feat(tools): migrate to litellm for multi-provider support (OpenAI, Gemini, Claude)

requirements.txt

@@ -1,2 +1,2 @@
-anthropic>=0.40.0
+litellm>=1.0.0
 networkx>=3.2

tools/build_graph.py

@@ -25,7 +25,7 @@ import webbrowser
 from pathlib import Path
 from datetime import date
 
-import anthropic
+import os
 
 try:
     import networkx as nx
@@ -64,6 +64,23 @@ def read_file(path: Path) -> str:
     return path.read_text(encoding="utf-8") if path.exists() else ""
 
 
+def call_llm(prompt: str, model_env: str, default_model: str, max_tokens: int = 4096) -> str:
+    try:
+        from litellm import completion
+    except ImportError:
+        print("Error: litellm not installed. Run: pip install litellm")
+        import sys
+        sys.exit(1)
+        
+    model = os.getenv(model_env, default_model)
+    response = completion(
+        model=model,
+        messages=[{"role": "user", "content": prompt}],
+        max_tokens=max_tokens
+    )
+    return response.choices[0].message.content
+
+
 def sha256(text: str) -> str:
     return hashlib.sha256(text.encode()).hexdigest()
 
@@ -143,8 +160,7 @@ def build_extracted_edges(pages: list[Path]) -> list[dict]:
 
 
 def build_inferred_edges(pages: list[Path], existing_edges: list[dict], cache: dict) -> list[dict]:
-    """Pass 2: Claude-inferred semantic relationships."""
+    """Pass 2: API-inferred semantic relationships."""
-    client = anthropic.Anthropic()
     new_edges = []
 
     # Only process pages that changed since last run
@@ -172,12 +188,7 @@ def build_inferred_edges(pages: list[Path], existing_edges: list[dict], cache: d
         content = read_file(p)[:2000]  # truncate for context efficiency
         src = page_id(p)
 
-        response = client.messages.create(
+        prompt = f"""Analyze this wiki page and identify implicit semantic relationships to other pages in the wiki.
-            model="claude-haiku-4-5-20251001",
-            max_tokens=1024,
-            messages=[{
-                "role": "user",
-                "content": f"""Analyze this wiki page and identify implicit semantic relationships to other pages in the wiki.
 
 Source page: {src}
 Content:
@@ -200,10 +211,8 @@ Rules:
 - Do not repeat edges already in the extracted list
 - Return empty array [] if no new relationships found
 """
-            }]
+        raw = call_llm(prompt, "LLM_MODEL_FAST", "claude-3-5-haiku-latest", max_tokens=1024)
-        )
+        raw = raw.strip()
-
-        raw = response.content[0].text.strip()
         raw = re.sub(r"^```(?:json)?\s*", "", raw)
         raw = re.sub(r"\s*```$", "", raw)
 

tools/ingest.py

@@ -23,7 +23,7 @@ import re
 from pathlib import Path
 from datetime import date
 
-import anthropic
+import os
 
 REPO_ROOT = Path(__file__).parent.parent
 WIKI_DIR = REPO_ROOT / "wiki"
@@ -41,6 +41,22 @@ def read_file(path: Path) -> str:
     return path.read_text(encoding="utf-8") if path.exists() else ""
 
 
+def call_llm(prompt: str, max_tokens: int = 8192) -> str:
+    try:
+        from litellm import completion
+    except ImportError:
+        print("Error: litellm not installed. Run: pip install litellm")
+        sys.exit(1)
+        
+    model = os.getenv("LLM_MODEL", "claude-3-5-sonnet-latest")
+    response = completion(
+        model=model,
+        messages=[{"role": "user", "content": prompt}],
+        max_tokens=max_tokens
+    )
+    return response.choices[0].message.content
+
+
 def write_file(path: Path, content: str):
     path.parent.mkdir(parents=True, exist_ok=True)
     path.write_text(content, encoding="utf-8")
@@ -105,7 +121,7 @@ def ingest(source_path: str):
     wiki_context = build_wiki_context()
     schema = read_file(SCHEMA_FILE)
 
-    client = anthropic.Anthropic()
+    schema = read_file(SCHEMA_FILE)
 
     prompt = f"""You are maintaining an LLM Wiki. Process this source document and integrate its knowledge into the wiki.
 
@@ -140,14 +156,8 @@ Return ONLY a valid JSON object with these fields (no markdown fences, no prose
 }}
 """
 
-    print("  calling Claude API...")
+    print(f"  calling API (model: ...)")
-    response = client.messages.create(
+    raw = call_llm(prompt, max_tokens=8192)
-        model="claude-sonnet-4-6",
-        max_tokens=8192,
-        messages=[{"role": "user", "content": prompt}],
-    )
-
-    raw = response.content[0].text
     try:
         data = parse_json_from_response(raw)
     except (ValueError, json.JSONDecodeError) as e:

tools/lint.py

@@ -21,7 +21,7 @@ from pathlib import Path
 from collections import defaultdict
 from datetime import date
 
-import anthropic
+import os
 
 REPO_ROOT = Path(__file__).parent.parent
 WIKI_DIR = REPO_ROOT / "wiki"
@@ -33,6 +33,22 @@ def read_file(path: Path) -> str:
     return path.read_text(encoding="utf-8") if path.exists() else ""
 
