diff --git a/.github/scripts/model-sweep/conftest.py b/.github/scripts/model-sweep/conftest.py
index 30e0a00b..db66a444 100644
--- a/.github/scripts/model-sweep/conftest.py
+++ b/.github/scripts/model-sweep/conftest.py
@@ -1,24 +1,19 @@
 import logging
 import os
-import requests
 import socket
 import threading
 import time
-
 from datetime import datetime, timezone
 from typing import Generator
 
 import pytest
+import requests
 from anthropic.types.beta.messages import BetaMessageBatch, BetaMessageBatchRequestCounts
-
 from dotenv import load_dotenv
-
-from letta_client import Letta, AsyncLetta
+from letta_client import AsyncLetta, Letta
 
 from letta.schemas.agent import AgentState
-
 from letta.schemas.llm_config import LLMConfig
-
 from letta.services.organization_manager import OrganizationManager
 from letta.services.user_manager import UserManager
 from letta.settings import tool_settings
@@ -160,11 +155,13 @@ def dummy_beta_message_batch() -> BetaMessageBatch:
         type="message_batch",
     )
 
+
 # --- Model Sweep ---
 # Global flag to track server state
 _server_started = False
 _server_url = None
 
+
 def _start_server_once() -> str:
     """Start server exactly once, return URL"""
     global _server_started, _server_url
@@ -176,16 +173,18 @@ def _start_server_once() -> str:
 
     # Check if already running
     with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
-        if s.connect_ex(('localhost', 8283)) == 0:
+        if s.connect_ex(("localhost", 8283)) == 0:
             _server_started = True
             _server_url = url
             return url
 
     # Start server (your existing logic)
     if not os.getenv("LETTA_SERVER_URL"):
+
         def _run_server():
             load_dotenv()
             from letta.server.rest_api.app import start_server
+
             start_server(debug=True)
 
         thread = threading.Thread(target=_run_server, daemon=True)
@@ -209,15 +208,18 @@ def _start_server_once() -> str:
     _server_url = url
     return url
 
+
 # ------------------------------
 # Fixtures
 # ------------------------------
 
+
 @pytest.fixture(scope="module")
 def server_url() -> str:
     """Return URL of already-started server"""
     return _start_server_once()
 
+
 @pytest.fixture(scope="module")
 def client(server_url: str) -> Letta:
     """
@@ -274,14 +276,11 @@ def get_available_llm_configs() -> [LLMConfig]:
     temp_client = Letta(base_url=server_url)
     return temp_client.models.list()
 
+
 # dynamically insert llm_config paramter at collection time
 def pytest_generate_tests(metafunc):
     """Dynamically parametrize tests that need llm_config."""
     if "llm_config" in metafunc.fixturenames:
         configs = get_available_llm_configs()
         if configs:
-            metafunc.parametrize(
-                "llm_config",
-                configs,
-                ids=[c.model for c in configs]
-            )
+            metafunc.parametrize("llm_config", configs, ids=[c.model for c in configs])
diff --git a/.github/scripts/model-sweep/generate_model_sweep_markdown.py b/.github/scripts/model-sweep/generate_model_sweep_markdown.py
index 2b63ec13..38552a8c 100644
--- a/.github/scripts/model-sweep/generate_model_sweep_markdown.py
+++ b/.github/scripts/model-sweep/generate_model_sweep_markdown.py
@@ -1,19 +1,20 @@
 #!/usr/bin/env python3
 import json
-import sys
 import os
+import sys
 from collections import defaultdict
 from datetime import datetime
 
+
 def load_feature_mappings(config_file=None):
     """Load feature mappings from config file."""
     if config_file is None:
         # Default to feature_mappings.json in the same directory as this script
         script_dir = os.path.dirname(os.path.abspath(__file__))
-        config_file = os.path.join(script_dir, 'feature_mappings.json')
+        config_file = os.path.join(script_dir, "feature_mappings.json")
 
     try:
-        with open(config_file, 'r') as f:
+        with open(config_file, "r") as f:
             return json.load(f)
     except FileNotFoundError:
         print(f"Error: Could not find feature mappings config file '{config_file}'")
@@ -22,14 +23,15 @@ def load_feature_mappings(config_file=None):
         print(f"Error: Invalid JSON in feature mappings config file '{config_file}'")
         sys.exit(1)
 
+
 def get_support_status(passed_tests, feature_tests):
     """Determine support status for a feature category."""
     if not feature_tests:
         return "❓"  # Unknown - no tests for this feature
 
     # Filter out error tests when checking for support
-    non_error_tests = [test for test in feature_tests if not test.endswith('_error')]
-    error_tests = [test for test in feature_tests if test.endswith('_error')]
+    non_error_tests = [test for test in feature_tests if not test.endswith("_error")]
+    error_tests = [test for test in feature_tests if test.endswith("_error")]
 
     # Check which non-error tests passed
     passed_non_error_tests = [test for test in non_error_tests if test in passed_tests]
@@ -46,6 +48,7 @@ def get_support_status(passed_tests, feature_tests):
     else:
         return "⚠️"  # Partial support
 
+
 def categorize_tests(all_test_names, feature_mapping):
     """Categorize test names into feature buckets."""
     categorized = {feature: [] for feature in feature_mapping.keys()}
@@ -58,6 +61,7 @@ def categorize_tests(all_test_names, feature_mapping):
 
     return categorized
 
+
 def calculate_support_score(feature_support, feature_order):
     """Calculate a numeric support score for ranking models.
 
@@ -83,86 +87,90 @@ def calculate_support_score(feature_support, feature_order):
         # Unknown (❓) gets 0 points
     return score
 
+
 def calculate_provider_support_score(models_data, feature_order):
     """Calculate a provider-level support score based on all models' support scores."""
     if not models_data:
         return 0
 
     # Calculate the average support score across all models in the provider
-    total_score = sum(model['support_score'] for model in models_data)
+    total_score = sum(model["support_score"] for model in models_data)
     return total_score / len(models_data)
 
+
 def get_test_function_line_numbers(test_file_path):
     """Extract line numbers for test functions from the test file."""
     test_line_numbers = {}
-    
+
     try:
-        with open(test_file_path, 'r') as f:
+        with open(test_file_path, "r") as f:
             lines = f.readlines()
-        
+
         for i, line in enumerate(lines, 1):
-            if 'def test_' in line and line.strip().startswith('def test_'):
+            if "def test_" in line and line.strip().startswith("def test_"):
                 # Extract function name
-                func_name = line.strip().split('def ')[1].split('(')[0]
+                func_name = line.strip().split("def ")[1].split("(")[0]
                 test_line_numbers[func_name] = i
     except FileNotFoundError:
         print(f"Warning: Could not find test file at {test_file_path}")
-    
+
     return test_line_numbers
 
+
 def get_github_repo_info():
     """Get GitHub repository information from git remote."""
     try:
         # Try to get the GitHub repo URL from git remote
         import subprocess
-        result = subprocess.run(['git', 'remote', 'get-url', 'origin'], 
-                              capture_output=True, text=True, cwd=os.path.dirname(__file__))
+
+        result = subprocess.run(["git", "remote", "get-url", "origin"], capture_output=True, text=True, cwd=os.path.dirname(__file__))
         if result.returncode == 0:
             remote_url = result.stdout.strip()
             # Parse GitHub URL
-            if 'github.com' in remote_url:
-                if remote_url.startswith('https://'):
+            if "github.com" in remote_url:
+                if remote_url.startswith("https://"):
                     # https://github.com/user/repo.git -> user/repo
-                    repo_path = remote_url.replace('https://github.com/', '').replace('.git', '')
-                elif remote_url.startswith('git@'):
+                    repo_path = remote_url.replace("https://github.com/", "").replace(".git", "")
+                elif remote_url.startswith("git@"):
                     # git@github.com:user/repo.git -> user/repo
-                    repo_path = remote_url.split(':')[1].replace('.git', '')
+                    repo_path = remote_url.split(":")[1].replace(".git", "")
                 else:
                     return None
                 return repo_path
     except:
         pass
-    
+
     # Default fallback
     return "letta-ai/letta"
 
+
 def generate_test_details(model_info, feature_mapping):
     """Generate detailed test results for a model."""
     details = []
-    
+
     # Get test function line numbers
     script_dir = os.path.dirname(os.path.abspath(__file__))
-    test_file_path = os.path.join(script_dir, 'model_sweep.py')
+    test_file_path = os.path.join(script_dir, "model_sweep.py")
     test_line_numbers = get_test_function_line_numbers(test_file_path)
-    
+
     # Use the main branch GitHub URL
     base_github_url = "https://github.com/letta-ai/letta/blob/main/.github/scripts/model-sweep/model_sweep.py"
-    
-    for feature, tests in model_info['categorized_tests'].items():
+
+    for feature, tests in model_info["categorized_tests"].items():
         if not tests:
             continue
-            
+
         details.append(f"### {feature}")
         details.append("")
-        
+
         for test in sorted(tests):
-            if test in model_info['passed_tests']:
+            if test in model_info["passed_tests"]:
                 status = "✅"
-            elif test in model_info['failed_tests']:
+            elif test in model_info["failed_tests"]:
                 status = "❌"
             else:
                 status = "❓"
-            
+
             # Create GitHub link if we have line number info
             if test in test_line_numbers:
                 line_num = test_line_numbers[test]
@@ -171,16 +179,13 @@ def generate_test_details(model_info, feature_mapping):
             else:
                 details.append(f"- {status} `{test}`")
         details.append("")
-    
+
     return details
 
+
 def calculate_column_widths(all_provider_data, feature_mapping):
     """Calculate the maximum width needed for each column across all providers."""
-    widths = {
-        'model': len('Model'),
-        'context_window': len('Context Window'),
-        'last_scanned': len('Last Scanned')
-    }
+    widths = {"model": len("Model"), "context_window": len("Context Window"), "last_scanned": len("Last Scanned")}
 
     # Feature column widths
     for feature in feature_mapping.keys():
@@ -191,19 +196,20 @@ def calculate_column_widths(all_provider_data, feature_mapping):
         for model_info in provider_data:
             # Model name width (including backticks)
             model_width = len(f"`{model_info['name']}`")
-            widths['model'] = max(widths['model'], model_width)
+            widths["model"] = max(widths["model"], model_width)
 
             # Context window width (with commas)
             context_width = len(f"{model_info['context_window']:,}")
-            widths['context_window'] = max(widths['context_window'], context_width)
+            widths["context_window"] = max(widths["context_window"], context_width)
 
             # Last scanned width
-            widths['last_scanned'] = max(widths['last_scanned'], len(str(model_info['last_scanned'])))
+            widths["last_scanned"] = max(widths["last_scanned"], len(str(model_info["last_scanned"])))
 
             # Feature support symbols are always 2 chars, so no need to check
 
     return widths
 
+
 def process_model_sweep_report(input_file, output_file, config_file=None, debug=False):
     """Convert model sweep JSON data to MDX report."""
 
@@ -217,18 +223,18 @@ def process_model_sweep_report(input_file, output_file, config_file=None, debug=
     #     print()
 
     # Read the JSON data
-    with open(input_file, 'r') as f:
+    with open(input_file, "r") as f:
         data = json.load(f)
 
-    tests = data.get('tests', [])
+    tests = data.get("tests", [])
 
     # if debug:
     #     print("DEBUG: Tests loaded:")
     #     print([test['outcome'] for test in tests if 'haiku' in test['nodeid']])
 
     # Calculate summary statistics
-    providers = set(test['metadata']['llm_config']['provider_name'] for test in tests)
-    models = set(test['metadata']['llm_config']['model'] for test in tests)
+    providers = set(test["metadata"]["llm_config"]["provider_name"] for test in tests)
+    models = set(test["metadata"]["llm_config"]["model"] for test in tests)
     total_tests = len(tests)
 
     # Start building the MDX
@@ -246,13 +252,13 @@ def process_model_sweep_report(input_file, output_file, config_file=None, debug=
         "",
         f"Ran {total_tests} tests against {len(models)} models across {len(providers)} providers on {datetime.now().strftime('%B %dth, %Y')}",
         "",
-        ""
+        "",
     ]
 
     # Group tests by provider
     provider_groups = defaultdict(list)
     for test in tests:
-        provider_name = test['metadata']['llm_config']['provider_name']
+        provider_name = test["metadata"]["llm_config"]["provider_name"]
         provider_groups[provider_name].append(test)
 
     # Process all providers first to collect model data
@@ -265,7 +271,7 @@ def process_model_sweep_report(input_file, output_file, config_file=None, debug=
         # Group tests by model within this provider
         model_groups = defaultdict(list)
         for test in provider_tests:
-            model_name = test['metadata']['llm_config']['model']
+            model_name = test["metadata"]["llm_config"]["model"]
             model_groups[model_name].append(test)
 
         # Process all models to calculate support scores for ranking
@@ -283,15 +289,15 @@ def process_model_sweep_report(input_file, output_file, config_file=None, debug=
 
             for test in model_tests:
                 # Extract test name from nodeid (split on :: and [)
-                test_name = test['nodeid'].split('::')[1].split('[')[0]
+                test_name = test["nodeid"].split("::")[1].split("[")[0]
                 all_test_names.add(test_name)
 
                 # if debug:
                 #     print(f"  Test name: {test_name}")
                 #     print(f"  Outcome: {test}")
-                if test['outcome'] == 'passed':
+                if test["outcome"] == "passed":
                     passed_tests.add(test_name)
-                elif test['outcome'] == 'failed':
+                elif test["outcome"] == "failed":
                     failed_tests.add(test_name)
 
             # if debug:
@@ -319,16 +325,16 @@ def process_model_sweep_report(input_file, output_file, config_file=None, debug=
             #     print()
 
             # Get context window and last scanned time
-            context_window = model_tests[0]['metadata']['llm_config']['context_window']
+            context_window = model_tests[0]["metadata"]["llm_config"]["context_window"]
 
             # Try to get time_last_scanned from metadata, fallback to current time
             try:
-                last_scanned = model_tests[0]['metadata'].get('time_last_scanned',
-                                                           model_tests[0]['metadata'].get('timestamp',
-                                                           datetime.now().isoformat()))
+                last_scanned = model_tests[0]["metadata"].get(
+                    "time_last_scanned", model_tests[0]["metadata"].get("timestamp", datetime.now().isoformat())
+                )
                 # Format timestamp if it's a full ISO string
-                if 'T' in str(last_scanned):
-                    last_scanned = str(last_scanned).split('T')[0]  # Just the date part
+                if "T" in str(last_scanned):
+                    last_scanned = str(last_scanned).split("T")[0]  # Just the date part
             except:
                 last_scanned = "Unknown"
 
@@ -337,19 +343,21 @@ def process_model_sweep_report(input_file, output_file, config_file=None, debug=
             support_score = calculate_support_score(feature_support, feature_order)
 
             # Store model data for sorting
-            model_data.append({
-                'name': model_name,
-                'feature_support': feature_support,
-                'context_window': context_window,
-                'last_scanned': last_scanned,
-                'support_score': support_score,
-                'failed_tests': failed_tests,
-                'passed_tests': passed_tests,
-                'categorized_tests': categorized_tests
-            })
+            model_data.append(
+                {
+                    "name": model_name,
+                    "feature_support": feature_support,
+                    "context_window": context_window,
+                    "last_scanned": last_scanned,
+                    "support_score": support_score,
+                    "failed_tests": failed_tests,
+                    "passed_tests": passed_tests,
+                    "categorized_tests": categorized_tests,
+                }
+            )
 
         # Sort models by support score (descending) then by name (ascending)
-        model_data.sort(key=lambda x: (-x['support_score'], x['name']))
+        model_data.sort(key=lambda x: (-x["support_score"], x["name"]))
 
         # Store provider data
         all_provider_data[provider_name] = model_data
@@ -357,13 +365,10 @@ def process_model_sweep_report(input_file, output_file, config_file=None, debug=
 
     # Calculate column widths for consistent formatting (add details column)
     column_widths = calculate_column_widths(all_provider_data, feature_mapping)
-    column_widths['details'] = len('Details')
+    column_widths["details"] = len("Details")
 
     # Sort providers by support score (descending) then by name (ascending)
-    sorted_providers = sorted(
-        provider_support_scores.keys(),
-        key=lambda x: (-provider_support_scores[x], x)
-    )
+    sorted_providers = sorted(provider_support_scores.keys(), key=lambda x: (-provider_support_scores[x], x))
 
     # Generate tables for all providers first
     for provider_name in sorted_providers:
@@ -377,47 +382,48 @@ def process_model_sweep_report(input_file, output_file, config_file=None, debug=
         header_parts = [f"{'Model':<{column_widths['model']}}"]
         for feature in feature_names:
             header_parts.append(f"{feature:^{column_widths[feature]}}")
-        header_parts.extend([
-            f"{'Context Window':^{column_widths['context_window']}}",
-            f"{'Last Scanned':^{column_widths['last_scanned']}}",
-            f"{'Details':^{column_widths['details']}}"
-        ])
+        header_parts.extend(
+            [
+                f"{'Context Window':^{column_widths['context_window']}}",
+                f"{'Last Scanned':^{column_widths['last_scanned']}}",
+                f"{'Details':^{column_widths['details']}}",
+            ]
+        )
         header_row = "| " + " | ".join(header_parts) + " |"
 
         # Build separator row with left-aligned first column, centered others
         separator_parts = [f"{'-' * column_widths['model']}"]
         for feature in feature_names:
             separator_parts.append(f":{'-' * (column_widths[feature] - 2)}:")
-        separator_parts.extend([
-            f":{'-' * (column_widths['context_window'] - 2)}:",
-            f":{'-' * (column_widths['last_scanned'] - 2)}:",
-            f":{'-' * (column_widths['details'] - 2)}:"
-        ])
+        separator_parts.extend(
+            [
+                f":{'-' * (column_widths['context_window'] - 2)}:",
+                f":{'-' * (column_widths['last_scanned'] - 2)}:",
+                f":{'-' * (column_widths['details'] - 2)}:",
+            ]
+        )
         separator_row = "|" + "|".join(separator_parts) + "|"
 
         # Add provider section without percentage
-        mdx_lines.extend([
-            f"## {provider_name}",
-            "",
-            header_row,
-            separator_row
-        ])
+        mdx_lines.extend([f"## {provider_name}", "", header_row, separator_row])
 
         # Generate table rows for sorted models with proper padding
         for model_info in model_data:
             # Create anchor for model details
-            model_anchor = model_info['name'].replace('/', '_').replace(':', '_').replace('-', '_').lower()
+            model_anchor = model_info["name"].replace("/", "_").replace(":", "_").replace("-", "_").lower()
             details_anchor = f"{provider_name.lower().replace(' ', '_')}_{model_anchor}_details"
-            
+
             # Build row with left-aligned first column, centered others
-            row_parts = [f"`{model_info['name']}`".ljust(column_widths['model'])]
+            row_parts = [f"`{model_info['name']}`".ljust(column_widths["model"])]
             for feature in feature_names:
                 row_parts.append(f"{model_info['feature_support'][feature]:^{column_widths[feature]}}")
-            row_parts.extend([
-                f"{model_info['context_window']:,}".center(column_widths['context_window']),
-                f"{model_info['last_scanned']}".center(column_widths['last_scanned']),
-                f"[View](#{details_anchor})".center(column_widths['details'])
-            ])
+            row_parts.extend(
+                [
+                    f"{model_info['context_window']:,}".center(column_widths["context_window"]),
+                    f"{model_info['last_scanned']}".center(column_widths["last_scanned"]),
+                    f"[View](#{details_anchor})".center(column_widths["details"]),
+                ]
+            )
             row = "| " + " | ".join(row_parts) + " |"
             mdx_lines.append(row)
 
@@ -426,31 +432,32 @@ def process_model_sweep_report(input_file, output_file, config_file=None, debug=
 
     # Add detailed test results section after all tables
     mdx_lines.extend(["---", "", "# Detailed Test Results", ""])
-    
+
     for provider_name in sorted_providers:
         model_data = all_provider_data[provider_name]
         mdx_lines.extend([f"## {provider_name}", ""])
-        
+
         for model_info in model_data:
-            model_anchor = model_info['name'].replace('/', '_').replace(':', '_').replace('-', '_').lower()
+            model_anchor = model_info["name"].replace("/", "_").replace(":", "_").replace("-", "_").lower()
             details_anchor = f"{provider_name.lower().replace(' ', '_')}_{model_anchor}_details"
-            mdx_lines.append(f"<a id=\"{details_anchor}\"></a>")
+            mdx_lines.append(f'<a id="{details_anchor}"></a>')
             mdx_lines.append(f"### {model_info['name']}")
             mdx_lines.append("")
-            
+
             # Add test details
             test_details = generate_test_details(model_info, feature_mapping)
             mdx_lines.extend(test_details)
-        
+
         # Add spacing between providers in details section
         mdx_lines.extend(["", ""])
 
