fix: use json_repair for robust LLM response parsing
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@ -16,7 +16,7 @@
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⚡️ Delivers core agent functionality in just **~4,000** lines of code — **99% smaller** than Clawdbot's 430k+ lines.
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📏 Real-time line count: **3,656 lines** (run `bash core_agent_lines.sh` to verify anytime)
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📏 Real-time line count: **3,663 lines** (run `bash core_agent_lines.sh` to verify anytime)
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## 📢 News
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@ -3,6 +3,7 @@
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import asyncio
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from contextlib import AsyncExitStack
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import json
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import json_repair
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from pathlib import Path
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from typing import Any
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@ -420,9 +421,15 @@ Respond with ONLY valid JSON, no markdown fences."""
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model=self.model,
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)
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text = (response.content or "").strip()
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if not text:
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logger.warning("Memory consolidation: LLM returned empty response, skipping")
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return
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if text.startswith("```"):
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text = text.split("\n", 1)[-1].rsplit("```", 1)[0].strip()
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result = json.loads(text)
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result = json_repair.loads(text)
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if not isinstance(result, dict):
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logger.warning(f"Memory consolidation: unexpected response type, skipping. Response: {text[:200]}")
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return
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if entry := result.get("history_entry"):
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memory.append_history(entry)
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@ -1,6 +1,7 @@
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"""LiteLLM provider implementation for multi-provider support."""
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import json
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import json_repair
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import os
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from typing import Any
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@ -173,10 +174,7 @@ class LiteLLMProvider(LLMProvider):
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# Parse arguments from JSON string if needed
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args = tc.function.arguments
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if isinstance(args, str):
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try:
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args = json.loads(args)
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except json.JSONDecodeError:
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args = {"raw": args}
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args = json_repair.loads(args)
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tool_calls.append(ToolCallRequest(
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id=tc.id,
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@ -39,6 +39,7 @@ dependencies = [
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"python-socks[asyncio]>=2.4.0",
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"prompt-toolkit>=3.0.0",
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"mcp>=1.0.0",
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"json-repair>=0.30.0",
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]
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[project.optional-dependencies]
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