 
+def call_llm(prompt: str, model_env: str, default_model: str, max_tokens: int = 4096) -> str:
+    try:
+        from litellm import completion
+    except ImportError:
+        print("Error: litellm not installed. Run: pip install litellm")
+        sys.exit(1)
+        
+    model = os.getenv(model_env, default_model)
+    response = completion(
+        model=model,
+        messages=[{"role": "user", "content": prompt}],
+        max_tokens=max_tokens
+    )
+    return response.choices[0].message.content
+
+
 def all_wiki_pages() -> list[Path]:
     return [p for p in WIKI_DIR.rglob("*.md")
             if p.name not in ("index.md", "log.md", "lint-report.md")]
@@ -112,14 +128,8 @@ def run_lint():
         rel = p.relative_to(REPO_ROOT)
         pages_context += f"\n\n### {rel}\n{read_file(p)[:1500]}"  # truncate long pages
 
-    client = anthropic.Anthropic()
+    print("  running semantic lint via API...")
-    print("  running semantic lint via Claude API...")
+    prompt = f"""You are linting an LLM Wiki. Review the pages below and identify:
-    response = client.messages.create(
-        model="claude-sonnet-4-6",
-        max_tokens=3000,
-        messages=[{
-            "role": "user",
-            "content": f"""You are linting an LLM Wiki. Review the pages below and identify:
 1. Contradictions between pages (claims that conflict)
 2. Stale content (summaries that newer sources have superseded)
 3. Data gaps (important questions the wiki can't answer — suggest specific sources to find)
@@ -136,10 +146,7 @@ Return a markdown lint report with these sections:
 
 Be specific — name the exact pages and claims involved.
 """
-        }]
+    semantic_report = call_llm(prompt, "LLM_MODEL", "claude-3-5-sonnet-latest", max_tokens=3000)
-    )
-
-    semantic_report = response.content[0].text
 
     # Compose full report
     report_lines = [

tools/query.py

@@ -19,7 +19,7 @@ import argparse
 from pathlib import Path
 from datetime import date
 
-import anthropic
+import os
 
 REPO_ROOT = Path(__file__).parent.parent
 WIKI_DIR = REPO_ROOT / "wiki"
@@ -38,6 +38,22 @@ def write_file(path: Path, content: str):
     print(f"  saved: {path.relative_to(REPO_ROOT)}")
 
 
+def call_llm(prompt: str, model_env: str, default_model: str, max_tokens: int = 4096) -> str:
+    try:
+        from litellm import completion
+    except ImportError:
+        print("Error: litellm not installed. Run: pip install litellm")
+        sys.exit(1)
+        
+    model = os.getenv(model_env, default_model)
+    response = completion(
+        model=model,
+        messages=[{"role": "user", "content": prompt}],
+        max_tokens=max_tokens
+    )
+    return response.choices[0].message.content
+
+
 def find_relevant_pages(question: str, index_content: str) -> list[Path]:
     """Extract linked pages from index that seem relevant to the question."""
     # Pull all [[links]] and markdown links from index
@@ -64,7 +80,6 @@ def append_log(entry: str):
 
 def query(question: str, save_path: str | None = None):
     today = date.today().isoformat()
-    client = anthropic.Anthropic()
 
     # Step 1: Read index
     index_content = read_file(INDEX_FILE)
@@ -77,16 +92,10 @@ def query(question: str, save_path: str | None = None):
 
     # If no keyword match, ask Claude to identify relevant pages from the index
     if not relevant_pages or len(relevant_pages) <= 1:
-        print("  selecting relevant pages via Claude...")
+        print("  selecting relevant pages via API...")
-        selection_response = client.messages.create(
+        prompt = f"Given this wiki index:\n\n{index_content}\n\nWhich pages are most relevant to answering: \"{question}\"\n\nReturn ONLY a JSON array of relative file paths (as listed in the index), e.g. [\"sources/foo.md\", \"concepts/Bar.md\"]. Maximum 10 pages."
-            model="claude-haiku-4-5-20251001",
+        raw = call_llm(prompt, "LLM_MODEL_FAST", "claude-3-5-haiku-latest", max_tokens=512)
-            max_tokens=512,
+        raw = raw.strip()
-            messages=[{
-                "role": "user",
-                "content": f"Given this wiki index:\n\n{index_content}\n\nWhich pages are most relevant to answering: \"{question}\"\n\nReturn ONLY a JSON array of relative file paths (as listed in the index), e.g. [\"sources/foo.md\", \"concepts/Bar.md\"]. Maximum 10 pages."
-            }]
-        )
-        raw = selection_response.content[0].text.strip()
         raw = re.sub(r"^```(?:json)?\s*", "", raw)
         raw = re.sub(r"\s*```$", "", raw)
         try:
@@ -108,12 +117,7 @@ def query(question: str, save_path: str | None = None):
 
     # Step 4: Synthesize answer
     print(f"  synthesizing answer from {len(relevant_pages)} pages...")
-    response = client.messages.create(
+    prompt = f"""You are querying an LLM Wiki to answer a question. Use the wiki pages below to synthesize a thorough answer. Cite sources using [[PageName]] wikilink syntax.
-        model="claude-sonnet-4-6",
-        max_tokens=4096,
-        messages=[{
-            "role": "user",
-            "content": f"""You are querying an LLM Wiki to answer a question. Use the wiki pages below to synthesize a thorough answer. Cite sources using [[PageName]] wikilink syntax.
 
 Schema:
 {schema}
@@ -125,10 +129,7 @@ Question: {question}
 
 Write a well-structured markdown answer with headers, bullets, and [[wikilink]] citations. At the end, add a ## Sources section listing the pages you drew from.
 """
-        }]
+    answer = call_llm(prompt, "LLM_MODEL", "claude-3-5-sonnet-latest", max_tokens=4096)
-    )
-
-    answer = response.content[0].text
     print("\n" + "=" * 60)
     print(answer)
     print("=" * 60)