     # Write the MDX file
-    with open(output_file, 'w') as f:
-        f.write('\n'.join(mdx_lines))
+    with open(output_file, "w") as f:
+        f.write("\n".join(mdx_lines))
 
     print(f"Model sweep report saved to {output_file}")
 
+
 def main():
     input_file = "model_sweep_report.json"
     output_file = "model_sweep_report.mdx"
@@ -483,5 +490,6 @@ def main():
         print(f"Error: {e}")
         sys.exit(1)
 
+
 if __name__ == "__main__":
     main()
diff --git a/.github/scripts/model-sweep/model_sweep.py b/.github/scripts/model-sweep/model_sweep.py
index a66efbaa..c92b9364 100644
--- a/.github/scripts/model-sweep/model_sweep.py
+++ b/.github/scripts/model-sweep/model_sweep.py
@@ -29,7 +29,6 @@ from letta_client.types import (
 from letta.schemas.agent import AgentState
 from letta.schemas.llm_config import LLMConfig
 
-
 # ------------------------------
 # Helper Functions and Constants
 # ------------------------------
@@ -109,6 +108,7 @@ requested = os.getenv("LLM_CONFIG_FILE")
 filenames = [requested] if requested else all_configs
 TESTED_LLM_CONFIGS: List[LLMConfig] = [get_llm_config(fn) for fn in filenames]
 
+
 def assert_greeting_with_assistant_message_response(
     messages: List[Any],
     streaming: bool = False,
diff --git a/alembic/versions/47d2277e530d_add_total_chunks_and_chunks_embedded_to_.py b/alembic/versions/47d2277e530d_add_total_chunks_and_chunks_embedded_to_.py
new file mode 100644
index 00000000..194d2acf
--- /dev/null
+++ b/alembic/versions/47d2277e530d_add_total_chunks_and_chunks_embedded_to_.py
@@ -0,0 +1,33 @@
+"""Add total_chunks and chunks_embedded to files
+
+Revision ID: 47d2277e530d
+Revises: 56254216524f
+Create Date: 2025-07-03 14:32:08.539280
+
+"""
+
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+
+from alembic import op
+
+# revision identifiers, used by Alembic.
+revision: str = "47d2277e530d"
+down_revision: Union[str, None] = "56254216524f"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.add_column("files", sa.Column("total_chunks", sa.Integer(), nullable=True))
+    op.add_column("files", sa.Column("chunks_embedded", sa.Integer(), nullable=True))
+    # ### end Alembic commands ###
+
+
+def downgrade() -> None:
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.drop_column("files", "chunks_embedded")
+    op.drop_column("files", "total_chunks")
+    # ### end Alembic commands ###
diff --git a/letta/__init__.py b/letta/__init__.py
index 5446b2d3..0ed266b2 100644
--- a/letta/__init__.py
+++ b/letta/__init__.py
@@ -5,7 +5,7 @@ try:
     __version__ = version("letta")
 except PackageNotFoundError:
     # Fallback for development installations
-    __version__ = "0.8.9"
+    __version__ = "0.8.10"
 
 
 if os.environ.get("LETTA_VERSION"):
diff --git a/letta/agents/letta_agent.py b/letta/agents/letta_agent.py
index 65478b31..9a6aaadb 100644
--- a/letta/agents/letta_agent.py
+++ b/letta/agents/letta_agent.py
@@ -82,11 +82,16 @@ class LettaAgent(BaseAgent):
         step_manager: StepManager = NoopStepManager(),
         telemetry_manager: TelemetryManager = NoopTelemetryManager(),
         current_run_id: str | None = None,
+        ## summarizer settings
+        summarizer_mode: SummarizationMode = summarizer_settings.mode,
+        # for static_buffer mode
         summary_block_label: str = DEFAULT_SUMMARY_BLOCK_LABEL,
         message_buffer_limit: int = summarizer_settings.message_buffer_limit,
         message_buffer_min: int = summarizer_settings.message_buffer_min,
         enable_summarization: bool = summarizer_settings.enable_summarization,
         max_summarization_retries: int = summarizer_settings.max_summarization_retries,
+        # for partial_evict mode
+        partial_evict_summarizer_percentage: float = summarizer_settings.partial_evict_summarizer_percentage,
     ):
         super().__init__(agent_id=agent_id, openai_client=None, message_manager=message_manager, agent_manager=agent_manager, actor=actor)
 
@@ -124,11 +129,13 @@ class LettaAgent(BaseAgent):
             )
 
         self.summarizer = Summarizer(
-            mode=SummarizationMode(summarizer_settings.mode),
+            mode=summarizer_mode,
+            # TODO consolidate to not use this, or push it into the Summarizer() class
             summarizer_agent=self.summarization_agent,
             # TODO: Make this configurable
             message_buffer_limit=message_buffer_limit,
             message_buffer_min=message_buffer_min,
+            partial_evict_summarizer_percentage=partial_evict_summarizer_percentage,
         )
 
     async def _check_run_cancellation(self) -> bool:
@@ -872,25 +879,35 @@ class LettaAgent(BaseAgent):
             self.logger.warning(
                 f"Total tokens {total_tokens} exceeds configured max tokens {llm_config.context_window}, forcefully clearing message history."
             )
-            new_in_context_messages, updated = self.summarizer.summarize(
-                in_context_messages=in_context_messages, new_letta_messages=new_letta_messages, force=True, clear=True
+            new_in_context_messages, updated = await self.summarizer.summarize(
+                in_context_messages=in_context_messages,
+                new_letta_messages=new_letta_messages,
+                force=True,
+                clear=True,
             )
         else:
-            new_in_context_messages, updated = self.summarizer.summarize(
-                in_context_messages=in_context_messages, new_letta_messages=new_letta_messages
+            self.logger.info(
+                f"Total tokens {total_tokens} does not exceed configured max tokens {llm_config.context_window}, passing summarizing w/o force."
+            )
+            new_in_context_messages, updated = await self.summarizer.summarize(
+                in_context_messages=in_context_messages,
+                new_letta_messages=new_letta_messages,
             )
         await self.agent_manager.set_in_context_messages_async(
-            agent_id=self.agent_id, message_ids=[m.id for m in new_in_context_messages], actor=self.actor
+            agent_id=self.agent_id,
+            message_ids=[m.id for m in new_in_context_messages],
+            actor=self.actor,
         )
 
         return new_in_context_messages
 
     @trace_method
     async def summarize_conversation_history(self) -> AgentState:
+        """Called when the developer explicitly triggers compaction via the API"""
         agent_state = await self.agent_manager.get_agent_by_id_async(agent_id=self.agent_id, actor=self.actor)
         message_ids = agent_state.message_ids
         in_context_messages = await self.message_manager.get_messages_by_ids_async(message_ids=message_ids, actor=self.actor)
-        new_in_context_messages, updated = self.summarizer.summarize(
+        new_in_context_messages, updated = await self.summarizer.summarize(
             in_context_messages=in_context_messages, new_letta_messages=[], force=True
         )
         return await self.agent_manager.set_in_context_messages_async(
diff --git a/letta/agents/voice_agent.py b/letta/agents/voice_agent.py
index 63ee8da2..0c77626e 100644
--- a/letta/agents/voice_agent.py
+++ b/letta/agents/voice_agent.py
@@ -295,7 +295,7 @@ class VoiceAgent(BaseAgent):
         new_letta_messages = await self.message_manager.create_many_messages_async(letta_message_db_queue, actor=self.actor)
 
         # TODO: Make this more general and configurable, less brittle
-        new_in_context_messages, updated = summarizer.summarize(
+        new_in_context_messages, updated = await summarizer.summarize(
             in_context_messages=in_context_messages, new_letta_messages=new_letta_messages
         )
 
diff --git a/letta/agents/voice_sleeptime_agent.py b/letta/agents/voice_sleeptime_agent.py
index 1d997be2..e9d013f9 100644
--- a/letta/agents/voice_sleeptime_agent.py
+++ b/letta/agents/voice_sleeptime_agent.py
@@ -90,7 +90,7 @@ class VoiceSleeptimeAgent(LettaAgent):
         current_in_context_messages, new_in_context_messages, stop_reason, usage = await super()._step(
             agent_state=agent_state, input_messages=input_messages, max_steps=max_steps
         )
-        new_in_context_messages, updated = self.summarizer.summarize(
+        new_in_context_messages, updated = await self.summarizer.summarize(
             in_context_messages=current_in_context_messages, new_letta_messages=new_in_context_messages
         )
         self.agent_manager.set_in_context_messages(
diff --git a/letta/constants.py b/letta/constants.py
index c83eefe4..bd8d2c49 100644
--- a/letta/constants.py
+++ b/letta/constants.py
@@ -364,3 +364,10 @@ REDIS_RUN_ID_PREFIX = "agent:send_message:run_id"
 MAX_FILES_OPEN = 5
 
 GET_PROVIDERS_TIMEOUT_SECONDS = 10
+
+# Pinecone related fields
+PINECONE_EMBEDDING_MODEL: str = "llama-text-embed-v2"
+PINECONE_TEXT_FIELD_NAME = "chunk_text"
+PINECONE_METRIC = "cosine"
+PINECONE_CLOUD = "aws"
+PINECONE_REGION = "us-east-1"
diff --git a/letta/functions/function_sets/files.py b/letta/functions/function_sets/files.py
index d2e523aa..43b34bd9 100644
--- a/letta/functions/function_sets/files.py
+++ b/letta/functions/function_sets/files.py
@@ -65,7 +65,7 @@ async def grep_files(
     raise NotImplementedError("Tool not implemented. Please contact the Letta team.")
 
 
-async def semantic_search_files(agent_state: "AgentState", query: str) -> List["FileMetadata"]:
+async def semantic_search_files(agent_state: "AgentState", query: str, limit: int = 5) -> List["FileMetadata"]:
     """
     Get list of most relevant chunks from any file using vector/embedding search.
 
@@ -76,6 +76,7 @@ async def semantic_search_files(agent_state: "AgentState", query: str) -> List["
 
     Args:
         query (str): The search query.
+        limit: Maximum number of results to return (default: 5)
 
     Returns:
         List[FileMetadata]: List of matching files.
diff --git a/letta/functions/functions.py b/letta/functions/functions.py
index 39a25381..8681ac38 100644
--- a/letta/functions/functions.py
+++ b/letta/functions/functions.py
@@ -29,7 +29,6 @@ def derive_openai_json_schema(source_code: str, name: Optional[str] = None) -> d
             # "Field": Field,
         }
         env.update(globals())
-
         # print("About to execute source code...")
         exec(source_code, env)
         # print("Source code executed successfully")
diff --git a/letta/helpers/pinecone_utils.py b/letta/helpers/pinecone_utils.py
new file mode 100644
index 00000000..d7cd1d4a
--- /dev/null
+++ b/letta/helpers/pinecone_utils.py
@@ -0,0 +1,143 @@
+from typing import Any, Dict, List
+
+from pinecone import PineconeAsyncio
+
+from letta.constants import PINECONE_CLOUD, PINECONE_EMBEDDING_MODEL, PINECONE_METRIC, PINECONE_REGION, PINECONE_TEXT_FIELD_NAME
+from letta.log import get_logger
+from letta.schemas.user import User
+from letta.settings import settings
+
+logger = get_logger(__name__)
+
+
+def should_use_pinecone(verbose: bool = False):
+    if verbose:
+        logger.info(
+            "Pinecone check: enable_pinecone=%s, api_key=%s, agent_index=%s, source_index=%s",
+            settings.enable_pinecone,
+            bool(settings.pinecone_api_key),
+            bool(settings.pinecone_agent_index),
+            bool(settings.pinecone_source_index),
+        )
+
+    return settings.enable_pinecone and settings.pinecone_api_key and settings.pinecone_agent_index and settings.pinecone_source_index
+
+
+async def upsert_pinecone_indices():
+    from pinecone import IndexEmbed, PineconeAsyncio
+
+    for index_name in get_pinecone_indices():
+        async with PineconeAsyncio(api_key=settings.pinecone_api_key) as pc:
+            if not await pc.has_index(index_name):
+                await pc.create_index_for_model(
+                    name=index_name,
+                    cloud=PINECONE_CLOUD,
+                    region=PINECONE_REGION,
+                    embed=IndexEmbed(model=PINECONE_EMBEDDING_MODEL, field_map={"text": PINECONE_TEXT_FIELD_NAME}, metric=PINECONE_METRIC),
+                )
+
+
+def get_pinecone_indices() -> List[str]:
+    return [settings.pinecone_agent_index, settings.pinecone_source_index]
+
+
+async def upsert_file_records_to_pinecone_index(file_id: str, source_id: str, chunks: List[str], actor: User):
+    records = []
+    for i, chunk in enumerate(chunks):
+        record = {
+            "_id": f"{file_id}_{i}",
+            PINECONE_TEXT_FIELD_NAME: chunk,
+            "file_id": file_id,
+            "source_id": source_id,
+        }
+        records.append(record)
+
+    return await upsert_records_to_pinecone_index(records, actor)
+
+
+async def delete_file_records_from_pinecone_index(file_id: str, actor: User):
+    from pinecone.exceptions.exceptions import NotFoundException
+
+    namespace = actor.organization_id
+    try:
+        async with PineconeAsyncio(api_key=settings.pinecone_api_key) as pc:
+            description = await pc.describe_index(name=settings.pinecone_source_index)
+            async with pc.IndexAsyncio(host=description.index.host) as dense_index:
+                await dense_index.delete(
+                    filter={
+                        "file_id": {"$eq": file_id},
+                    },
+                    namespace=namespace,
+                )
+    except NotFoundException:
+        logger.warning(f"Pinecone namespace {namespace} not found for {file_id} and {actor.organization_id}")
+
+
+async def delete_source_records_from_pinecone_index(source_id: str, actor: User):
+    from pinecone.exceptions.exceptions import NotFoundException
+
+    namespace = actor.organization_id
+    try:
+        async with PineconeAsyncio(api_key=settings.pinecone_api_key) as pc:
+            description = await pc.describe_index(name=settings.pinecone_source_index)
+            async with pc.IndexAsyncio(host=description.index.host) as dense_index:
+                await dense_index.delete(filter={"source_id": {"$eq": source_id}}, namespace=namespace)
+    except NotFoundException:
+        logger.warning(f"Pinecone namespace {namespace} not found for {source_id} and {actor.organization_id}")
+
+
+async def upsert_records_to_pinecone_index(records: List[dict], actor: User):
+    async with PineconeAsyncio(api_key=settings.pinecone_api_key) as pc:
+        description = await pc.describe_index(name=settings.pinecone_source_index)
+        async with pc.IndexAsyncio(host=description.index.host) as dense_index:
+            await dense_index.upsert_records(actor.organization_id, records)
+
+
+async def search_pinecone_index(query: str, limit: int, filter: Dict[str, Any], actor: User) -> Dict[str, Any]:
+    async with PineconeAsyncio(api_key=settings.pinecone_api_key) as pc:
+        description = await pc.describe_index(name=settings.pinecone_source_index)
+        async with pc.IndexAsyncio(host=description.index.host) as dense_index:
+            namespace = actor.organization_id
+
+            try:
+                # Search the dense index with reranking
+                search_results = await dense_index.search(
+                    namespace=namespace,
+                    query={
+                        "top_k": limit,
+                        "inputs": {"text": query},
+                        "filter": filter,
+                    },
+                    rerank={"model": "bge-reranker-v2-m3", "top_n": limit, "rank_fields": [PINECONE_TEXT_FIELD_NAME]},
+                )
+                return search_results
+            except Exception as e:
+                logger.warning(f"Failed to search Pinecone namespace {actor.organization_id}: {str(e)}")
+                raise e
+
+
+async def list_pinecone_index_for_files(file_id: str, actor: User, limit: int = None, pagination_token: str = None) -> List[str]:
+    from pinecone.exceptions.exceptions import NotFoundException
+
+    namespace = actor.organization_id
+    try:
+        async with PineconeAsyncio(api_key=settings.pinecone_api_key) as pc:
+            description = await pc.describe_index(name=settings.pinecone_source_index)
+            async with pc.IndexAsyncio(host=description.index.host) as dense_index:
+
+                kwargs = {"namespace": namespace, "prefix": file_id}
+                if limit is not None:
+                    kwargs["limit"] = limit
+                if pagination_token is not None:
+                    kwargs["pagination_token"] = pagination_token
+
+                try:
+                    result = []
+                    async for ids in dense_index.list(**kwargs):
+                        result.extend(ids)
+                    return result
+                except Exception as e:
+                    logger.warning(f"Failed to list Pinecone namespace {actor.organization_id}: {str(e)}")
+                    raise e
+    except NotFoundException:
+        logger.warning(f"Pinecone namespace {namespace} not found for {file_id} and {actor.organization_id}")
diff --git a/letta/llm_api/openai_client.py b/letta/llm_api/openai_client.py
index ea17d0da..3b12095f 100644
--- a/letta/llm_api/openai_client.py
+++ b/letta/llm_api/openai_client.py
@@ -216,6 +216,10 @@ class OpenAIClient(LLMClientBase):
             # NOTE: the reasoners that don't support temperature require 1.0, not None
             temperature=llm_config.temperature if supports_temperature_param(model) else 1.0,
         )
+
+        if llm_config.frequency_penalty is not None:
+            data.frequency_penalty = llm_config.frequency_penalty
+
         if tools and supports_parallel_tool_calling(model):
             data.parallel_tool_calls = False
 
diff --git a/letta/orm/file.py b/letta/orm/file.py
index 35184f67..885731e5 100644
--- a/letta/orm/file.py
+++ b/letta/orm/file.py
@@ -60,6 +60,8 @@ class FileMetadata(SqlalchemyBase, OrganizationMixin, SourceMixin, AsyncAttrs):
     )
 
     error_message: Mapped[Optional[str]] = mapped_column(Text, nullable=True, doc="Any error message encountered during processing.")
+    total_chunks: Mapped[Optional[int]] = mapped_column(Integer, nullable=True, doc="Total number of chunks for the file.")
+    chunks_embedded: Mapped[Optional[int]] = mapped_column(Integer, nullable=True, doc="Number of chunks that have been embedded.")
 
     # relationships
     organization: Mapped["Organization"] = relationship("Organization", back_populates="files", lazy="selectin")
@@ -112,6 +114,8 @@ class FileMetadata(SqlalchemyBase, OrganizationMixin, SourceMixin, AsyncAttrs):
             file_last_modified_date=self.file_last_modified_date,
             processing_status=self.processing_status,
             error_message=self.error_message,
+            total_chunks=self.total_chunks,
+            chunks_embedded=self.chunks_embedded,
             created_at=self.created_at,
             updated_at=self.updated_at,
             is_deleted=self.is_deleted,
diff --git a/letta/prompts/gpt_summarize.py b/letta/prompts/gpt_summarize.py
index 945268de..c9e9ccdd 100644
--- a/letta/prompts/gpt_summarize.py
+++ b/letta/prompts/gpt_summarize.py
@@ -1,14 +1,12 @@
 WORD_LIMIT = 100
-SYSTEM = f"""
-Your job is to summarize a history of previous messages in a conversation between an AI persona and a human.
+SYSTEM = f"""Your job is to summarize a history of previous messages in a conversation between an AI persona and a human.
 The conversation you are given is a from a fixed context window and may not be complete.
 Messages sent by the AI are marked with the 'assistant' role.
-The AI 'assistant' can also make calls to functions, whose outputs can be seen in messages with the 'function' role.
+The AI 'assistant' can also make calls to tools, whose outputs can be seen in messages with the 'tool' role.
 Things the AI says in the message content are considered inner monologue and are not seen by the user.
 The only AI messages seen by the user are from when the AI uses 'send_message'.
 Messages the user sends are in the 'user' role.
 The 'user' role is also used for important system events, such as login events and heartbeat events (heartbeats run the AI's program without user action, allowing the AI to act without prompting from the user sending them a message).
-Summarize what happened in the conversation from the perspective of the AI (use the first person).
+Summarize what happened in the conversation from the perspective of the AI (use the first person from the perspective of the AI).
 Keep your summary less than {WORD_LIMIT} words, do NOT exceed this word limit.
-Only output the summary, do NOT include anything else in your output.
-"""
+Only output the summary, do NOT include anything else in your output."""
diff --git a/letta/schemas/file.py b/letta/schemas/file.py
index 64b6eed5..14e2a122 100644
--- a/letta/schemas/file.py
+++ b/letta/schemas/file.py
@@ -41,6 +41,8 @@ class FileMetadata(FileMetadataBase):
         description="The current processing status of the file (e.g. pending, parsing, embedding, completed, error).",
     )
     error_message: Optional[str] = Field(default=None, description="Optional error message if the file failed processing.")
+    total_chunks: Optional[int] = Field(default=None, description="Total number of chunks for the file.")
+    chunks_embedded: Optional[int] = Field(default=None, description="Number of chunks that have been embedded.")
 
