX_LINES]) if was_line_truncated else trimmed
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if len(truncated.encode()) > MAX_INDEX_BYTES:
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# Cut at last newline before byte limit
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raw_bytes = truncated.encode()
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cut = raw_bytes[:MAX_INDEX_BYTES].rfind(b"\n")
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truncated = raw_bytes[: cut if cut > 0 else MAX_INDEX_BYTES].decode(errors="replace")
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if was_byte_truncated and not was_line_truncated:
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reason = f"{byte_count:,} bytes (limit: {MAX_INDEX_BYTES:,}) — index entries are too long"
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elif was_line_truncated and not was_byte_truncated:
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reason = f"{line_count} lines (limit: {MAX_INDEX_LINES})"
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else:
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reason = f"{line_count} lines and {byte_count:,} bytes"
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warning = (
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f"\n\n> WARNING: {INDEX_FILENAME} is {reason}. "
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"Only part of it was loaded. Keep index entries to one line under ~150 chars."
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)
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return truncated + warning
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# ── System prompt context ──────────────────────────────────────────────────
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def get_memory_context(include_guidance: bool = False) -> str:
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"""Return memory context for injection into the system prompt.
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Combines user-level and project-level MEMORY.md content (if present).
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Returns empty string when no memories exist.
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Args:
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include_guidance: if True, prepend the full memory system guidance
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(MEMORY_SYSTEM_PROMPT). Normally False since the
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system prompt template already includes brief guidance.
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"""
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parts: list[str] = []
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# User-level index
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user_content = get_index_content("user")
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if user_content:
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truncated = truncate_index_content(user_content)
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parts.append(truncated)
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# Project-level index (labelled separately)
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proj_content = get_index_content("project")
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if proj_content:
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truncated = truncate_index_content(proj_content)
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parts.append(f"[Project memories]\n{truncated}")
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if not parts:
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return ""
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body = "\n\n".join(parts)
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if include_guidance:
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return f"{MEMORY_SYSTEM_PROMPT}\n\n## MEMORY.md\n{body}"
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return body
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# ── Relevant memory finder ─────────────────────────────────────────────────
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def find_relevant_memories(
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query: str,
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max_results: int = 5,
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use_ai: bool = False,
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config: dict | None = None,
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) -> list[dict]:
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"""Find memories relevant to a query.
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Strategy:
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1. Always: keyword match on name + description + content
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2. If use_ai=True and config has a model: use a small AI call to rank
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Returns:
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List of dicts with keys: name, description, type, scope, content,
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file_path, mtime_s, freshness_text
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"""
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# Step 1: Keyword filter
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keyword_results = search_memory(query)
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if not keyword_results:
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return []
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if not use_ai or not config:
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# Return top max_results by recency (newest first)
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from .scan import scan_all_memories
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headers = scan_all_memories()
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path_to_mtime = {h.file_path: h.mtime_s for h in headers}
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results = []
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for entry in keyword_results[:max_results]:
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mtime_s = path_to_mtime.get(entry.file_path, 0)
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results.append({
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"name": entry.name,
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"description": entry.description,
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"type": entry.type,
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"scope": entry.scope,
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"content": entry.content,
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"file_path": entry.file_path,
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"mtime_s": mtime_s,
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"freshness_text": memory_freshness_text(mtime_s),
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})
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results.sort(key=lambda r: r["mtime_s"], reverse=True)
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return results[:max_results]
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# Step 2: AI-powered relevance selection (optional, lightweight)
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return _ai_select_memories(query, keyword_results, max_results, config)
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def _ai_select_memories(
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query: str,
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candidates: list,
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max_results: int,
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config: dict,
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) -> list[dict]:
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"""Use a fast AI call to select the most relevant memories from candidates.
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Falls back to keyword results on any error.
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"""
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try:
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from providers import stream, AssistantTurn
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from .scan import scan_all_memories
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headers = scan_all_memories()
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path_to_mtime = {h.file_path: h.mtime_s for h in headers}
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# Build manifest of candidates only
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manifest_lines = []
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for i, e in enumerate(candidates):
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manifest_lines.append(f"{i}: [{e.type}] {e.name} — {e.description}")
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manifest = "\n".join(manifest_lines)
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system = (
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"You select memories relevant to a query. "
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"Return a JSON object with key 'indices' containing a list of integer indices "
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f"(0-based) from the provided list. Select at most {max_results} entries. "
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"Only include indices clearly relevant to the query. Return {\"indices\": []} if none."
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)
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messages = [{"role": "user", "content": f"Query: {query}\n\nMemories:\n{manifest}"}]
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result_text = ""
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for event in stream(
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model=config.get("model", "claude-haiku-4-5-20251001"),
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system=system,
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messages=messages,
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tool_schemas=[],
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config={**config, "max_tokens": 256, "no_tools": True},
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):
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if isinstance(event, AssistantTurn):
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result_text = event.text
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break
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import json as _json
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parsed = _json.loads(result_text)
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selected_indices = [int(i) for i in parsed.get("indices", []) if isinstance(i, int)]
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except Exception:
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# Fall back to keyword results
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selected_indices = list(range(min(max_results, len(candidates))))
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results = []
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for i in selected_indices[:max_results]:
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if i < 0 or i >= len(candidates):
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continue
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entry = candidates[i]
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mtime_s = path_to_mtime.get(entry.file_path, 0) if "path_to_mtime" in dir() else 0
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results.append({
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"name": entry.name,
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"description": entry.description,
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"type": entry.type,
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"scope": entry.scope,
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"content": entry.content,
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"file_path": entry.file_path,
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"mtime_s": mtime_s,
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"freshness_text": memory_freshness_text(mtime_s),
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})
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return results
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