     # orm metadata, optional fields
     created_at: Optional[datetime] = Field(default_factory=datetime.utcnow, description="The creation date of the file.")
@@ -52,6 +54,10 @@ class FileMetadata(FileMetadataBase):
         default=None, description="Optional full-text content of the file; only populated on demand due to its size."
     )
 
+    def is_processing_terminal(self) -> bool:
+        """Check if the file processing status is in a terminal state (completed or error)."""
+        return self.processing_status in (FileProcessingStatus.COMPLETED, FileProcessingStatus.ERROR)
+
 
 class FileAgentBase(LettaBase):
     """Base class for the FileMetadata-⇄-Agent association schemas"""
diff --git a/letta/schemas/letta_base.py b/letta/schemas/letta_base.py
index 5d2a3da3..abd87d5d 100644
--- a/letta/schemas/letta_base.py
+++ b/letta/schemas/letta_base.py
@@ -97,7 +97,7 @@ class LettaBase(BaseModel):
 
 class OrmMetadataBase(LettaBase):
     # metadata fields
-    created_by_id: Optional[str] = Field(None, description="The id of the user that made this object.")
-    last_updated_by_id: Optional[str] = Field(None, description="The id of the user that made this object.")
-    created_at: Optional[datetime] = Field(None, description="The timestamp when the object was created.")
-    updated_at: Optional[datetime] = Field(None, description="The timestamp when the object was last updated.")
+    created_by_id: Optional[str] = Field(default=None, description="The id of the user that made this object.")
+    last_updated_by_id: Optional[str] = Field(default=None, description="The id of the user that made this object.")
+    created_at: Optional[datetime] = Field(default=None, description="The timestamp when the object was created.")
+    updated_at: Optional[datetime] = Field(default=None, description="The timestamp when the object was last updated.")
diff --git a/letta/schemas/letta_message.py b/letta/schemas/letta_message.py
index fb773fa6..cfae6b38 100644
--- a/letta/schemas/letta_message.py
+++ b/letta/schemas/letta_message.py
@@ -72,7 +72,7 @@ class SystemMessage(LettaMessage):
         content (str): The message content sent by the system
     """
 
-    message_type: Literal[MessageType.system_message] = Field(MessageType.system_message, description="The type of the message.")
+    message_type: Literal[MessageType.system_message] = Field(default=MessageType.system_message, description="The type of the message.")
     content: str = Field(..., description="The message content sent by the system")
 
 
@@ -87,7 +87,7 @@ class UserMessage(LettaMessage):
         content (Union[str, List[LettaUserMessageContentUnion]]): The message content sent by the user (can be a string or an array of multi-modal content parts)
     """
 
-    message_type: Literal[MessageType.user_message] = Field(MessageType.user_message, description="The type of the message.")
+    message_type: Literal[MessageType.user_message] = Field(default=MessageType.user_message, description="The type of the message.")
     content: Union[str, List[LettaUserMessageContentUnion]] = Field(
         ...,
         description="The message content sent by the user (can be a string or an array of multi-modal content parts)",
@@ -109,7 +109,9 @@ class ReasoningMessage(LettaMessage):
         signature (Optional[str]): The model-generated signature of the reasoning step
     """
 
-    message_type: Literal[MessageType.reasoning_message] = Field(MessageType.reasoning_message, description="The type of the message.")
+    message_type: Literal[MessageType.reasoning_message] = Field(
+        default=MessageType.reasoning_message, description="The type of the message."
+    )
     source: Literal["reasoner_model", "non_reasoner_model"] = "non_reasoner_model"
     reasoning: str
     signature: Optional[str] = None
@@ -130,7 +132,7 @@ class HiddenReasoningMessage(LettaMessage):
     """
 
     message_type: Literal[MessageType.hidden_reasoning_message] = Field(
-        MessageType.hidden_reasoning_message, description="The type of the message."
+        default=MessageType.hidden_reasoning_message, description="The type of the message."
     )
     state: Literal["redacted", "omitted"]
     hidden_reasoning: Optional[str] = None
@@ -170,7 +172,9 @@ class ToolCallMessage(LettaMessage):
         tool_call (Union[ToolCall, ToolCallDelta]): The tool call
     """
 
-    message_type: Literal[MessageType.tool_call_message] = Field(MessageType.tool_call_message, description="The type of the message.")
+    message_type: Literal[MessageType.tool_call_message] = Field(
+        default=MessageType.tool_call_message, description="The type of the message."
+    )
     tool_call: Union[ToolCall, ToolCallDelta]
 
     def model_dump(self, *args, **kwargs):
@@ -222,7 +226,9 @@ class ToolReturnMessage(LettaMessage):
         stderr (Optional[List(str)]): Captured stderr from the tool invocation
     """
 
-    message_type: Literal[MessageType.tool_return_message] = Field(MessageType.tool_return_message, description="The type of the message.")
+    message_type: Literal[MessageType.tool_return_message] = Field(
+        default=MessageType.tool_return_message, description="The type of the message."
+    )
     tool_return: str
     status: Literal["success", "error"]
     tool_call_id: str
@@ -241,7 +247,9 @@ class AssistantMessage(LettaMessage):
         content (Union[str, List[LettaAssistantMessageContentUnion]]): The message content sent by the agent (can be a string or an array of content parts)
     """
 
-    message_type: Literal[MessageType.assistant_message] = Field(MessageType.assistant_message, description="The type of the message.")
+    message_type: Literal[MessageType.assistant_message] = Field(
+        default=MessageType.assistant_message, description="The type of the message."
+    )
     content: Union[str, List[LettaAssistantMessageContentUnion]] = Field(
         ...,
         description="The message content sent by the agent (can be a string or an array of content parts)",
diff --git a/letta/schemas/letta_message_content.py b/letta/schemas/letta_message_content.py
index a9ca2144..dd03c728 100644
--- a/letta/schemas/letta_message_content.py
+++ b/letta/schemas/letta_message_content.py
@@ -24,7 +24,7 @@ class MessageContent(BaseModel):
 
 
 class TextContent(MessageContent):
-    type: Literal[MessageContentType.text] = Field(MessageContentType.text, description="The type of the message.")
+    type: Literal[MessageContentType.text] = Field(default=MessageContentType.text, description="The type of the message.")
     text: str = Field(..., description="The text content of the message.")
 
 
@@ -44,27 +44,27 @@ class ImageSource(BaseModel):
 
 
 class UrlImage(ImageSource):
-    type: Literal[ImageSourceType.url] = Field(ImageSourceType.url, description="The source type for the image.")
+    type: Literal[ImageSourceType.url] = Field(default=ImageSourceType.url, description="The source type for the image.")
     url: str = Field(..., description="The URL of the image.")
 
 
 class Base64Image(ImageSource):
-    type: Literal[ImageSourceType.base64] = Field(ImageSourceType.base64, description="The source type for the image.")
+    type: Literal[ImageSourceType.base64] = Field(default=ImageSourceType.base64, description="The source type for the image.")
     media_type: str = Field(..., description="The media type for the image.")
     data: str = Field(..., description="The base64 encoded image data.")
     detail: Optional[str] = Field(
-        None,
+        default=None,
         description="What level of detail to use when processing and understanding the image (low, high, or auto to let the model decide)",
     )
 
 
 class LettaImage(ImageSource):
-    type: Literal[ImageSourceType.letta] = Field(ImageSourceType.letta, description="The source type for the image.")
+    type: Literal[ImageSourceType.letta] = Field(default=ImageSourceType.letta, description="The source type for the image.")
     file_id: str = Field(..., description="The unique identifier of the image file persisted in storage.")
-    media_type: Optional[str] = Field(None, description="The media type for the image.")
-    data: Optional[str] = Field(None, description="The base64 encoded image data.")
+    media_type: Optional[str] = Field(default=None, description="The media type for the image.")
+    data: Optional[str] = Field(default=None, description="The base64 encoded image data.")
     detail: Optional[str] = Field(
-        None,
+        default=None,
         description="What level of detail to use when processing and understanding the image (low, high, or auto to let the model decide)",
     )
 
@@ -73,7 +73,7 @@ ImageSourceUnion = Annotated[Union[UrlImage, Base64Image, LettaImage], Field(dis
 
 
 class ImageContent(MessageContent):
-    type: Literal[MessageContentType.image] = Field(MessageContentType.image, description="The type of the message.")
+    type: Literal[MessageContentType.image] = Field(default=MessageContentType.image, description="The type of the message.")
     source: ImageSourceUnion = Field(..., description="The source of the image.")
 
 
@@ -164,7 +164,7 @@ def get_letta_assistant_message_content_union_str_json_schema():
 
 class ToolCallContent(MessageContent):
     type: Literal[MessageContentType.tool_call] = Field(
-        MessageContentType.tool_call, description="Indicates this content represents a tool call event."
+        default=MessageContentType.tool_call, description="Indicates this content represents a tool call event."
     )
     id: str = Field(..., description="A unique identifier for this specific tool call instance.")
     name: str = Field(..., description="The name of the tool being called.")
@@ -175,7 +175,7 @@ class ToolCallContent(MessageContent):
 
 class ToolReturnContent(MessageContent):
     type: Literal[MessageContentType.tool_return] = Field(
-        MessageContentType.tool_return, description="Indicates this content represents a tool return event."
+        default=MessageContentType.tool_return, description="Indicates this content represents a tool return event."
     )
     tool_call_id: str = Field(..., description="References the ID of the ToolCallContent that initiated this tool call.")
     content: str = Field(..., description="The content returned by the tool execution.")
@@ -184,23 +184,23 @@ class ToolReturnContent(MessageContent):
 
 class ReasoningContent(MessageContent):
     type: Literal[MessageContentType.reasoning] = Field(
-        MessageContentType.reasoning, description="Indicates this is a reasoning/intermediate step."
+        default=MessageContentType.reasoning, description="Indicates this is a reasoning/intermediate step."
     )
     is_native: bool = Field(..., description="Whether the reasoning content was generated by a reasoner model that processed this step.")
     reasoning: str = Field(..., description="The intermediate reasoning or thought process content.")
-    signature: Optional[str] = Field(None, description="A unique identifier for this reasoning step.")
+    signature: Optional[str] = Field(default=None, description="A unique identifier for this reasoning step.")
 
 
 class RedactedReasoningContent(MessageContent):
     type: Literal[MessageContentType.redacted_reasoning] = Field(
-        MessageContentType.redacted_reasoning, description="Indicates this is a redacted thinking step."
+        default=MessageContentType.redacted_reasoning, description="Indicates this is a redacted thinking step."
     )
     data: str = Field(..., description="The redacted or filtered intermediate reasoning content.")
 
 
 class OmittedReasoningContent(MessageContent):
     type: Literal[MessageContentType.omitted_reasoning] = Field(
-        MessageContentType.omitted_reasoning, description="Indicates this is an omitted reasoning step."
+        default=MessageContentType.omitted_reasoning, description="Indicates this is an omitted reasoning step."
     )
     # NOTE: dropping because we don't track this kind of information for the other reasoning types
     # tokens: int = Field(..., description="The reasoning token count for intermediate reasoning content.")
diff --git a/letta/schemas/llm_config.py b/letta/schemas/llm_config.py
index dc0f4f58..342b8d8e 100644
--- a/letta/schemas/llm_config.py
+++ b/letta/schemas/llm_config.py
@@ -78,6 +78,10 @@ class LLMConfig(BaseModel):
         0,
         description="Configurable thinking budget for extended thinking. Used for enable_reasoner and also for Google Vertex models like Gemini 2.5 Flash. Minimum value is 1024 when used with enable_reasoner.",
     )
+    frequency_penalty: Optional[float] = Field(
+        None,  # Can also deafult to 0.0?
+        description="Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim. From OpenAI: Number between -2.0 and 2.0.",
+    )
 
     # FIXME hack to silence pydantic protected namespace warning
     model_config = ConfigDict(protected_namespaces=())
diff --git a/letta/schemas/message.py b/letta/schemas/message.py
index 34a0dfd9..2243cc96 100644
--- a/letta/schemas/message.py
+++ b/letta/schemas/message.py
@@ -84,11 +84,11 @@ class MessageCreate(BaseModel):
         description="The content of the message.",
         json_schema_extra=get_letta_message_content_union_str_json_schema(),
     )
-    name: Optional[str] = Field(None, description="The name of the participant.")
-    otid: Optional[str] = Field(None, description="The offline threading id associated with this message")
-    sender_id: Optional[str] = Field(None, description="The id of the sender of the message, can be an identity id or agent id")
-    batch_item_id: Optional[str] = Field(None, description="The id of the LLMBatchItem that this message is associated with")
-    group_id: Optional[str] = Field(None, description="The multi-agent group that the message was sent in")
+    name: Optional[str] = Field(default=None, description="The name of the participant.")
+    otid: Optional[str] = Field(default=None, description="The offline threading id associated with this message")
+    sender_id: Optional[str] = Field(default=None, description="The id of the sender of the message, can be an identity id or agent id")
+    batch_item_id: Optional[str] = Field(default=None, description="The id of the LLMBatchItem that this message is associated with")
+    group_id: Optional[str] = Field(default=None, description="The multi-agent group that the message was sent in")
 
     def model_dump(self, to_orm: bool = False, **kwargs) -> Dict[str, Any]:
         data = super().model_dump(**kwargs)
@@ -101,9 +101,9 @@ class MessageCreate(BaseModel):
 class MessageUpdate(BaseModel):
     """Request to update a message"""
 
-    role: Optional[MessageRole] = Field(None, description="The role of the participant.")
+    role: Optional[MessageRole] = Field(default=None, description="The role of the participant.")
     content: Optional[Union[str, List[LettaMessageContentUnion]]] = Field(
-        None,
+        default=None,
         description="The content of the message.",
         json_schema_extra=get_letta_message_content_union_str_json_schema(),
     )
@@ -112,11 +112,11 @@ class MessageUpdate(BaseModel):
     # agent_id: Optional[str] = Field(None, description="The unique identifier of the agent.")
     # NOTE: we probably shouldn't allow updating the model field, otherwise this loses meaning
     # model: Optional[str] = Field(None, description="The model used to make the function call.")
-    name: Optional[str] = Field(None, description="The name of the participant.")
+    name: Optional[str] = Field(default=None, description="The name of the participant.")
     # NOTE: we probably shouldn't allow updating the created_at field, right?
     # created_at: Optional[datetime] = Field(None, description="The time the message was created.")
-    tool_calls: Optional[List[OpenAIToolCall,]] = Field(None, description="The list of tool calls requested.")
-    tool_call_id: Optional[str] = Field(None, description="The id of the tool call.")
+    tool_calls: Optional[List[OpenAIToolCall,]] = Field(default=None, description="The list of tool calls requested.")
+    tool_call_id: Optional[str] = Field(default=None, description="The id of the tool call.")
 
     def model_dump(self, to_orm: bool = False, **kwargs) -> Dict[str, Any]:
         data = super().model_dump(**kwargs)
@@ -150,28 +150,28 @@ class Message(BaseMessage):
     """
 
     id: str = BaseMessage.generate_id_field()
-    organization_id: Optional[str] = Field(None, description="The unique identifier of the organization.")
-    agent_id: Optional[str] = Field(None, description="The unique identifier of the agent.")
-    model: Optional[str] = Field(None, description="The model used to make the function call.")
+    organization_id: Optional[str] = Field(default=None, description="The unique identifier of the organization.")
+    agent_id: Optional[str] = Field(default=None, description="The unique identifier of the agent.")
+    model: Optional[str] = Field(default=None, description="The model used to make the function call.")
     # Basic OpenAI-style fields
     role: MessageRole = Field(..., description="The role of the participant.")
-    content: Optional[List[LettaMessageContentUnion]] = Field(None, description="The content of the message.")
+    content: Optional[List[LettaMessageContentUnion]] = Field(default=None, description="The content of the message.")
     # NOTE: in OpenAI, this field is only used for roles 'user', 'assistant', and 'function' (now deprecated). 'tool' does not use it.
     name: Optional[str] = Field(
-        None,
+        default=None,
         description="For role user/assistant: the (optional) name of the participant. For role tool/function: the name of the function called.",
     )
     tool_calls: Optional[List[OpenAIToolCall]] = Field(
-        None, description="The list of tool calls requested. Only applicable for role assistant."
+        default=None, description="The list of tool calls requested. Only applicable for role assistant."
     )
-    tool_call_id: Optional[str] = Field(None, description="The ID of the tool call. Only applicable for role tool.")
+    tool_call_id: Optional[str] = Field(default=None, description="The ID of the tool call. Only applicable for role tool.")
     # Extras
-    step_id: Optional[str] = Field(None, description="The id of the step that this message was created in.")
-    otid: Optional[str] = Field(None, description="The offline threading id associated with this message")
-    tool_returns: Optional[List[ToolReturn]] = Field(None, description="Tool execution return information for prior tool calls")
-    group_id: Optional[str] = Field(None, description="The multi-agent group that the message was sent in")
-    sender_id: Optional[str] = Field(None, description="The id of the sender of the message, can be an identity id or agent id")
-    batch_item_id: Optional[str] = Field(None, description="The id of the LLMBatchItem that this message is associated with")
+    step_id: Optional[str] = Field(default=None, description="The id of the step that this message was created in.")
+    otid: Optional[str] = Field(default=None, description="The offline threading id associated with this message")
+    tool_returns: Optional[List[ToolReturn]] = Field(default=None, description="Tool execution return information for prior tool calls")
+    group_id: Optional[str] = Field(default=None, description="The multi-agent group that the message was sent in")
+    sender_id: Optional[str] = Field(default=None, description="The id of the sender of the message, can be an identity id or agent id")
+    batch_item_id: Optional[str] = Field(default=None, description="The id of the LLMBatchItem that this message is associated with")
     # This overrides the optional base orm schema, created_at MUST exist on all messages objects
     created_at: datetime = Field(default_factory=get_utc_time, description="The timestamp when the object was created.")
 
@@ -482,7 +482,9 @@ class Message(BaseMessage):
         # TODO(caren) implicit support for only non-parts/list content types
         if openai_message_dict["content"] is not None and type(openai_message_dict["content"]) is not str:
             raise ValueError(f"Invalid content type: {type(openai_message_dict['content'])}")
-        content = [TextContent(text=openai_message_dict["content"])] if openai_message_dict["content"] else []
+        content: List[LettaMessageContentUnion] = (
+            [TextContent(text=openai_message_dict["content"])] if openai_message_dict["content"] else []
+        )
 
         # TODO(caren) bad assumption here that "reasoning_content" always comes before "redacted_reasoning_content"
         if "reasoning_content" in openai_message_dict and openai_message_dict["reasoning_content"]:
@@ -491,14 +493,16 @@ class Message(BaseMessage):
                     reasoning=openai_message_dict["reasoning_content"],
                     is_native=True,
                     signature=(
-                        openai_message_dict["reasoning_content_signature"] if openai_message_dict["reasoning_content_signature"] else None
+                        str(openai_message_dict["reasoning_content_signature"])
+                        if "reasoning_content_signature" in openai_message_dict
+                        else None
                     ),
                 ),
             )
         if "redacted_reasoning_content" in openai_message_dict and openai_message_dict["redacted_reasoning_content"]:
             content.append(
                 RedactedReasoningContent(
-                    data=openai_message_dict["redacted_reasoning_content"] if "redacted_reasoning_content" in openai_message_dict else None,
+                    data=str(openai_message_dict["redacted_reasoning_content"]),
                 ),
             )
         if "omitted_reasoning_content" in openai_message_dict and openai_message_dict["omitted_reasoning_content"]:
@@ -694,7 +698,7 @@ class Message(BaseMessage):
         elif self.role == "assistant":
             assert self.tool_calls is not None or text_content is not None
             openai_message = {
-                "content": None if put_inner_thoughts_in_kwargs else text_content,
+                "content": None if (put_inner_thoughts_in_kwargs and self.tool_calls is not None) else text_content,
                 "role": self.role,
             }
 
@@ -733,7 +737,7 @@ class Message(BaseMessage):
             else:
                 warnings.warn(f"Using OpenAI with invalid 'name' field (name={self.name} role={self.role}).")
 
-        if parse_content_parts:
+        if parse_content_parts and self.content is not None:
             for content in self.content:
                 if isinstance(content, ReasoningContent):
                     openai_message["reasoning_content"] = content.reasoning
@@ -819,7 +823,7 @@ class Message(BaseMessage):
             }
             content = []
             # COT / reasoning / thinking
-            if len(self.content) > 1:
+            if self.content is not None and len(self.content) > 1:
                 for content_part in self.content:
                     if isinstance(content_part, ReasoningContent):
                         content.append(
@@ -1154,6 +1158,6 @@ class Message(BaseMessage):
 
 class ToolReturn(BaseModel):
     status: Literal["success", "error"] = Field(..., description="The status of the tool call")
-    stdout: Optional[List[str]] = Field(None, description="Captured stdout (e.g. prints, logs) from the tool invocation")
-    stderr: Optional[List[str]] = Field(None, description="Captured stderr from the tool invocation")
+    stdout: Optional[List[str]] = Field(default=None, description="Captured stdout (e.g. prints, logs) from the tool invocation")
+    stderr: Optional[List[str]] = Field(default=None, description="Captured stderr from the tool invocation")
     # func_return: Optional[Any] = Field(None, description="The function return object")
diff --git a/letta/schemas/providers.py b/letta/schemas/providers.py
index 9e741dd8..51d988bc 100644
--- a/letta/schemas/providers.py
+++ b/letta/schemas/providers.py
@@ -324,18 +324,25 @@ class OpenAIProvider(Provider):
             else:
                 handle = self.get_handle(model_name)
 
-            configs.append(
-                LLMConfig(
-                    model=model_name,
-                    model_endpoint_type="openai",
-                    model_endpoint=self.base_url,
-                    context_window=context_window_size,
-                    handle=handle,
-                    provider_name=self.name,
-                    provider_category=self.provider_category,
-                )
+            llm_config = LLMConfig(
+                model=model_name,
+                model_endpoint_type="openai",
+                model_endpoint=self.base_url,
+                context_window=context_window_size,
+                handle=handle,
+                provider_name=self.name,
+                provider_category=self.provider_category,
             )
 
+            # gpt-4o-mini has started to regress with pretty bad emoji spam loops
+            # this is to counteract that
+            if "gpt-4o-mini" in model_name:
+                llm_config.frequency_penalty = 1.0
+            if "gpt-4.1-mini" in model_name:
+                llm_config.frequency_penalty = 1.0
+
+            configs.append(llm_config)
+
         # for OpenAI, sort in reverse order
         if self.base_url == "https://api.openai.com/v1":
             # alphnumeric sort
diff --git a/letta/server/rest_api/app.py b/letta/server/rest_api/app.py
index a3075a8e..8ddb175f 100644
--- a/letta/server/rest_api/app.py
+++ b/letta/server/rest_api/app.py
@@ -17,6 +17,7 @@ from letta.__init__ import __version__ as letta_version
 from letta.agents.exceptions import IncompatibleAgentType
 from letta.constants import ADMIN_PREFIX, API_PREFIX, OPENAI_API_PREFIX
 from letta.errors import BedrockPermissionError, LettaAgentNotFoundError, LettaUserNotFoundError
+from letta.helpers.pinecone_utils import get_pinecone_indices, should_use_pinecone, upsert_pinecone_indices
 from letta.jobs.scheduler import start_scheduler_with_leader_election
 from letta.log import get_logger
 from letta.orm.errors import DatabaseTimeoutError, ForeignKeyConstraintViolationError, NoResultFound, UniqueConstraintViolationError
@@ -127,6 +128,16 @@ async def lifespan(app_: FastAPI):
     db_registry.initialize_async()
     logger.info(f"[Worker {worker_id}] Database connections initialized")
 
+    if should_use_pinecone():
+        if settings.upsert_pinecone_indices:
+            logger.info(f"[Worker {worker_id}] Upserting pinecone indices: {get_pinecone_indices()}")
+            await upsert_pinecone_indices()
+            logger.info(f"[Worker {worker_id}] Upserted pinecone indices")
+        else:
+            logger.info(f"[Worker {worker_id}] Enabled pinecone")
+    else:
+        logger.info(f"[Worker {worker_id}] Disabled pinecone")
+
     logger.info(f"[Worker {worker_id}] Starting scheduler with leader election")
     global server
     try:
diff --git a/letta/server/rest_api/routers/v1/agents.py b/letta/server/rest_api/routers/v1/agents.py
index 7f8ce572..6f0df27d 100644
--- a/letta/server/rest_api/routers/v1/agents.py
+++ b/letta/server/rest_api/routers/v1/agents.py
@@ -38,6 +38,7 @@ from letta.schemas.user import User
 from letta.serialize_schemas.pydantic_agent_schema import AgentSchema
 from letta.server.rest_api.utils import get_letta_server
 from letta.server.server import SyncServer
+from letta.services.summarizer.enums import SummarizationMode
 from letta.services.telemetry_manager import NoopTelemetryManager
 from letta.settings import settings
 from letta.utils import safe_create_task
@@ -750,6 +751,12 @@ async def send_message(
                     step_manager=server.step_manager,
                     telemetry_manager=server.telemetry_manager if settings.llm_api_logging else NoopTelemetryManager(),
                     current_run_id=run.id,
+                    # summarizer settings to be added here
+                    summarizer_mode=(
+                        SummarizationMode.STATIC_MESSAGE_BUFFER
+                        if agent.agent_type == AgentType.voice_convo_agent
+                        else SummarizationMode.PARTIAL_EVICT_MESSAGE_BUFFER
+                    ),
                 )
 
             result = await agent_loop.step(
@@ -878,6 +885,12 @@ async def send_message_streaming(
                     step_manager=server.step_manager,
                     telemetry_manager=server.telemetry_manager if settings.llm_api_logging else NoopTelemetryManager(),
                     current_run_id=run.id,
+                    # summarizer settings to be added here
+                    summarizer_mode=(
+                        SummarizationMode.STATIC_MESSAGE_BUFFER
+                        if agent.agent_type == AgentType.voice_convo_agent
+                        else SummarizationMode.PARTIAL_EVICT_MESSAGE_BUFFER
+                    ),
                 )
             from letta.server.rest_api.streaming_response import StreamingResponseWithStatusCode
 
@@ -1014,6 +1027,12 @@ async def _process_message_background(
                     actor=actor,
                     step_manager=server.step_manager,
                     telemetry_manager=server.telemetry_manager if settings.llm_api_logging else NoopTelemetryManager(),
+                    # summarizer settings to be added here
+                    summarizer_mode=(
+                        SummarizationMode.STATIC_MESSAGE_BUFFER
+                        if agent.agent_type == AgentType.voice_convo_agent
+                        else SummarizationMode.PARTIAL_EVICT_MESSAGE_BUFFER
+                    ),
                 )
 
             result = await agent_loop.step(
diff --git a/letta/server/rest_api/routers/v1/sources.py b/letta/server/rest_api/routers/v1/sources.py
index a3e32df2..bb6694eb 100644
--- a/letta/server/rest_api/routers/v1/sources.py
+++ b/letta/server/rest_api/routers/v1/sources.py
@@ -9,6 +9,12 @@ from fastapi import APIRouter, Depends, Header, HTTPException, Query, UploadFile
 from starlette import status
 
 import letta.constants as constants
+from letta.helpers.pinecone_utils import (
+    delete_file_records_from_pinecone_index,
+    delete_source_records_from_pinecone_index,
+    list_pinecone_index_for_files,
+    should_use_pinecone,
+)
 from letta.log import get_logger
 from letta.otel.tracing import trace_method
 from letta.schemas.agent import AgentState
@@ -22,6 +28,7 @@ from letta.server.rest_api.utils import get_letta_server
 from letta.server.server import SyncServer
 from letta.services.file_processor.chunker.llama_index_chunker import LlamaIndexChunker
 from letta.services.file_processor.embedder.openai_embedder import OpenAIEmbedder
+from letta.services.file_processor.embedder.pinecone_embedder import PineconeEmbedder
 from letta.services.file_processor.file_processor import FileProcessor
 from letta.services.file_processor.file_types import (
     get_allowed_media_types,
@@ -163,6 +170,10 @@ async def delete_source(
     files = await server.file_manager.list_files(source_id, actor)
     file_ids = [f.id for f in files]
 
+    if should_use_pinecone():
+        logger.info(f"Deleting source {source_id} from pinecone index")
+        await delete_source_records_from_pinecone_index(source_id=source_id, actor=actor)
+
     for agent_state in agent_states:
         await server.remove_files_from_context_window(agent_state=agent_state, file_ids=file_ids, actor=actor)
 
@@ -326,16 +337,24 @@ async def get_file_metadata(
     """
     actor = await server.user_manager.get_actor_or_default_async(actor_id=actor_id)
 
-    # Verify the source exists and user has access
-    source = await server.source_manager.get_source_by_id(source_id=source_id, actor=actor)
-    if not source:
-        raise HTTPException(status_code=404, detail=f"Source with id={source_id} not found.")
-
     # Get file metadata using the file manager
     file_metadata = await server.file_manager.get_file_by_id(
         file_id=file_id, actor=actor, include_content=include_content, strip_directory_prefix=True
     )
 
+    if should_use_pinecone() and not file_metadata.is_processing_terminal():
+        ids = await list_pinecone_index_for_files(file_id=file_id, actor=actor, limit=file_metadata.total_chunks)
+        logger.info(f"Embedded chunks {len(ids)}/{file_metadata.total_chunks} for {file_id} in organization {actor.organization_id}")
+
+        if len(ids) != file_metadata.chunks_embedded or len(ids) == file_metadata.total_chunks:
+            if len(ids) != file_metadata.total_chunks:
+                file_status = file_metadata.processing_status
+            else:
+                file_status = FileProcessingStatus.COMPLETED
+            await server.file_manager.update_file_status(
+                file_id=file_metadata.id, actor=actor, chunks_embedded=len(ids), processing_status=file_status
+            )
+
     if not file_metadata:
         raise HTTPException(status_code=404, detail=f"File with id={file_id} not found.")
 
@@ -364,6 +383,10 @@ async def delete_file_from_source(
 
     await server.remove_file_from_context_windows(source_id=source_id, file_id=deleted_file.id, actor=actor)
 
+    if should_use_pinecone():
+        logger.info(f"Deleting file {file_id} from pinecone index")
+        await delete_file_records_from_pinecone_index(file_id=file_id, actor=actor)
+
     asyncio.create_task(sleeptime_document_ingest_async(server, source_id, actor, clear_history=True))
     if deleted_file is None:
         raise HTTPException(status_code=404, detail=f"File with id={file_id} not found.")
@@ -402,8 +425,14 @@ async def load_file_to_source_cloud(
 ):
     file_processor = MistralFileParser()
     text_chunker = LlamaIndexChunker(chunk_size=embedding_config.embedding_chunk_size)
-    embedder = OpenAIEmbedder(embedding_config=embedding_config)
-    file_processor = FileProcessor(file_parser=file_processor, text_chunker=text_chunker, embedder=embedder, actor=actor)
+    using_pinecone = should_use_pinecone()
+    if using_pinecone:
+        embedder = PineconeEmbedder()
+    else:
+        embedder = OpenAIEmbedder(embedding_config=embedding_config)
+    file_processor = FileProcessor(
+        file_parser=file_processor, text_chunker=text_chunker, embedder=embedder, actor=actor, using_pinecone=using_pinecone
+    )
     await file_processor.process(
         server=server, agent_states=agent_states, source_id=source_id, content=content, file_metadata=file_metadata
     )
diff --git a/letta/services/file_manager.py b/letta/services/file_manager.py
index fbfa44e8..ed1ee7bd 100644
--- a/letta/services/file_manager.py
+++ b/letta/services/file_manager.py
@@ -109,15 +109,17 @@ class FileManager:
         actor: PydanticUser,
         processing_status: Optional[FileProcessingStatus] = None,
         error_message: Optional[str] = None,
+        total_chunks: Optional[int] = None,
+        chunks_embedded: Optional[int] = None,
     ) -> PydanticFileMetadata:
         """
-        Update processing_status and/or error_message on a FileMetadata row.
+        Update processing_status, error_message, total_chunks, and/or chunks_embedded on a FileMetadata row.
 
         * 1st round-trip → UPDATE
         * 2nd round-trip → SELECT fresh row (same as read_async)
         """
 
-        if processing_status is None and error_message is None:
+        if processing_status is None and error_message is None and total_chunks is None and chunks_embedded is None:
             raise ValueError("Nothing to update")
 
         values: dict[str, object] = {"updated_at": datetime.utcnow()}
@@ -125,6 +127,10 @@ class FileManager:
             values["processing_status"] = processing_status
         if error_message is not None:
             values["error_message"] = error_message
+        if total_chunks is not None:
+            values["total_chunks"] = total_chunks
+        if chunks_embedded is not None:
+            values["chunks_embedded"] = chunks_embedded
 
         async with db_registry.async_session() as session:
             # Fast in-place update – no ORM hydration
diff --git a/letta/services/file_processor/embedder/base_embedder.py b/letta/services/file_processor/embedder/base_embedder.py
new file mode 100644
index 00000000..b9310c3e
--- /dev/null
+++ b/letta/services/file_processor/embedder/base_embedder.py
@@ -0,0 +1,16 @@
+from abc import ABC, abstractmethod
+from typing import List
+
+from letta.log import get_logger
+from letta.schemas.passage import Passage
+from letta.schemas.user import User
+
+logger = get_logger(__name__)
+
+
+class BaseEmbedder(ABC):
+    """Abstract base class for embedding generation"""
+
+    @abstractmethod
+    async def generate_embedded_passages(self, file_id: str, source_id: str, chunks: List[str], actor: User) -> List[Passage]:
+        """Generate embeddings for chunks with batching and concurrent processing"""
diff --git a/letta/services/file_processor/embedder/openai_embedder.py b/letta/services/file_processor/embedder/openai_embedder.py
index b831c819..ce43a72d 100644
--- a/letta/services/file_processor/embedder/openai_embedder.py
+++ b/letta/services/file_processor/embedder/openai_embedder.py
@@ -9,12 +9,13 @@ from letta.schemas.embedding_config import EmbeddingConfig
 from letta.schemas.enums import ProviderType
 from letta.schemas.passage import Passage
 from letta.schemas.user import User
+from letta.services.file_processor.embedder.base_embedder import BaseEmbedder
 from letta.settings import model_settings
 
 logger = get_logger(__name__)
 
 
-class OpenAIEmbedder:
+class OpenAIEmbedder(BaseEmbedder):
     """OpenAI-based embedding generation"""
 
     def __init__(self, embedding_config: Optional[EmbeddingConfig] = None):
@@ -24,6 +25,7 @@ class OpenAIEmbedder:
             else EmbeddingConfig.default_config(model_name="letta")
         )
         self.embedding_config = embedding_config or self.default_embedding_config
+        self.max_concurrent_requests = 20
 
         # TODO: Unify to global OpenAI client
         self.client: OpenAIClient = cast(
@@ -34,7 +36,6 @@ class OpenAIEmbedder:
                 actor=None,  # Not necessary
             ),
         )
-        self.max_concurrent_requests = 20
 
     @trace_method
     async def _embed_batch(self, batch: List[str], batch_indices: List[int]) -> List[Tuple[int, List[float]]]:
diff --git a/letta/services/file_processor/embedder/pinecone_embedder.py b/letta/services/file_processor/embedder/pinecone_embedder.py
new file mode 100644
index 00000000..212ba95d
--- /dev/null
+++ b/letta/services/file_processor/embedder/pinecone_embedder.py
@@ -0,0 +1,74 @@
+from typing import List
+
+from letta.helpers.pinecone_utils import upsert_file_records_to_pinecone_index
+from letta.log import get_logger
+from letta.otel.tracing import log_event, trace_method
+from letta.schemas.passage import Passage
+from letta.schemas.user import User
+from letta.services.file_processor.embedder.base_embedder import BaseEmbedder
+
+try:
+    PINECONE_AVAILABLE = True
+except ImportError:
+    PINECONE_AVAILABLE = False
+
+logger = get_logger(__name__)
+
+
+class PineconeEmbedder(BaseEmbedder):
+    """Pinecone-based embedding generation"""
+
+    def __init__(self):
+        if not PINECONE_AVAILABLE:
+            raise ImportError("Pinecone package is not installed. Install it with: pip install pinecone")
+
+        super().__init__()
+
+    @trace_method
+    async def generate_embedded_passages(self, file_id: str, source_id: str, chunks: List[str], actor: User) -> List[Passage]:
+        """Generate embeddings and upsert to Pinecone, then return Passage objects"""
+        if not chunks:
+            return []
+
+        logger.info(f"Upserting {len(chunks)} chunks to Pinecone using namespace {source_id}")
+        log_event(
+            "embedder.generation_started",
+            {
+                "total_chunks": len(chunks),
+                "file_id": file_id,
+                "source_id": source_id,
+            },
+        )
+
+        # Upsert records to Pinecone using source_id as namespace
+        try:
+            await upsert_file_records_to_pinecone_index(file_id=file_id, source_id=source_id, chunks=chunks, actor=actor)
+            logger.info(f"Successfully kicked off upserting {len(chunks)} records to Pinecone")
+            log_event(
+                "embedder.upsert_started",
+                {"records_upserted": len(chunks), "namespace": source_id, "file_id": file_id},
+            )
+        except Exception as e:
+            logger.error(f"Failed to upsert records to Pinecone: {str(e)}")
+            log_event("embedder.upsert_failed", {"error": str(e), "error_type": type(e).__name__})
+            raise
+
+        # Create Passage objects (without embeddings since Pinecone handles them)
+        passages = []
+        for i, text in enumerate(chunks):
+            passage = Passage(
+                text=text,
+                file_id=file_id,
+                source_id=source_id,
+                embedding=None,  # Pinecone handles embeddings internally
+                embedding_config=None,  # None
+                organization_id=actor.organization_id,
+            )
+            passages.append(passage)
+
+        logger.info(f"Successfully created {len(passages)} passages")
+        log_event(
+            "embedder.generation_completed",
+            {"passages_created": len(passages), "total_chunks_processed": len(chunks), "file_id": file_id, "source_id": source_id},
+        )
+        return passages
diff --git a/letta/services/file_processor/file_processor.py b/letta/services/file_processor/file_processor.py
index 67c086ed..cbf70b1c 100644
--- a/letta/services/file_processor/file_processor.py
+++ b/letta/services/file_processor/file_processor.py
@@ -11,7 +11,7 @@ from letta.server.server import SyncServer
 from letta.services.file_manager import FileManager
 from letta.services.file_processor.chunker.line_chunker import LineChunker
 from letta.services.file_processor.chunker.llama_index_chunker import LlamaIndexChunker
-from letta.services.file_processor.embedder.openai_embedder import OpenAIEmbedder
+from letta.services.file_processor.embedder.base_embedder import BaseEmbedder
 from letta.services.file_processor.parser.mistral_parser import MistralFileParser
 from letta.services.job_manager import JobManager
 from letta.services.passage_manager import PassageManager
@@ -27,8 +27,9 @@ class FileProcessor:
         self,
         file_parser: MistralFileParser,
         text_chunker: LlamaIndexChunker,
-        embedder: OpenAIEmbedder,
+        embedder: BaseEmbedder,
         actor: User,
+        using_pinecone: bool,
         max_file_size: int = 50 * 1024 * 1024,  # 50MB default
     ):
         self.file_parser = file_parser
@@ -41,6 +42,7 @@ class FileProcessor:
         self.passage_manager = PassageManager()
         self.job_manager = JobManager()
         self.actor = actor
+        self.using_pinecone = using_pinecone
 
     # TODO: Factor this function out of SyncServer
     @trace_method
@@ -109,7 +111,7 @@ class FileProcessor:
 
             logger.info("Chunking extracted text")
             log_event("file_processor.chunking_started", {"filename": filename, "pages_to_process": len(ocr_response.pages)})
-            all_passages = []
+            all_chunks = []
 
             for page in ocr_response.pages:
                 chunks = self.text_chunker.chunk_text(page)
@@ -118,24 +120,17 @@ class FileProcessor:
                     log_event("file_processor.chunking_failed", {"filename": filename, "page_index": ocr_response.pages.index(page)})
                     raise ValueError("No chunks created from text")
 
-                passages = await self.embedder.generate_embedded_passages(
-                    file_id=file_metadata.id, source_id=source_id, chunks=chunks, actor=self.actor
-                )
-                log_event(
-                    "file_processor.page_processed",
-                    {
-                        "filename": filename,
-                        "page_index": ocr_response.pages.index(page),
-                        "chunks_created": len(chunks),
-                        "passages_generated": len(passages),
-                    },
-                )
-                all_passages.extend(passages)
+                all_chunks.extend(self.text_chunker.chunk_text(page))
 
-            all_passages = await self.passage_manager.create_many_source_passages_async(
-                passages=all_passages, file_metadata=file_metadata, actor=self.actor
+            all_passages = await self.embedder.generate_embedded_passages(
+                file_id=file_metadata.id, source_id=source_id, chunks=all_chunks, actor=self.actor
             )
-            log_event("file_processor.passages_created", {"filename": filename, "total_passages": len(all_passages)})
+
+            if not self.using_pinecone:
+                all_passages = await self.passage_manager.create_many_source_passages_async(
+                    passages=all_passages, file_metadata=file_metadata, actor=self.actor
+                )
+                log_event("file_processor.passages_created", {"filename": filename, "total_passages": len(all_passages)})
 
             logger.info(f"Successfully processed {filename}: {len(all_passages)} passages")
             log_event(
@@ -149,9 +144,14 @@ class FileProcessor:
             )
 
             # update job status
-            await self.file_manager.update_file_status(
-                file_id=file_metadata.id, actor=self.actor, processing_status=FileProcessingStatus.COMPLETED
-            )
+            if not self.using_pinecone:
+                await self.file_manager.update_file_status(
+                    file_id=file_metadata.id, actor=self.actor, processing_status=FileProcessingStatus.COMPLETED
+                )
+            else:
+                await self.file_manager.update_file_status(
+                    file_id=file_metadata.id, actor=self.actor, total_chunks=len(all_passages), chunks_embedded=0
+                )
 
             return all_passages
 
diff --git a/letta/services/job_manager.py b/letta/services/job_manager.py
index ca5fa9b9..5b8e2693 100644
--- a/letta/services/job_manager.py
+++ b/letta/services/job_manager.py
@@ -115,10 +115,6 @@ class JobManager:
                 job.completed_at = get_utc_time().replace(tzinfo=None)
                 if job.callback_url:
                     await self._dispatch_callback_async(job)
-                else:
-                    logger.info(f"Job does not contain callback url: {job}")
-            else:
-                logger.info(f"Job update is not terminal {job_update}")
 
             # Save the updated job to the database
             await job.update_async(db_session=session, actor=actor)
diff --git a/letta/services/source_manager.py b/letta/services/source_manager.py
index 88aa4c6d..9e3ee4d2 100644
--- a/letta/services/source_manager.py
+++ b/letta/services/source_manager.py
@@ -19,7 +19,6 @@ class SourceManager:
     @trace_method
     async def create_source(self, source: PydanticSource, actor: PydanticUser) -> PydanticSource:
         """Create a new source based on the PydanticSource schema."""
-        # Try getting the source first by id
         db_source = await self.get_source_by_id(source.id, actor=actor)
         if db_source:
             return db_source
diff --git a/letta/services/summarizer/enums.py b/letta/services/summarizer/enums.py
index 33c42d65..620ec332 100644
--- a/letta/services/summarizer/enums.py
+++ b/letta/services/summarizer/enums.py
@@ -7,3 +7,4 @@ class SummarizationMode(str, Enum):
     """
 
     STATIC_MESSAGE_BUFFER = "static_message_buffer_mode"
+    PARTIAL_EVICT_MESSAGE_BUFFER = "partial_evict_message_buffer_mode"
diff --git a/letta/services/summarizer/summarizer.py b/letta/services/summarizer/summarizer.py
index 7795117e..699998e6 100644
--- a/letta/services/summarizer/summarizer.py
+++ b/letta/services/summarizer/summarizer.py
@@ -4,13 +4,19 @@ import traceback
 from typing import List, Optional, Tuple, Union
 
 from letta.agents.ephemeral_summary_agent import EphemeralSummaryAgent
-from letta.constants import DEFAULT_MESSAGE_TOOL, DEFAULT_MESSAGE_TOOL_KWARG
+from letta.constants import DEFAULT_MESSAGE_TOOL, DEFAULT_MESSAGE_TOOL_KWARG, MESSAGE_SUMMARY_REQUEST_ACK
+from letta.helpers.message_helper import convert_message_creates_to_messages
+from letta.llm_api.llm_client import LLMClient
 from letta.log import get_logger
 from letta.otel.tracing import trace_method
+from letta.prompts import gpt_summarize
 from letta.schemas.enums import MessageRole
 from letta.schemas.letta_message_content import TextContent
+from letta.schemas.llm_config import LLMConfig
 from letta.schemas.message import Message, MessageCreate
+from letta.schemas.user import User
 from letta.services.summarizer.enums import SummarizationMode
+from letta.system import package_summarize_message_no_counts
 from letta.templates.template_helper import render_template
 
 logger = get_logger(__name__)
@@ -29,18 +35,24 @@ class Summarizer:
         summarizer_agent: Optional[Union[EphemeralSummaryAgent, "VoiceSleeptimeAgent"]] = None,
         message_buffer_limit: int = 10,
         message_buffer_min: int = 3,
+        partial_evict_summarizer_percentage: float = 0.30,
     ):
         self.mode = mode
 
         # Need to do validation on this
+        # TODO: Move this to config
         self.message_buffer_limit = message_buffer_limit
         self.message_buffer_min = message_buffer_min
         self.summarizer_agent = summarizer_agent
-        # TODO: Move this to config
+        self.partial_evict_summarizer_percentage = partial_evict_summarizer_percentage
 
     @trace_method
-    def summarize(
-        self, in_context_messages: List[Message], new_letta_messages: List[Message], force: bool = False, clear: bool = False
+    async def summarize(
+        self,
+        in_context_messages: List[Message],
+        new_letta_messages: List[Message],
+        force: bool = False,
+        clear: bool = False,
     ) -> Tuple[List[Message], bool]:
         """
         Summarizes or trims in_context_messages according to the chosen mode,
@@ -58,7 +70,19 @@ class Summarizer:
                              (could be appended to the conversation if desired)
         """
         if self.mode == SummarizationMode.STATIC_MESSAGE_BUFFER:
-            return self._static_buffer_summarization(in_context_messages, new_letta_messages, force=force, clear=clear)
+            return self._static_buffer_summarization(
+                in_context_messages,
+                new_letta_messages,
+                force=force,
+                clear=clear,
+            )
+        elif self.mode == SummarizationMode.PARTIAL_EVICT_MESSAGE_BUFFER:
+            return await self._partial_evict_buffer_summarization(
+                in_context_messages,
+                new_letta_messages,
+                force=force,
+                clear=clear,
+            )
         else:
             # Fallback or future logic
             return in_context_messages, False
@@ -75,9 +99,131 @@ class Summarizer:
         task.add_done_callback(callback)
         return task
 
-    def _static_buffer_summarization(
-        self, in_context_messages: List[Message], new_letta_messages: List[Message], force: bool = False, clear: bool = False
+    async def _partial_evict_buffer_summarization(
+        self,
+        in_context_messages: List[Message],
+        new_letta_messages: List[Message],
+        force: bool = False,
+        clear: bool = False,
     ) -> Tuple[List[Message], bool]:
+        """Summarization as implemented in the original MemGPT loop, but using message count instead of token count.
+        Evict a partial amount of messages, and replace message[1] with a recursive summary.
+
+        Note that this can't be made sync, because we're waiting on the summary to inject it into the context window,
+        unlike the version that writes it to a block.
+
+        Unless force is True, don't summarize.
+        Ignore clear, we don't use it.
+        """
+        all_in_context_messages = in_context_messages + new_letta_messages
+
+        if not force:
+            logger.debug("Not forcing summarization, returning in-context messages as is.")
+            return all_in_context_messages, False
+
+        # Very ugly code to pull LLMConfig etc from the SummarizerAgent if we're not using it for anything else
+        assert self.summarizer_agent is not None
+
+        # First step: determine how many messages to retain
+        total_message_count = len(all_in_context_messages)
+        assert self.partial_evict_summarizer_percentage >= 0.0 and self.partial_evict_summarizer_percentage <= 1.0
+        target_message_start = round((1.0 - self.partial_evict_summarizer_percentage) * total_message_count)
+        logger.info(f"Target message count: {total_message_count}->{(total_message_count-target_message_start)}")
+
+        # The summary message we'll insert is role 'user' (vs 'assistant', 'tool', or 'system')
+        # We are going to put it at index 1 (index 0 is the system message)
+        # That means that index 2 needs to be role 'assistant', so walk up the list starting at
+        # the target_message_count and find the first assistant message
+        for i in range(target_message_start, total_message_count):
+            if all_in_context_messages[i].role == MessageRole.assistant:
+                assistant_message_index = i
+                break
+        else:
+            raise ValueError(f"No assistant message found from indices {target_message_start} to {total_message_count}")
+
+        # The sequence to summarize is index 1 -> assistant_message_index
+        messages_to_summarize = all_in_context_messages[1:assistant_message_index]
+        logger.info(f"Eviction indices: {1}->{assistant_message_index}(/{total_message_count})")
+
+        # Dynamically get the LLMConfig from the summarizer agent
+        # Pretty cringe code here that we need the agent for this but we don't use it
+        agent_state = await self.summarizer_agent.agent_manager.get_agent_by_id_async(
+            agent_id=self.summarizer_agent.agent_id, actor=self.summarizer_agent.actor
+        )
+
+        # TODO if we do this via the "agent", then we can more easily allow toggling on the memory block version
+        summary_message_str = await simple_summary(
+            messages=messages_to_summarize,
+            llm_config=agent_state.llm_config,
+            actor=self.summarizer_agent.actor,
+            include_ack=True,
+        )
+
+        # TODO add counts back
+        # Recall message count
+        # num_recall_messages_current = await self.message_manager.size_async(actor=self.actor, agent_id=agent_state.id)
+        # num_messages_evicted = len(messages_to_summarize)
+        # num_recall_messages_hidden = num_recall_messages_total - len()
+
+        # Create the summary message
+        summary_message_str_packed = package_summarize_message_no_counts(
+            summary=summary_message_str,
+            timezone=agent_state.timezone,
+        )
+        summary_message_obj = convert_message_creates_to_messages(
+            message_creates=[
+                MessageCreate(
+                    role=MessageRole.user,
+                    content=[TextContent(text=summary_message_str_packed)],
+                )
+            ],
+            agent_id=agent_state.id,
+            timezone=agent_state.timezone,
+            # We already packed, don't pack again
+            wrap_user_message=False,
+            wrap_system_message=False,
+        )[0]
+
+        # Create the message in the DB
+        await self.summarizer_agent.message_manager.create_many_messages_async(
+            pydantic_msgs=[summary_message_obj],
+            actor=self.summarizer_agent.actor,
+        )
+
+        updated_in_context_messages = all_in_context_messages[assistant_message_index:]
+        return [all_in_context_messages[0], summary_message_obj] + updated_in_context_messages, True
+
+    def _static_buffer_summarization(
+        self,
+        in_context_messages: List[Message],
+        new_letta_messages: List[Message],
+        force: bool = False,
+        clear: bool = False,
+    ) -> Tuple[List[Message], bool]:
+        """
+        Implements static buffer summarization by maintaining a fixed-size message buffer (< N messages).
+
+        Logic:
+        1. Combine existing context messages with new messages
+        2. If total messages <= buffer limit and not forced, return unchanged
+        3. Calculate how many messages to retain (0 if clear=True, otherwise message_buffer_min)
+        4. Find the trim index to keep the most recent messages while preserving user message boundaries
+        5. Evict older messages (everything between system message and trim index)
+        6. If summarizer agent is available, trigger background summarization of evicted messages
+        7. Return updated context with system message + retained recent messages
+
+        Args:
+            in_context_messages: Existing conversation context messages
+            new_letta_messages: Newly added messages to append
+            force: Force summarization even if buffer limit not exceeded
+            clear: Clear all messages except system message (retain_count = 0)
+
+        Returns:
+            Tuple of (updated_messages, was_summarized)
+            - updated_messages: New context after trimming/summarization
+            - was_summarized: True if messages were evicted and summarization triggered
+        """
+
         all_in_context_messages = in_context_messages + new_letta_messages
 
         if len(all_in_context_messages) <= self.message_buffer_limit and not force:
@@ -139,6 +285,91 @@ class Summarizer:
         return [all_in_context_messages[0]] + updated_in_context_messages, True
 
 
+def simple_formatter(messages: List[Message], include_system: bool = False) -> str:
+    """Go from an OpenAI-style list of messages to a concatenated string"""
+
+    parsed_messages = [message.to_openai_dict() for message in messages if message.role != MessageRole.system or include_system]
+    return "\n".join(json.dumps(msg) for msg in parsed_messages)
+
+
+def simple_message_wrapper(openai_msg: dict) -> Message:
+    """Extremely simple way to map from role/content to Message object w/ throwaway dummy fields"""
+
+    if "role" not in openai_msg:
+        raise ValueError(f"Missing role in openai_msg: {openai_msg}")
+    if "content" not in openai_msg:
+        raise ValueError(f"Missing content in openai_msg: {openai_msg}")
+
+    if openai_msg["role"] == "user":
+        return Message(
+            role=MessageRole.user,
+            content=[TextContent(text=openai_msg["content"])],
+        )
+    elif openai_msg["role"] == "assistant":
+        return Message(
+            role=MessageRole.assistant,
+            content=[TextContent(text=openai_msg["content"])],
+        )
+    elif openai_msg["role"] == "system":
+        return Message(
+            role=MessageRole.system,
+            content=[TextContent(text=openai_msg["content"])],
+        )
+    else:
+        raise ValueError(f"Unknown role: {openai_msg['role']}")
+
+
+async def simple_summary(messages: List[Message], llm_config: LLMConfig, actor: User, include_ack: bool = True) -> str:
+    """Generate a simple summary from a list of messages.
+
+    Intentionally kept functional due to the simplicity of the prompt.
+    """
+
+    # Create an LLMClient from the config
+    llm_client = LLMClient.create(
+        provider_type=llm_config.model_endpoint_type,
+        put_inner_thoughts_first=True,
+        actor=actor,
+    )
+    assert llm_client is not None
+
+    # Prepare the messages payload to send to the LLM
+    system_prompt = gpt_summarize.SYSTEM
+    summary_transcript = simple_formatter(messages)
+
+    if include_ack:
+        input_messages = [
+            {"role": "system", "content": system_prompt},
+            {"role": "assistant", "content": MESSAGE_SUMMARY_REQUEST_ACK},
+            {"role": "user", "content": summary_transcript},
+        ]
+    else:
+        input_messages = [
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": summary_transcript},
+        ]
+    print("messages going to summarizer:", input_messages)
+    input_messages_obj = [simple_message_wrapper(msg) for msg in input_messages]
+    print("messages going to summarizer (objs):", input_messages_obj)
+
+    request_data = llm_client.build_request_data(input_messages_obj, llm_config, tools=[])
+    print("request data:", request_data)
+    # NOTE: we should disable the inner_thoughts_in_kwargs here, because we don't use it
+    # I'm leaving it commented it out for now for safety but is fine assuming the var here is a copy not a reference
+    # llm_config.put_inner_thoughts_in_kwargs = False
+    response_data = await llm_client.request_async(request_data, llm_config)
+    response = llm_client.convert_response_to_chat_completion(response_data, input_messages_obj, llm_config)
+    if response.choices[0].message.content is None:
+        logger.warning("No content returned from summarizer")
+        # TODO raise an error error instead?
+        # return "[Summary failed to generate]"
+        raise Exception("Summary failed to generate")
+    else:
+        summary = response.choices[0].message.content.strip()
+
+    return summary
+
+
 def format_transcript(messages: List[Message], include_system: bool = False) -> List[str]:
     """
     Turn a list of Message objects into a human-readable transcript.
diff --git a/letta/services/tool_executor/files_tool_executor.py b/letta/services/tool_executor/files_tool_executor.py
index de8c0a2d..1de53724 100644
--- a/letta/services/tool_executor/files_tool_executor.py
+++ b/letta/services/tool_executor/files_tool_executor.py
@@ -2,8 +2,9 @@ import asyncio
 import re
 from typing import Any, Dict, List, Optional
 
-from letta.constants import MAX_FILES_OPEN
+from letta.constants import MAX_FILES_OPEN, PINECONE_TEXT_FIELD_NAME
 from letta.functions.types import FileOpenRequest
+from letta.helpers.pinecone_utils import search_pinecone_index, should_use_pinecone
 from letta.log import get_logger
 from letta.otel.tracing import trace_method
 from letta.schemas.agent import AgentState
@@ -463,14 +464,15 @@ class LettaFileToolExecutor(ToolExecutor):
         return "\n".join(formatted_results)
 
     @trace_method
-    async def semantic_search_files(self, agent_state: AgentState, query: str, limit: int = 10) -> str:
+    async def semantic_search_files(self, agent_state: AgentState, query: str, limit: int = 5) -> str:
         """
         Search for text within attached files using semantic search and return passages with their source filenames.
+        Uses Pinecone if configured, otherwise falls back to traditional search.
 
         Args:
             agent_state: Current agent state
             query: Search query for semantic matching
-            limit: Maximum number of results to return (default: 10)
+            limit: Maximum number of results to return (default: 5)
 
         Returns:
             Formatted string with search results in IDE/terminal style
@@ -485,6 +487,110 @@ class LettaFileToolExecutor(ToolExecutor):
 
         self.logger.info(f"Semantic search started for agent {agent_state.id} with query '{query}' (limit: {limit})")
 
+        # Check if Pinecone is enabled and use it if available
+        if should_use_pinecone():
+            return await self._search_files_pinecone(agent_state, query, limit)
+        else:
+            return await self._search_files_traditional(agent_state, query, limit)
+
+    async def _search_files_pinecone(self, agent_state: AgentState, query: str, limit: int) -> str:
+        """Search files using Pinecone vector database."""
+
+        # Extract unique source_ids
+        # TODO: Inefficient
+        attached_sources = await self.agent_manager.list_attached_sources_async(agent_id=agent_state.id, actor=self.actor)
+        source_ids = [source.id for source in attached_sources]
+        if not source_ids:
+            return f"No valid source IDs found for attached files"
+
+        # Get all attached files for this agent
+        file_agents = await self.files_agents_manager.list_files_for_agent(agent_id=agent_state.id, actor=self.actor)
+        if not file_agents:
+            return "No files are currently attached to search"
+
+        results = []
+        total_hits = 0
+        files_with_matches = {}
+
+        try:
+            filter = {"source_id": {"$in": source_ids}}
+            search_results = await search_pinecone_index(query, limit, filter, self.actor)
+
+            # Process search results
+            if "result" in search_results and "hits" in search_results["result"]:
+                for hit in search_results["result"]["hits"]:
+                    if total_hits >= limit:
+                        break
+
+                    total_hits += 1
+
+                    # Extract hit information
+                    hit_id = hit.get("_id", "unknown")
+                    score = hit.get("_score", 0.0)
+                    fields = hit.get("fields", {})
+                    text = fields.get(PINECONE_TEXT_FIELD_NAME, "")
+                    file_id = fields.get("file_id", "")
+
+                    # Find corresponding file name
+                    file_name = "Unknown File"
+                    for fa in file_agents:
+                        if fa.file_id == file_id:
+                            file_name = fa.file_name
+                            break
+
+                    # Group by file name
+                    if file_name not in files_with_matches:
+                        files_with_matches[file_name] = []
+                    files_with_matches[file_name].append({"text": text, "score": score, "hit_id": hit_id})
+
+        except Exception as e:
+            self.logger.error(f"Pinecone search failed: {str(e)}")
+            raise e
+
+        if not files_with_matches:
+            return f"No semantic matches found in Pinecone for query: '{query}'"
+
+        # Format results
+        passage_num = 0
+        for file_name, matches in files_with_matches.items():
+            for match in matches:
+                passage_num += 1
+
+                # Format each passage with terminal-style header
+                score_display = f"(score: {match['score']:.3f})"
+                passage_header = f"\n=== {file_name} (passage #{passage_num}) {score_display} ==="
+
+                # Format the passage text
+                passage_text = match["text"].strip()
+                lines = passage_text.splitlines()
+                formatted_lines = []
+                for line in lines[:20]:  # Limit to first 20 lines per passage
+                    formatted_lines.append(f"  {line}")
+
+                if len(lines) > 20:
+                    formatted_lines.append(f"  ... [truncated {len(lines) - 20} more lines]")
+
+                passage_content = "\n".join(formatted_lines)
+                results.append(f"{passage_header}\n{passage_content}")
+
+        # Mark access for files that had matches
+        if files_with_matches:
+            matched_file_names = [name for name in files_with_matches.keys() if name != "Unknown File"]
+            if matched_file_names:
+                await self.files_agents_manager.mark_access_bulk(agent_id=agent_state.id, file_names=matched_file_names, actor=self.actor)
+
+        # Create summary header
+        file_count = len(files_with_matches)
+        summary = f"Found {total_hits} Pinecone matches in {file_count} file{'s' if file_count != 1 else ''} for query: '{query}'"
+
+        # Combine all results
+        formatted_results = [summary, "=" * len(summary)] + results
+
+        self.logger.info(f"Pinecone search completed: {total_hits} matches across {file_count} files")
+        return "\n".join(formatted_results)
+
+    async def _search_files_traditional(self, agent_state: AgentState, query: str, limit: int) -> str:
+        """Traditional search using existing passage manager."""
         # Get semantic search results
         passages = await self.agent_manager.list_source_passages_async(
             actor=self.actor,
diff --git a/letta/services/user_manager.py b/letta/services/user_manager.py
index 02b2c058..0f05e41f 100644
--- a/letta/services/user_manager.py
+++ b/letta/services/user_manager.py
@@ -14,7 +14,6 @@ from letta.otel.tracing import trace_method
 from letta.schemas.user import User as PydanticUser
 from letta.schemas.user import UserUpdate
 from letta.server.db import db_registry
-from letta.settings import settings
 from letta.utils import enforce_types
 
 logger = get_logger(__name__)
diff --git a/letta/settings.py b/letta/settings.py
index 4a0983b1..fb2751c3 100644
--- a/letta/settings.py
+++ b/letta/settings.py
@@ -39,12 +39,17 @@ class ToolSettings(BaseSettings):
 class SummarizerSettings(BaseSettings):
     model_config = SettingsConfigDict(env_prefix="letta_summarizer_", extra="ignore")
 
-    mode: SummarizationMode = SummarizationMode.STATIC_MESSAGE_BUFFER
+    # mode: SummarizationMode = SummarizationMode.STATIC_MESSAGE_BUFFER
+    mode: SummarizationMode = SummarizationMode.PARTIAL_EVICT_MESSAGE_BUFFER
     message_buffer_limit: int = 60
     message_buffer_min: int = 15
     enable_summarization: bool = True
     max_summarization_retries: int = 3
 
+    # partial evict summarizer percentage
+    # eviction based on percentage of message count, not token count
+    partial_evict_summarizer_percentage: float = 0.30
+
     # TODO(cliandy): the below settings are tied to old summarization and should be deprecated or moved
     # Controls if we should evict all messages
     # TODO: Can refactor this into an enum if we have a bunch of different kinds of summarizers
@@ -253,6 +258,13 @@ class Settings(BaseSettings):
     llm_request_timeout_seconds: float = Field(default=60.0, ge=10.0, le=1800.0, description="Timeout for LLM requests in seconds")
     llm_stream_timeout_seconds: float = Field(default=60.0, ge=10.0, le=1800.0, description="Timeout for LLM streaming requests in seconds")
 
+    # For embeddings
+    enable_pinecone: bool = False
+    pinecone_api_key: Optional[str] = None
+    pinecone_source_index: Optional[str] = "sources"
+    pinecone_agent_index: Optional[str] = "recall"
+    upsert_pinecone_indices: bool = False
+
     @property
     def letta_pg_uri(self) -> str:
         if self.pg_uri:
diff --git a/letta/system.py b/letta/system.py
index 33337569..e4031bcc 100644
--- a/letta/system.py
+++ b/letta/system.py
@@ -188,6 +188,22 @@ def package_summarize_message(summary, summary_message_count, hidden_message_cou
     return json_dumps(packaged_message)
 
 
+def package_summarize_message_no_counts(summary, timezone):
+    context_message = (
+        f"Note: prior messages have been hidden from view due to conversation memory constraints.\n"
+        + f"The following is a summary of the previous messages:\n {summary}"
+    )
+
+    formatted_time = get_local_time(timezone=timezone)
+    packaged_message = {
+        "type": "system_alert",
+        "message": context_message,
+        "time": formatted_time,
+    }
+
+    return json_dumps(packaged_message)
+
+
 def package_summarize_message_no_summary(hidden_message_count, message=None, timezone=None):
     """Add useful metadata to the summary message"""
 
diff --git a/poetry.lock b/poetry.lock
index 0d5f4ea2..84c51b99 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -182,6 +182,21 @@ yarl = ">=1.17.0,<2.0"
 [package.extras]
 speedups = ["Brotli ; platform_python_implementation == \"CPython\"", "aiodns (>=3.3.0)", "brotlicffi ; platform_python_implementation != \"CPython\""]
 
+[[package]]
+name = "aiohttp-retry"
+version = "2.9.1"
+description = "Simple retry client for aiohttp"
+optional = false
+python-versions = ">=3.7"
+groups = ["main"]
+files = [
+    {file = "aiohttp_retry-2.9.1-py3-none-any.whl", hash = "sha256:66d2759d1921838256a05a3f80ad7e724936f083e35be5abb5e16eed6be6dc54"},
+    {file = "aiohttp_retry-2.9.1.tar.gz", hash = "sha256:8eb75e904ed4ee5c2ec242fefe85bf04240f685391c4879d8f541d6028ff01f1"},
+]
+
+[package.dependencies]
+aiohttp = "*"
+
 [[package]]
 name = "aioitertools"
 version = "0.12.0"
@@ -217,18 +232,19 @@ docs = ["sphinx (==7.3.7)", "sphinx-mdinclude (==0.6.0)"]
 
 [[package]]
 name = "aiosignal"
-version = "1.3.2"
+version = "1.4.0"
 description = "aiosignal: a list of registered asynchronous callbacks"
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
 files = [
-    {file = "aiosignal-1.3.2-py2.py3-none-any.whl", hash = "sha256:45cde58e409a301715980c2b01d0c28bdde3770d8290b5eb2173759d9acb31a5"},
-    {file = "aiosignal-1.3.2.tar.gz", hash = "sha256:a8c255c66fafb1e499c9351d0bf32ff2d8a0321595ebac3b93713656d2436f54"},
+    {file = "aiosignal-1.4.0-py3-none-any.whl", hash = "sha256:053243f8b92b990551949e63930a839ff0cf0b0ebbe0597b0f3fb19e1a0fe82e"},
+    {file = "aiosignal-1.4.0.tar.gz", hash = "sha256:f47eecd9468083c2029cc99945502cb7708b082c232f9aca65da147157b251c7"},
 ]
 
 [package.dependencies]
 frozenlist = ">=1.1.0"
+typing-extensions = {version = ">=4.2", markers = "python_version < \"3.13\""}
 
 [[package]]
 name = "aiosqlite"
@@ -549,14 +565,14 @@ tomli = {version = ">=2.0.1", markers = "python_version < \"3.11\""}
 
 [[package]]
 name = "banks"
-version = "2.1.2"
+version = "2.1.3"
 description = "A prompt programming language"
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
 files = [
-    {file = "banks-2.1.2-py3-none-any.whl", hash = "sha256:7fba451069f6bea376483b8136a0f29cb1e6883133626d00e077e20a3d102c0e"},
-    {file = "banks-2.1.2.tar.gz", hash = "sha256:a0651db9d14b57fa2e115e78f68dbb1b36fe226ad6eef96192542908b1d20c1f"},
+    {file = "banks-2.1.3-py3-none-any.whl", hash = "sha256:9e1217dc977e6dd1ce42c5ff48e9bcaf238d788c81b42deb6a555615ffcffbab"},
+    {file = "banks-2.1.3.tar.gz", hash = "sha256:c0dd2cb0c5487274a513a552827e6a8ddbd0ab1a1b967f177e71a6e4748a3ed2"},
 ]
 
 [package.dependencies]
@@ -928,7 +944,7 @@ version = "2025.6.15"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
 python-versions = ">=3.7"
-groups = ["main"]
+groups = ["main", "dev"]
 files = [
     {file = "certifi-2025.6.15-py3-none-any.whl", hash = "sha256:2e0c7ce7cb5d8f8634ca55d2ba7e6ec2689a2fd6537d8dec1296a477a4910057"},
     {file = "certifi-2025.6.15.tar.gz", hash = "sha256:d747aa5a8b9bbbb1bb8c22bb13e22bd1f18e9796defa16bab421f7f7a317323b"},
@@ -1034,7 +1050,7 @@ version = "3.4.2"
 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
 optional = false
 python-versions = ">=3.7"
-groups = ["main"]
+groups = ["main", "dev"]
 files = [
     {file = "charset_normalizer-3.4.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:7c48ed483eb946e6c04ccbe02c6b4d1d48e51944b6db70f697e089c193404941"},
     {file = "charset_normalizer-3.4.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b2d318c11350e10662026ad0eb71bb51c7812fc8590825304ae0bdd4ac283acd"},
@@ -1177,14 +1193,14 @@ test = ["pytest"]
 
 [[package]]
 name = "composio-core"
-version = "0.7.19"
+version = "0.7.20"
 description = "Core package to act as a bridge between composio platform and other services."
 optional = false
 python-versions = "<4,>=3.9"
 groups = ["main"]
 files = [
-    {file = "composio_core-0.7.19-py3-none-any.whl", hash = "sha256:7170bc801eda09b153fde2276ba2a44fb164c74ba5d40a727b826c033c490fb3"},
-    {file = "composio_core-0.7.19.tar.gz", hash = "sha256:e20480ceb8afea6d94d02e76c4c4bb489e3aca48dc8bc0d9d50c834e20a96d84"},
+    {file = "composio_core-0.7.20-py3-none-any.whl", hash = "sha256:e1cfb9cfc68a4622bc15827143ddf726f429d281e8f9de5d4c0965e75d039f14"},
+    {file = "composio_core-0.7.20.tar.gz", hash = "sha256:1dc29dbf73eb72d2df1c5b0d4d2f21459d15029322cf74df8fdecc44dcaeb1f4"},
 ]
 
 [package.dependencies]
@@ -1310,49 +1326,49 @@ test-no-images = ["pytest", "pytest-cov", "pytest-rerunfailures", "pytest-xdist"
 
 [[package]]
 name = "cryptography"
-version = "45.0.4"
+version = "45.0.5"
 description = "cryptography is a package which provides cryptographic recipes and primitives to Python developers."
 optional = false
 python-versions = "!=3.9.0,!=3.9.1,>=3.7"
 groups = ["main"]
 files = [
-    {file = "cryptography-45.0.4-cp311-abi3-macosx_10_9_universal2.whl", hash = "sha256:425a9a6ac2823ee6e46a76a21a4e8342d8fa5c01e08b823c1f19a8b74f096069"},
-    {file = "cryptography-45.0.4-cp311-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:680806cf63baa0039b920f4976f5f31b10e772de42f16310a6839d9f21a26b0d"},
-    {file = "cryptography-45.0.4-cp311-abi3-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:4ca0f52170e821bc8da6fc0cc565b7bb8ff8d90d36b5e9fdd68e8a86bdf72036"},
-    {file = "cryptography-45.0.4-cp311-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:f3fe7a5ae34d5a414957cc7f457e2b92076e72938423ac64d215722f6cf49a9e"},
-    {file = "cryptography-45.0.4-cp311-abi3-manylinux_2_28_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:25eb4d4d3e54595dc8adebc6bbd5623588991d86591a78c2548ffb64797341e2"},
-    {file = "cryptography-45.0.4-cp311-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:ce1678a2ccbe696cf3af15a75bb72ee008d7ff183c9228592ede9db467e64f1b"},
-    {file = "cryptography-45.0.4-cp311-abi3-manylinux_2_34_aarch64.whl", hash = "sha256:49fe9155ab32721b9122975e168a6760d8ce4cffe423bcd7ca269ba41b5dfac1"},
-    {file = "cryptography-45.0.4-cp311-abi3-manylinux_2_34_x86_64.whl", hash = "sha256:2882338b2a6e0bd337052e8b9007ced85c637da19ef9ecaf437744495c8c2999"},
-    {file = "cryptography-45.0.4-cp311-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:23b9c3ea30c3ed4db59e7b9619272e94891f8a3a5591d0b656a7582631ccf750"},
-    {file = "cryptography-45.0.4-cp311-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:b0a97c927497e3bc36b33987abb99bf17a9a175a19af38a892dc4bbb844d7ee2"},
-    {file = "cryptography-45.0.4-cp311-abi3-win32.whl", hash = "sha256:e00a6c10a5c53979d6242f123c0a97cff9f3abed7f064fc412c36dc521b5f257"},
-    {file = "cryptography-45.0.4-cp311-abi3-win_amd64.whl", hash = "sha256:817ee05c6c9f7a69a16200f0c90ab26d23a87701e2a284bd15156783e46dbcc8"},
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 test-randomorder = ["pytest-randomly"]
 
 [[package]]
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 [package.dependencies]
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 python-versions = ">=3.9"
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 ]
 
 [package.dependencies]
@@ -2432,7 +2466,8 @@ uvloop = {version = ">=0.18.0", optional = true, markers = "platform_python_impl
 watchfiles = {version = ">=1.0,<2.0", optional = true, markers = "extra == \"reload\""}
 
 [package.extras]
-all = ["granian[pname,reload]"]
+all = ["granian[dotenv,pname,reload]"]
+dotenv = ["python-dotenv (>=1.1,<2.0)"]
 pname = ["setproctitle (>=1.3.3,<1.4.0)"]
 reload = ["watchfiles (>=1.0,<2.0)"]
 rloop = ["rloop (>=0.1,<1.0) ; sys_platform != \"win32\""]
@@ -2445,7 +2480,7 @@ description = "Lightweight in-process concurrent programming"
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
-markers = "platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\" or (extra == \"dev\" or extra == \"desktop\" or extra == \"all\") and platform_python_implementation == \"CPython\""
+markers = "platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\" or platform_python_implementation == \"CPython\" and (extra == \"dev\" or extra == \"desktop\" or extra == \"all\")"
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@@ -2524,131 +2559,131 @@ colorama = ">=0.4"
 
 [[package]]
 name = "grpcio"
-version = "1.73.0"
+version = "1.73.1"
 description = "HTTP/2-based RPC framework"
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
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+    {file = "grpcio_tools-1.71.2-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:b3312bdd5952bba2ef8e4314b2e2f886fa23b2f6d605cd56097605ae65d30515"},
+    {file = "grpcio_tools-1.71.2-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:085de63843946b967ae561e7dd832fa03147f01282f462a0a0cbe1571d9ee986"},
+    {file = "grpcio_tools-1.71.2-cp39-cp39-win32.whl", hash = "sha256:c1ff5f79f49768d4c561508b62878f27198b3420a87390e0c51969b8dbfcfca8"},
+    {file = "grpcio_tools-1.71.2-cp39-cp39-win_amd64.whl", hash = "sha256:c3e02b345cf96673dcf77599a61482f68c318a62c9cde20a5ae0882619ff8c98"},
+    {file = "grpcio_tools-1.71.2.tar.gz", hash = "sha256:b5304d65c7569b21270b568e404a5a843cf027c66552a6a0978b23f137679c09"},
 ]
 
 [package.dependencies]
-grpcio = ">=1.71.0"
+grpcio = ">=1.71.2"
 protobuf = ">=5.26.1,<6.0dev"
 setuptools = "*"
 
@@ -2757,7 +2792,7 @@ version = "3.10"
 description = "Internationalized Domain Names in Applications (IDNA)"
 optional = false
 python-versions = ">=3.6"
-groups = ["main"]
+groups = ["main", "dev"]
 files = [
     {file = "idna-3.10-py3-none-any.whl", hash = "sha256:946d195a0d259cbba61165e88e65941f16e9b36ea6ddb97f00452bae8b1287d3"},
     {file = "idna-3.10.tar.gz", hash = "sha256:12f65c9b470abda6dc35cf8e63cc574b1c52b11df2c86030af0ac09b01b13ea9"},
@@ -2844,8 +2879,8 @@ files = [
 ]
 
 [package.dependencies]
-decorator = {version = "*", markers = "python_version >= \"3.11\""}
-ipython = {version = ">=7.31.1", markers = "python_version >= \"3.11\""}
+decorator = {version = "*", markers = "python_version > \"3.6\""}
+ipython = {version = ">=7.31.1", markers = "python_version > \"3.6\""}
 tomli = {version = "*", markers = "python_version > \"3.6\" and python_version < \"3.11\""}
 
 [[package]]
@@ -2924,14 +2959,14 @@ test-extra = ["curio", "ipython[test]", "jupyter_ai", "matplotlib (!=3.2.0)", "n
 
 [[package]]
 name = "ipython"
-version = "9.3.0"
+version = "9.4.0"
 description = "IPython: Productive Interactive Computing"
 optional = false
 python-versions = ">=3.11"
 groups = ["main", "dev"]
 files = [
-    {file = "ipython-9.3.0-py3-none-any.whl", hash = "sha256:1a0b6dd9221a1f5dddf725b57ac0cb6fddc7b5f470576231ae9162b9b3455a04"},
-    {file = "ipython-9.3.0.tar.gz", hash = "sha256:79eb896f9f23f50ad16c3bc205f686f6e030ad246cc309c6279a242b14afe9d8"},
+    {file = "ipython-9.4.0-py3-none-any.whl", hash = "sha256:25850f025a446d9b359e8d296ba175a36aedd32e83ca9b5060430fe16801f066"},
+    {file = "ipython-9.4.0.tar.gz", hash = "sha256:c033c6d4e7914c3d9768aabe76bbe87ba1dc66a92a05db6bfa1125d81f2ee270"},
 ]
 markers = {main = "(extra == \"dev\" or extra == \"all\") and python_version >= \"3.11\"", dev = "python_version >= \"3.11\""}
 
@@ -3434,15 +3469,15 @@ tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<10"
 
 [[package]]
 name = "langchain-community"
-version = "0.3.26"
+version = "0.3.27"
 description = "Community contributed LangChain integrations."
 optional = true
 python-versions = ">=3.9"
 groups = ["main"]
-markers = "(extra == \"external-tools\" or extra == \"desktop\" or extra == \"all\") and python_version < \"3.13\""
+markers = "(extra == \"external-tools\" or extra == \"desktop\" or extra == \"all\") and python_version <= \"3.12\""
 files = [
-    {file = "langchain_community-0.3.26-py3-none-any.whl", hash = "sha256:b25a553ee9d44a6c02092a440da6c561a9312c7013ffc25365ac3f8694edb53a"},
-    {file = "langchain_community-0.3.26.tar.gz", hash = "sha256:49f9d71dc20bc42ccecd6875d02fafef1be0e211a0b22cecbd678f5fd3719487"},
+    {file = "langchain_community-0.3.27-py3-none-any.whl", hash = "sha256:581f97b795f9633da738ea95da9cb78f8879b538090c9b7a68c0aed49c828f0d"},
+    {file = "langchain_community-0.3.27.tar.gz", hash = "sha256:e1037c3b9da0c6d10bf06e838b034eb741e016515c79ef8f3f16e53ead33d882"},
 ]
 
 [package.dependencies]
@@ -3461,15 +3496,15 @@ tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<10"
 
 [[package]]
 name = "langchain-core"
-version = "0.3.66"
+version = "0.3.68"
 description = "Building applications with LLMs through composability"
 optional = true
 python-versions = ">=3.9"
 groups = ["main"]
 markers = "extra == \"external-tools\" or extra == \"desktop\" or extra == \"all\""
 files = [
-    {file = "langchain_core-0.3.66-py3-none-any.whl", hash = "sha256:65cd6c3659afa4f91de7aa681397a0c53ff9282425c281e53646dd7faf16099e"},
-    {file = "langchain_core-0.3.66.tar.gz", hash = "sha256:350c92e792ec1401f4b740d759b95f297710a50de29e1be9fbfff8676ef62117"},
+    {file = "langchain_core-0.3.68-py3-none-any.whl", hash = "sha256:5e5c1fbef419590537c91b8c2d86af896fbcbaf0d5ed7fdcdd77f7d8f3467ba0"},
+    {file = "langchain_core-0.3.68.tar.gz", hash = "sha256:312e1932ac9aa2eaf111b70fdc171776fa571d1a86c1f873dcac88a094b19c6f"},
 ]
 
 [package.dependencies]
@@ -3527,25 +3562,22 @@ pytest = ["pytest (>=7.0.0)", "rich (>=13.9.4,<14.0.0)"]
 
 [[package]]
 name = "langsmith"
-version = "0.4.1"
+version = "0.4.4"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = true
 python-versions = ">=3.9"
 groups = ["main"]
-markers = "(extra == \"external-tools\" or extra == \"desktop\" or extra == \"all\") and python_version < \"3.13\""
+markers = "(extra == \"external-tools\" or extra == \"desktop\" or extra == \"all\") and python_version <= \"3.12\""
 files = [
-    {file = "langsmith-0.4.1-py3-none-any.whl", hash = "sha256:19c4c40bbb6735cb1136c453b2edcde265ca5ba1b108b7e0e3583ec4bda28625"},
-    {file = "langsmith-0.4.1.tar.gz", hash = "sha256:ae8ec403fb2b9cabcfc3b0c54556d65555598c85879dac83b009576927f7eb1d"},
+    {file = "langsmith-0.4.4-py3-none-any.whl", hash = "sha256:014c68329bd085bd6c770a6405c61bb6881f82eb554ce8c4d1984b0035fd1716"},
+    {file = "langsmith-0.4.4.tar.gz", hash = "sha256:70c53bbff24a7872e88e6fa0af98270f4986a6e364f9e85db1cc5636defa4d66"},
 ]
 
 [package.dependencies]
 httpx = ">=0.23.0,<1"
 orjson = {version = ">=3.9.14,<4.0.0", markers = "platform_python_implementation != \"PyPy\""}
 packaging = ">=23.2"
-pydantic = [
-    {version = ">=1,<3", markers = "python_full_version < \"3.12.4\""},
-    {version = ">=2.7.4,<3.0.0", markers = "python_full_version >= \"3.12.4\""},
-]
+pydantic = ">=1,<3"
 requests = ">=2,<3"
 requests-toolbelt = ">=1.0.0,<2.0.0"
 zstandard = ">=0.23.0,<0.24.0"
@@ -3558,14 +3590,14 @@ pytest = ["pytest (>=7.0.0)", "rich (>=13.9.4,<14.0.0)"]
 
 [[package]]
 name = "letta-client"
-version = "0.1.183"
+version = "0.1.191"
 description = ""
 optional = false
 python-versions = "<4.0,>=3.8"
 groups = ["main"]
 files = [
-    {file = "letta_client-0.1.183-py3-none-any.whl", hash = "sha256:72d7d53ae3093a6d79fcefd24d079a135be806b9ff1f77b1501d7966a14222dd"},
-    {file = "letta_client-0.1.183.tar.gz", hash = "sha256:f89026e96ffd0c24fec9cedb5f76895fdb8a5414d440f4f66c7e427f9a37e110"},
+    {file = "letta_client-0.1.191-py3-none-any.whl", hash = "sha256:2cc234668784b022a25aeab4db48b944a0a188e42112870efd8b028ad223347b"},
+    {file = "letta_client-0.1.191.tar.gz", hash = "sha256:95957695e679183ec0d87673c8dd169a83f9807359c4740d42ed84fbb6b05efc"},
 ]
 
 [package.dependencies]
@@ -3577,14 +3609,14 @@ typing_extensions = ">=4.0.0"
 
 [[package]]
 name = "llama-cloud"
-version = "0.1.26"
+version = "0.1.30"
 description = ""
 optional = false
 python-versions = "<4,>=3.8"
 groups = ["main"]
 files = [
-    {file = "llama_cloud-0.1.26-py3-none-any.whl", hash = "sha256:2c0b2663e619b71c0645885ef622d6443725ab37bdc6ae5fb723e097f3af9459"},
-    {file = "llama_cloud-0.1.26.tar.gz", hash = "sha256:b307f91b1ad97189b5278119ac4ad665931b65f240fb643b3e384d0a1fc81f56"},
+    {file = "llama_cloud-0.1.30-py3-none-any.whl", hash = "sha256:a51e862303d44386e0822747d76d31100ef21bbfdb4670f7e37cee7fd975b69e"},
+    {file = "llama_cloud-0.1.30.tar.gz", hash = "sha256:c859965d6fd29242ad644335a0c7c8bdbe1fb62d1f01bef0bf372e877120ddad"},
 ]
 
 [package.dependencies]
@@ -3594,23 +3626,24 @@ pydantic = ">=1.10"
 
 [[package]]
 name = "llama-cloud-services"
-version = "0.6.34"
+version = "0.6.41"
 description = "Tailored SDK clients for LlamaCloud services."
 optional = false
 python-versions = "<4.0,>=3.9"
 groups = ["main"]
 files = [
-    {file = "llama_cloud_services-0.6.34-py3-none-any.whl", hash = "sha256:02df81b03ed0286422a1cce0c650a3fe0eba385c88b12ac690a23153aab55432"},
-    {file = "llama_cloud_services-0.6.34.tar.gz", hash = "sha256:b56884ee0cc2298fd625b6b6d9f161e09340cb962cdb04006770ecf097e925ae"},
+    {file = "llama_cloud_services-0.6.41-py3-none-any.whl", hash = "sha256:730b92a44bb87d4dfe6ace844cd4e3ec9e0c010fee627d1dadba96abe540c005"},
+    {file = "llama_cloud_services-0.6.41.tar.gz", hash = "sha256:e80599d5ec8afe6077b9256748b77d21379108f310ffe51f1c21bd8b143ee3be"},
 ]
 
 [package.dependencies]
 click = ">=8.1.7,<9.0.0"
-llama-cloud = "0.1.26"
+llama-cloud = "0.1.30"
 llama-index-core = ">=0.12.0"
 platformdirs = ">=4.3.7,<5.0.0"
 pydantic = ">=2.8,<2.10 || >2.10"
 python-dotenv = ">=1.0.1,<2.0.0"
+tenacity = ">=8.5.0,<10.0"
 
 [[package]]
 name = "llama-index"
@@ -3640,14 +3673,14 @@ nltk = ">3.8.1"
 
 [[package]]
 name = "llama-index-agent-openai"
-version = "0.4.11"
+version = "0.4.12"
 description = "llama-index agent openai integration"
 optional = false
 python-versions = "<4.0,>=3.9"
 groups = ["main"]
 files = [
-    {file = "llama_index_agent_openai-0.4.11-py3-none-any.whl", hash = "sha256:957bae65a072bde8b6c4c49fbd6e1cb47c182f55fa8fd2d748eb008176c22fe0"},
-    {file = "llama_index_agent_openai-0.4.11.tar.gz", hash = "sha256:bcd25f34412d1700f19a691c5f47db48f438f72a09f7467a20cffc6d3fdc326c"},
+    {file = "llama_index_agent_openai-0.4.12-py3-none-any.whl", hash = "sha256:6dbb6276b2e5330032a726b28d5eef5140825f36d72d472b231f08ad3af99665"},
+    {file = "llama_index_agent_openai-0.4.12.tar.gz", hash = "sha256:d2fe53feb69cfe45752edb7328bf0d25f6a9071b3c056787e661b93e5b748a28"},
 ]
 
 [package.dependencies]
@@ -3728,18 +3761,18 @@ openai = ">=1.1.0"
 
 [[package]]
 name = "llama-index-indices-managed-llama-cloud"
-version = "0.7.7"
+version = "0.7.8"
 description = "llama-index indices llama-cloud integration"
 optional = false
 python-versions = "<4.0,>=3.9"
 groups = ["main"]
 files = [
-    {file = "llama_index_indices_managed_llama_cloud-0.7.7-py3-none-any.whl", hash = "sha256:5a8a24de6a2f425a5616818582b57f603e4192179443045f636e865efc6692c4"},
-    {file = "llama_index_indices_managed_llama_cloud-0.7.7.tar.gz", hash = "sha256:7fd730a2967dc20eb422ae2fa4028b4cf7023114a5cfadb5232355137754186f"},
+    {file = "llama_index_indices_managed_llama_cloud-0.7.8-py3-none-any.whl", hash = "sha256:7eff6bb44a9aa3e9d2e98039178c31d9de649fddc82acc05e81f24199a8071a2"},
+    {file = "llama_index_indices_managed_llama_cloud-0.7.8.tar.gz", hash = "sha256:a3f421e2c82c4c641995da72f939b0ae83409d62bb0f7c7a8db24c5f59a75e49"},
 ]
 
 [package.dependencies]
-llama-cloud = "0.1.26"
+llama-cloud = "0.1.30"
 llama-index-core = ">=0.12.0,<0.13"
 
 [[package]]
@@ -3848,18 +3881,18 @@ llama-parse = ">=0.5.0"
 
 [[package]]
 name = "llama-parse"
-version = "0.6.34"
+version = "0.6.41"
 description = "Parse files into RAG-Optimized formats."
 optional = false
 python-versions = "<4.0,>=3.9"
 groups = ["main"]
 files = [
-    {file = "llama_parse-0.6.34-py3-none-any.whl", hash = "sha256:395174c3c4d22dc372ab310727de2b3fc4e268e36f11fe9959a95bfa9cf63d66"},
-    {file = "llama_parse-0.6.34.tar.gz", hash = "sha256:a228619806687ff7b3fb44f21210e98c337f7e05f19374f0fb52196158caae0d"},
+    {file = "llama_parse-0.6.41-py3-none-any.whl", hash = "sha256:d1a6e0fbc27ca21d1986dc4346829b8aa087b4d3054fb51c548c72bda3762491"},
+    {file = "llama_parse-0.6.41.tar.gz", hash = "sha256:ccb5fc3d043b89ab925726b881c0e95805e2f441455e8226030e080509501db3"},
 ]
 
 [package.dependencies]
-llama-cloud-services = ">=0.6.32"
+llama-cloud-services = ">=0.6.41"
 
 [[package]]
 name = "locust"
@@ -3887,8 +3920,8 @@ psutil = ">=5.9.1"
 pywin32 = {version = "*", markers = "sys_platform == \"win32\""}
 pyzmq = ">=25.0.0"
 requests = [
-    {version = ">=2.32.2", markers = "python_version > \"3.11\""},
     {version = ">=2.26.0", markers = "python_version <= \"3.11\""},
+    {version = ">=2.32.2", markers = "python_version > \"3.11\""},
 ]
 setuptools = ">=70.0.0"
 tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
@@ -4149,20 +4182,21 @@ traitlets = "*"
 
 [[package]]
 name = "mcp"
-version = "1.9.4"
+version = "1.10.1"
 description = "Model Context Protocol SDK"
 optional = false
 python-versions = ">=3.10"
 groups = ["main"]
 files = [
-    {file = "mcp-1.9.4-py3-none-any.whl", hash = "sha256:7fcf36b62936adb8e63f89346bccca1268eeca9bf6dfb562ee10b1dfbda9dac0"},
-    {file = "mcp-1.9.4.tar.gz", hash = "sha256:cfb0bcd1a9535b42edaef89947b9e18a8feb49362e1cc059d6e7fc636f2cb09f"},
+    {file = "mcp-1.10.1-py3-none-any.whl", hash = "sha256:4d08301aefe906dce0fa482289db55ce1db831e3e67212e65b5e23ad8454b3c5"},
+    {file = "mcp-1.10.1.tar.gz", hash = "sha256:aaa0957d8307feeff180da2d9d359f2b801f35c0c67f1882136239055ef034c2"},
 ]
 
 [package.dependencies]
 anyio = ">=4.5"
 httpx = ">=0.27"
 httpx-sse = ">=0.4"
+jsonschema = ">=4.20.0"
 pydantic = ">=2.7.2,<3.0.0"
 pydantic-settings = ">=2.5.2"
 python-dotenv = {version = ">=1.0.0", optional = true, markers = "extra == \"cli\""}
@@ -4191,14 +4225,14 @@ files = [
 
 [[package]]
 name = "mistralai"
-version = "1.8.2"
+version = "1.9.1"
 description = "Python Client SDK for the Mistral AI API."
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
 files = [
-    {file = "mistralai-1.8.2-py3-none-any.whl", hash = "sha256:d7f2c3c9d02475c1f1911cff2458bd01e91bbe8e15bfb57cb7ac397a9440ef8e"},
-    {file = "mistralai-1.8.2.tar.gz", hash = "sha256:3a2fdf35498dd71cca3ee065adf8d75331f3bc6bbfbc7ffdd20dc82ae01d9d6d"},
+    {file = "mistralai-1.9.1-py3-none-any.whl", hash = "sha256:250ec26534db6f4a4d5e6292b0801a64da2ab1f0d4c63a20d8ce27e3a427e402"},
+    {file = "mistralai-1.9.1.tar.gz", hash = "sha256:89eb1d48e9555c8289c02ddea966115eba0516355731726ea0a24eabb42f8419"},
 ]
 
 [package.dependencies]
@@ -4284,110 +4318,122 @@ files = [
 
 [[package]]
 name = "multidict"
-version = "6.5.1"
+version = "6.6.3"
 description = "multidict implementation"
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
 files = [
-    {file = "multidict-6.5.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:7b7d75cb5b90fa55700edbbdca12cd31f6b19c919e98712933c7a1c3c6c71b73"},
-    {file = "multidict-6.5.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:ad32e43e028276612bf5bab762677e7d131d2df00106b53de2efb2b8a28d5bce"},
-    {file = "multidict-6.5.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:0499cbc67c1b02ba333781798560c5b1e7cd03e9273b678c92c6de1b1657fac9"},
-    {file = "multidict-6.5.1-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3c78fc6bc1dd7a139dab7ee9046f79a2082dce9360e3899b762615d564e2e857"},
-    {file = "multidict-6.5.1-cp310-cp310-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:f369d6619b24da4df4a02455fea8641fe8324fc0100a3e0dcebc5bf55fa903f3"},
-    {file = "multidict-6.5.1-cp310-cp310-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:719af50a44ce9cf9ab15d829bf8cf146de486b4816284c17c3c9b9c9735abb8f"},
-    {file = "multidict-6.5.1-cp310-cp310-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:199a0a9b3de8bbeb6881460d32b857dc7abec94448aeb6d607c336628c53580a"},
-    {file = "multidict-6.5.1-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:fe09318a28b00c6f43180d0d889df1535e98fb2d93d25955d46945f8d5410d87"},
-    {file = "multidict-6.5.1-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:ab94923ae54385ed480e4ab19f10269ee60f3eabd0b35e2a5d1ba6dbf3b0cc27"},
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 [package.dependencies]
@@ -4547,14 +4593,14 @@ files = [
 
 [[package]]
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-version = "1.91.0"
+version = "1.93.0"
 description = "The official Python library for the openai API"
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
 files = [
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 [package.dependencies]
@@ -4910,9 +4956,9 @@ files = [
 
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     {version = ">=1.23.2", markers = "python_version == \"3.11\""},
     {version = ">=1.22.4", markers = "python_version < \"3.11\""},
+    {version = ">=1.26.0", markers = "python_version >= \"3.12\""},
 ]
 python-dateutil = ">=2.8.2"
 pytz = ">=2020.1"
@@ -5157,6 +5203,63 @@ tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "pa
 typing = ["typing-extensions ; python_version < \"3.10\""]
 xmp = ["defusedxml"]
 
+[[package]]
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+version = "7.3.0"
+description = "Pinecone client and SDK"
+optional = false
+python-versions = "<4.0,>=3.9"
+groups = ["main", "dev"]
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+certifi = ">=2019.11.17"
+pinecone-plugin-assistant = ">=1.6.0,<2.0.0"
+pinecone-plugin-interface = ">=0.0.7,<0.0.8"
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+typing-extensions = ">=3.7.4"
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+
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+
+[[package]]
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 [[package]]
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 version = "4.3.8"
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 [[package]]
 name = "pypdf"
-version = "5.6.1"
+version = "5.7.0"
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@@ -6475,7 +6578,7 @@ version = "2.32.4"
 description = "Python HTTP for Humans."
 optional = false
 python-versions = ">=3.8"
-groups = ["main"]
+groups = ["main", "dev"]
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+    {file = "rpds_py-0.26.0.tar.gz", hash = "sha256:20dae58a859b0906f0685642e591056f1e787f3a8b39c8e8749a45dc7d26bdb0"},
 ]
 
 [[package]]
@@ -6691,15 +6821,15 @@ crt = ["botocore[crt] (>=1.36.0,<2.0a.0)"]
 
 [[package]]
 name = "scramp"
-version = "1.4.5"
+version = "1.4.6"
 description = "An implementation of the SCRAM protocol."
 optional = true
 python-versions = ">=3.8"
 groups = ["main"]
 markers = "extra == \"postgres\" or extra == \"desktop\" or extra == \"all\""
 files = [
-    {file = "scramp-1.4.5-py3-none-any.whl", hash = "sha256:50e37c464fc67f37994e35bee4151e3d8f9320e9c204fca83a5d313c121bbbe7"},
-    {file = "scramp-1.4.5.tar.gz", hash = "sha256:be3fbe774ca577a7a658117dca014e5d254d158cecae3dd60332dfe33ce6d78e"},
+    {file = "scramp-1.4.6-py3-none-any.whl", hash = "sha256:a0cf9d2b4624b69bac5432dd69fecfc55a542384fe73c3a23ed9b138cda484e1"},
+    {file = "scramp-1.4.6.tar.gz", hash = "sha256:fe055ebbebf4397b9cb323fcc4b299f219cd1b03fd673ca40c97db04ac7d107e"},
 ]
 
 [package.dependencies]
@@ -7018,14 +7148,14 @@ SQLAlchemy = ">=2.0.0,<2.1.0"
 
 [[package]]
 name = "sse-starlette"
-version = "2.3.6"
+version = "2.4.1"
 description = "SSE plugin for Starlette"
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
 files = [
-    {file = "sse_starlette-2.3.6-py3-none-any.whl", hash = "sha256:d49a8285b182f6e2228e2609c350398b2ca2c36216c2675d875f81e93548f760"},
-    {file = "sse_starlette-2.3.6.tar.gz", hash = "sha256:0382336f7d4ec30160cf9ca0518962905e1b69b72d6c1c995131e0a703b436e3"},
+    {file = "sse_starlette-2.4.1-py3-none-any.whl", hash = "sha256:08b77ea898ab1a13a428b2b6f73cfe6d0e607a7b4e15b9bb23e4a37b087fd39a"},
+    {file = "sse_starlette-2.4.1.tar.gz", hash = "sha256:7c8a800a1ca343e9165fc06bbda45c78e4c6166320707ae30b416c42da070926"},
 ]
 
 [package.dependencies]
@@ -7105,14 +7235,14 @@ typing-extensions = {version = "*", markers = "python_version < \"3.11\""}
 
 [[package]]
 name = "tavily-python"
-version = "0.7.8"
+version = "0.7.9"
 description = "Python wrapper for the Tavily API"
 optional = false
 python-versions = ">=3.6"
 groups = ["main"]
 files = [
-    {file = "tavily_python-0.7.8-py3-none-any.whl", hash = "sha256:d6beccb7f588875001cf4a3041ac263d188af6fd144bf5c93eaebb1a5bd4eb95"},
-    {file = "tavily_python-0.7.8.tar.gz", hash = "sha256:720a62abc2e5812cee8ae0b05e3ad23df71042fec392deb7f8673881b4ba3151"},
+    {file = "tavily_python-0.7.9-py3-none-any.whl", hash = "sha256:6d70ea86e2ccba061d0ea98c81922784a01c186960304d44436304f114f22372"},
+    {file = "tavily_python-0.7.9.tar.gz", hash = "sha256:61aa13ca89e2e40d645042c8d27afc478b27846fb79bb21d4f683ed28f173dc7"},
 ]
 
 [package.dependencies]
@@ -7334,16 +7464,16 @@ typing-extensions = ">=3.7.4.3"
 
 [[package]]
 name = "typing-extensions"
-version = "4.14.0"
+version = "4.14.1"
 description = "Backported and Experimental Type Hints for Python 3.9+"
 optional = false
 python-versions = ">=3.9"
 groups = ["main", "dev", "dev,tests"]
 files = [
-    {file = "typing_extensions-4.14.0-py3-none-any.whl", hash = "sha256:a1514509136dd0b477638fc68d6a91497af5076466ad0fa6c338e44e359944af"},
-    {file = "typing_extensions-4.14.0.tar.gz", hash = "sha256:8676b788e32f02ab42d9e7c61324048ae4c6d844a399eebace3d4979d75ceef4"},
+    {file = "typing_extensions-4.14.1-py3-none-any.whl", hash = "sha256:d1e1e3b58374dc93031d6eda2420a48ea44a36c2b4766a4fdeb3710755731d76"},
+    {file = "typing_extensions-4.14.1.tar.gz", hash = "sha256:38b39f4aeeab64884ce9f74c94263ef78f3c22467c8724005483154c26648d36"},
 ]
-markers = {dev = "python_version < \"3.12\"", "dev,tests" = "python_version == \"3.10\""}
+markers = {"dev,tests" = "python_version == \"3.10\""}
 
 [[package]]
 name = "typing-inspect"
@@ -7412,7 +7542,7 @@ version = "2.5.0"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 optional = false
 python-versions = ">=3.9"
-groups = ["main"]
+groups = ["main", "dev"]
 files = [
     {file = "urllib3-2.5.0-py3-none-any.whl", hash = "sha256:e6b01673c0fa6a13e374b50871808eb3bf7046c4b125b216f6bf1cc604cff0dc"},
     {file = "urllib3-2.5.0.tar.gz", hash = "sha256:3fc47733c7e419d4bc3f6b3dc2b4f890bb743906a30d56ba4a5bfa4bbff92760"},
@@ -8029,15 +8159,15 @@ type = ["pytest-mypy"]
 
 [[package]]
 name = "zope-event"
-version = "5.0"
+version = "5.1"
 description = "Very basic event publishing system"
 optional = true
-python-versions = ">=3.7"
+python-versions = ">=3.9"
 groups = ["main"]
 markers = "extra == \"dev\" or extra == \"desktop\" or extra == \"all\""
 files = [
-    {file = "zope.event-5.0-py3-none-any.whl", hash = "sha256:2832e95014f4db26c47a13fdaef84cef2f4df37e66b59d8f1f4a8f319a632c26"},
-    {file = "zope.event-5.0.tar.gz", hash = "sha256:bac440d8d9891b4068e2b5a2c5e2c9765a9df762944bda6955f96bb9b91e67cd"},
+    {file = "zope_event-5.1-py3-none-any.whl", hash = "sha256:53de8f0e9f61dc0598141ac591f49b042b6d74784dab49971b9cc91d0f73a7df"},
+    {file = "zope_event-5.1.tar.gz", hash = "sha256:a153660e0c228124655748e990396b9d8295d6e4f546fa1b34f3319e1c666e7f"},
 ]
 
 [package.dependencies]
@@ -8234,4 +8364,4 @@ tests = ["wikipedia"]
 [metadata]
 lock-version = "2.1"
 python-versions = "<3.14,>=3.10"
-content-hash = "8c336b606dc17dac1bcb10c63a889f18437ca4fb3938de97786cc9bf1c1f284c"
+content-hash = "b2f23b566c52a2ecb9d656383d00f8ac64293690e0f6fcc0d354ed2d2ad8807b"
diff --git a/pyproject.toml b/pyproject.toml
index 6357b07a..00386f38 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "letta"
-version = "0.8.9"
+version = "0.8.10"
 packages = [
     {include = "letta"},
 ]
@@ -98,6 +98,7 @@ redis = {version = "^6.2.0", optional = true}
 structlog = "^25.4.0"
 certifi = "^2025.6.15"
 aioboto3 = {version = "^14.3.0", optional = true}
+pinecone = {extras = ["asyncio"], version = "^7.3.0"}
 aiosqlite = "^0.21.0"
 
 
@@ -121,6 +122,7 @@ black = "^24.4.2"
 ipykernel = "^6.29.5"
 ipdb = "^0.13.13"
 pytest-mock = "^3.14.0"
+pinecone = "^7.3.0"
 
 
 [tool.poetry.group."dev,tests".dependencies]
diff --git a/tests/configs/llm_model_configs/openai-gpt-4o-mini.json b/tests/configs/llm_model_configs/openai-gpt-4o-mini.json
index 0e6c32b2..661b8aa1 100644
--- a/tests/configs/llm_model_configs/openai-gpt-4o-mini.json
+++ b/tests/configs/llm_model_configs/openai-gpt-4o-mini.json
@@ -1,5 +1,5 @@
 {
-  "context_window": 8192,
+  "context_window": 128000,
   "model": "gpt-4o-mini",
   "model_endpoint_type": "openai",
   "model_endpoint": "https://api.openai.com/v1",
diff --git a/tests/conftest.py b/tests/conftest.py
index cfca0ebf..0abe389d 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -28,6 +28,24 @@ def disable_e2b_api_key() -> Generator[None, None, None]:
     tool_settings.e2b_api_key = original_api_key
 
 
+@pytest.fixture
+def disable_pinecone() -> Generator[None, None, None]:
+    """
+    Temporarily disables Pinecone by setting `settings.enable_pinecone` to False
+    and `settings.pinecone_api_key` to None for the duration of the test.
+    Restores the original values afterward.
+    """
+    from letta.settings import settings
+
+    original_enable_pinecone = settings.enable_pinecone
+    original_pinecone_api_key = settings.pinecone_api_key
+    settings.enable_pinecone = False
+    settings.pinecone_api_key = None
+    yield
+    settings.enable_pinecone = original_enable_pinecone
+    settings.pinecone_api_key = original_pinecone_api_key
+
+
 @pytest.fixture
 def check_e2b_key_is_set():
     from letta.settings import tool_settings
diff --git a/tests/test_managers.py b/tests/test_managers.py
index 630f8c8b..9edc8af3 100644
--- a/tests/test_managers.py
+++ b/tests/test_managers.py
@@ -3320,7 +3320,7 @@ async def test_update_tool_pip_requirements(server: SyncServer, print_tool, defa
     # Add pip requirements to existing tool
     pip_reqs = [
         PipRequirement(name="pandas", version="1.5.0"),
-        PipRequirement(name="matplotlib"),
+        PipRequirement(name="sumy"),
     ]
 
     tool_update = ToolUpdate(pip_requirements=pip_reqs)
@@ -3334,7 +3334,7 @@ async def test_update_tool_pip_requirements(server: SyncServer, print_tool, defa
     assert len(updated_tool.pip_requirements) == 2
     assert updated_tool.pip_requirements[0].name == "pandas"
     assert updated_tool.pip_requirements[0].version == "1.5.0"
-    assert updated_tool.pip_requirements[1].name == "matplotlib"
+    assert updated_tool.pip_requirements[1].name == "sumy"
     assert updated_tool.pip_requirements[1].version is None
 
 
@@ -5218,6 +5218,41 @@ async def test_update_file_status_error_only(server, default_user, default_sourc
     assert updated.processing_status == FileProcessingStatus.PENDING  # default from creation
 
 
+@pytest.mark.asyncio
+async def test_update_file_status_with_chunks(server, default_user, default_source):
+    """Update chunk progress fields along with status."""
+    meta = PydanticFileMetadata(
+        file_name="chunks_test.txt",
+        file_path="/tmp/chunks_test.txt",
+        file_type="text/plain",
+        file_size=500,
+        source_id=default_source.id,
+    )
+    created = await server.file_manager.create_file(file_metadata=meta, actor=default_user)
+
+    # Update with chunk progress
+    updated = await server.file_manager.update_file_status(
+        file_id=created.id,
+        actor=default_user,
+        processing_status=FileProcessingStatus.EMBEDDING,
+        total_chunks=100,
+        chunks_embedded=50,
+    )
+    assert updated.processing_status == FileProcessingStatus.EMBEDDING
+    assert updated.total_chunks == 100
+    assert updated.chunks_embedded == 50
+
+    # Update only chunk progress
+    updated = await server.file_manager.update_file_status(
+        file_id=created.id,
+        actor=default_user,
+        chunks_embedded=100,
+    )
+    assert updated.chunks_embedded == 100
+    assert updated.total_chunks == 100  # unchanged
+    assert updated.processing_status == FileProcessingStatus.EMBEDDING  # unchanged
+
+
 @pytest.mark.asyncio
 async def test_upsert_file_content_basic(server: SyncServer, default_user, default_source, async_session):
     """Test creating and updating file content with upsert_file_content()."""
diff --git a/tests/test_sources.py b/tests/test_sources.py
index 2e239580..6a1a4af2 100644
--- a/tests/test_sources.py
+++ b/tests/test_sources.py
@@ -9,9 +9,10 @@ from letta_client import CreateBlock
 from letta_client import Letta as LettaSDKClient
 from letta_client.types import AgentState
 
-from letta.constants import FILES_TOOLS
+from letta.constants import DEFAULT_ORG_ID, FILES_TOOLS
 from letta.orm.enums import ToolType
 from letta.schemas.message import MessageCreate
+from letta.schemas.user import User
 from tests.utils import wait_for_server
 
 # Constants
@@ -49,7 +50,7 @@ def client() -> LettaSDKClient:
     yield client
 
 
-def upload_file_and_wait(client: LettaSDKClient, source_id: str, file_path: str, max_wait: int = 30):
+def upload_file_and_wait(client: LettaSDKClient, source_id: str, file_path: str, max_wait: int = 60):
     """Helper function to upload a file and wait for processing to complete"""
     with open(file_path, "rb") as f:
         file_metadata = client.sources.files.upload(source_id=source_id, file=f)
@@ -70,7 +71,7 @@ def upload_file_and_wait(client: LettaSDKClient, source_id: str, file_path: str,
 
 
 @pytest.fixture
-def agent_state(client: LettaSDKClient):
+def agent_state(disable_pinecone, client: LettaSDKClient):
     open_file_tool = client.tools.list(name="open_files")[0]
     search_files_tool = client.tools.list(name="semantic_search_files")[0]
     grep_tool = client.tools.list(name="grep_files")[0]
@@ -93,7 +94,7 @@ def agent_state(client: LettaSDKClient):
 # Tests
 
 
-def test_auto_attach_detach_files_tools(client: LettaSDKClient):
+def test_auto_attach_detach_files_tools(disable_pinecone, client: LettaSDKClient):
     """Test automatic attachment and detachment of file tools when managing agent sources."""
     # Create agent with basic configuration
     agent = client.agents.create(
@@ -164,6 +165,7 @@ def test_auto_attach_detach_files_tools(client: LettaSDKClient):
     ],
 )
 def test_file_upload_creates_source_blocks_correctly(
+    disable_pinecone,
     client: LettaSDKClient,
     agent_state: AgentState,
     file_path: str,
@@ -204,7 +206,7 @@ def test_file_upload_creates_source_blocks_correctly(
     assert not any(re.fullmatch(expected_label_regex, b.label) for b in blocks)
 
 
-def test_attach_existing_files_creates_source_blocks_correctly(client: LettaSDKClient, agent_state: AgentState):
+def test_attach_existing_files_creates_source_blocks_correctly(disable_pinecone, client: LettaSDKClient, agent_state: AgentState):
     # Create a new source
     source = client.sources.create(name="test_source", embedding="openai/text-embedding-3-small")
     assert len(client.sources.list()) == 1
@@ -240,7 +242,7 @@ def test_attach_existing_files_creates_source_blocks_correctly(client: LettaSDKC
     assert not any("test" in b.value for b in blocks)
 
 
-def test_delete_source_removes_source_blocks_correctly(client: LettaSDKClient, agent_state: AgentState):
+def test_delete_source_removes_source_blocks_correctly(disable_pinecone, client: LettaSDKClient, agent_state: AgentState):
     # Create a new source
     source = client.sources.create(name="test_source", embedding="openai/text-embedding-3-small")
     assert len(client.sources.list()) == 1
@@ -270,7 +272,7 @@ def test_delete_source_removes_source_blocks_correctly(client: LettaSDKClient, a
     assert not any("test" in b.value for b in blocks)
 
 
-def test_agent_uses_open_close_file_correctly(client: LettaSDKClient, agent_state: AgentState):
+def test_agent_uses_open_close_file_correctly(disable_pinecone, client: LettaSDKClient, agent_state: AgentState):
     # Create a new source
     source = client.sources.create(name="test_source", embedding="openai/text-embedding-3-small")
 
@@ -377,7 +379,7 @@ def test_agent_uses_open_close_file_correctly(client: LettaSDKClient, agent_stat
     print("✓ File successfully opened with different range - content differs as expected")
 
 
-def test_agent_uses_search_files_correctly(client: LettaSDKClient, agent_state: AgentState):
+def test_agent_uses_search_files_correctly(disable_pinecone, client: LettaSDKClient, agent_state: AgentState):
     # Create a new source
     source = client.sources.create(name="test_source", embedding="openai/text-embedding-3-small")
 
@@ -423,7 +425,7 @@ def test_agent_uses_search_files_correctly(client: LettaSDKClient, agent_state:
     assert all(tr.status == "success" for tr in tool_returns), "Tool call failed"
 
 
-def test_agent_uses_grep_correctly_basic(client: LettaSDKClient, agent_state: AgentState):
+def test_agent_uses_grep_correctly_basic(disable_pinecone, client: LettaSDKClient, agent_state: AgentState):
     # Create a new source
     source = client.sources.create(name="test_source", embedding="openai/text-embedding-3-small")
 
@@ -465,7 +467,7 @@ def test_agent_uses_grep_correctly_basic(client: LettaSDKClient, agent_state: Ag
     assert all(tr.status == "success" for tr in tool_returns), "Tool call failed"
 
 
-def test_agent_uses_grep_correctly_advanced(client: LettaSDKClient, agent_state: AgentState):
+def test_agent_uses_grep_correctly_advanced(disable_pinecone, client: LettaSDKClient, agent_state: AgentState):
     # Create a new source
     source = client.sources.create(name="test_source", embedding="openai/text-embedding-3-small")
 
@@ -517,7 +519,7 @@ def test_agent_uses_grep_correctly_advanced(client: LettaSDKClient, agent_state:
     assert "513:" in tool_return_message.tool_return
 
 
-def test_create_agent_with_source_ids_creates_source_blocks_correctly(client: LettaSDKClient):
+def test_create_agent_with_source_ids_creates_source_blocks_correctly(disable_pinecone, client: LettaSDKClient):
     """Test that creating an agent with source_ids parameter correctly creates source blocks."""
     # Create a new source
     source = client.sources.create(name="test_source", embedding="openai/text-embedding-3-small")
@@ -560,7 +562,7 @@ def test_create_agent_with_source_ids_creates_source_blocks_correctly(client: Le
     assert file_tools == set(FILES_TOOLS)
 
 
-def test_view_ranges_have_metadata(client: LettaSDKClient, agent_state: AgentState):
+def test_view_ranges_have_metadata(disable_pinecone, client: LettaSDKClient, agent_state: AgentState):
     # Create a new source
     source = client.sources.create(name="test_source", embedding="openai/text-embedding-3-small")
 
@@ -623,7 +625,7 @@ def test_view_ranges_have_metadata(client: LettaSDKClient, agent_state: AgentSta
     )
 
 
-def test_duplicate_file_renaming(client: LettaSDKClient):
+def test_duplicate_file_renaming(disable_pinecone, client: LettaSDKClient):
     """Test that duplicate files are renamed with count-based suffixes (e.g., file.txt, file (1).txt, file (2).txt)"""
     # Create a new source
     source = client.sources.create(name="test_duplicate_source", embedding="openai/text-embedding-3-small")
@@ -662,7 +664,7 @@ def test_duplicate_file_renaming(client: LettaSDKClient):
         print(f"  File {i+1}: original='{file.original_file_name}' → renamed='{file.file_name}'")
 
 
-def test_open_files_schema_descriptions(client: LettaSDKClient):
+def test_open_files_schema_descriptions(disable_pinecone, client: LettaSDKClient):
     """Test that open_files tool schema contains correct descriptions from docstring"""
 
     # Get the open_files tool
@@ -743,3 +745,132 @@ def test_open_files_schema_descriptions(client: LettaSDKClient):
     expected_length_desc = "Optional number of lines to view from offset (inclusive). If not specified, views to end of file."
     assert length_prop["description"] == expected_length_desc
     assert length_prop["type"] == "integer"
+
+
+# --- Pinecone Tests ---
+
+
+def test_pinecone_search_files_tool(client: LettaSDKClient):
+    """Test that search_files tool uses Pinecone when enabled"""
+    from letta.helpers.pinecone_utils import should_use_pinecone
+
+    if not should_use_pinecone(verbose=True):
+        pytest.skip("Pinecone not configured (missing API key or disabled), skipping Pinecone-specific tests")
+
+    print("Testing Pinecone search_files tool functionality")
+
+    # Create agent with file tools
+    agent = client.agents.create(
+        name="test_pinecone_agent",
+        memory_blocks=[
+            CreateBlock(label="human", value="username: testuser"),
+        ],
+        model="openai/gpt-4o-mini",
+        embedding="openai/text-embedding-3-small",
+    )
+
+    # Create source and attach to agent
+    source = client.sources.create(name="test_pinecone_source", embedding="openai/text-embedding-3-small")
+    client.agents.sources.attach(source_id=source.id, agent_id=agent.id)
+
+    # Upload a file with searchable content
+    file_path = "tests/data/long_test.txt"
+    upload_file_and_wait(client, source.id, file_path)
+
+    # Test semantic search using Pinecone
+    search_response = client.agents.messages.create(
+        agent_id=agent.id,
+        messages=[MessageCreate(role="user", content="Use the semantic_search_files tool to search for 'electoral history' in the files.")],
+    )
+
+    # Verify tool was called successfully
+    tool_calls = [msg for msg in search_response.messages if msg.message_type == "tool_call_message"]
+    assert len(tool_calls) > 0, "No tool calls found"
+    assert any(tc.tool_call.name == "semantic_search_files" for tc in tool_calls), "semantic_search_files not called"
+
+    # Verify tool returned results
+    tool_returns = [msg for msg in search_response.messages if msg.message_type == "tool_return_message"]
+    assert len(tool_returns) > 0, "No tool returns found"
+    assert all(tr.status == "success" for tr in tool_returns), "Tool call failed"
+
+    # Check that results contain expected content
+    search_results = tool_returns[0].tool_return
+    print(search_results)
+    assert (
+        "electoral" in search_results.lower() or "history" in search_results.lower()
+    ), f"Search results should contain relevant content: {search_results}"
+
+
+def test_pinecone_lifecycle_file_and_source_deletion(client: LettaSDKClient):
+    """Test that file and source deletion removes records from Pinecone"""
+    import asyncio
+
+    from letta.helpers.pinecone_utils import list_pinecone_index_for_files, should_use_pinecone
+
+    if not should_use_pinecone():
+        pytest.skip("Pinecone not configured (missing API key or disabled), skipping Pinecone-specific tests")
+
+    print("Testing Pinecone file and source deletion lifecycle")
+
+    # Create source
+    source = client.sources.create(name="test_lifecycle_source", embedding="openai/text-embedding-3-small")
+
+    # Upload multiple files and wait for processing
+    file_paths = ["tests/data/test.txt", "tests/data/test.md"]
+    uploaded_files = []
+    for file_path in file_paths:
+        file_metadata = upload_file_and_wait(client, source.id, file_path)
+        uploaded_files.append(file_metadata)
+
+    # Get temp user for Pinecone operations
+    user = User(name="temp", organization_id=DEFAULT_ORG_ID)
+
+    # Test file-level deletion first
+    if len(uploaded_files) > 1:
+        file_to_delete = uploaded_files[0]
+
+        # Check records for the specific file using list function
+        records_before = asyncio.run(list_pinecone_index_for_files(file_to_delete.id, user))
+        print(f"Found {len(records_before)} records for file before deletion")
+
+        # Delete the file
+        client.sources.files.delete(source_id=source.id, file_id=file_to_delete.id)
+
+        # Allow time for deletion to propagate
+        time.sleep(2)
+
+        # Verify file records are removed
+        records_after = asyncio.run(list_pinecone_index_for_files(file_to_delete.id, user))
+        print(f"Found {len(records_after)} records for file after deletion")
+
+        assert len(records_after) == 0, f"File records should be removed from Pinecone after deletion, but found {len(records_after)}"
+
+    # Test source-level deletion - check remaining files
+    # Check records for remaining files
+    remaining_records = []
+    for file_metadata in uploaded_files[1:]:  # Skip the already deleted file
+        file_records = asyncio.run(list_pinecone_index_for_files(file_metadata.id, user))
+        remaining_records.extend(file_records)
+
+    records_before = len(remaining_records)
+    print(f"Found {records_before} records for remaining files before source deletion")
+
+    # Delete the entire source
+    client.sources.delete(source_id=source.id)
+
+    # Allow time for deletion to propagate
+    time.sleep(3)
+
+    # Verify all remaining file records are removed
+    records_after = []
+    for file_metadata in uploaded_files[1:]:
+        file_records = asyncio.run(list_pinecone_index_for_files(file_metadata.id, user))
+        records_after.extend(file_records)
+
+    print(f"Found {len(records_after)} records for files after source deletion")
+
+    assert (
+        len(records_after) == 0
+    ), f"All source records should be removed from Pinecone after source deletion, but found {len(records_after)}"
+
+    print("✓ Pinecone lifecycle verified - namespace is clean after source deletion")