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Enables AI agents to record and rank learnings, facts, and methods through a collaborative voting framework. It provides tools for agents to surface the most us
Enables AI agents to record and rank learnings, facts, and methods through a collaborative voting framework. It provides tools for agents to surface the most useful information across sessions using persistent memory storage.
A memory/tidbits voting framework for AI agents. Agents can record learnings (facts, methods, gotchas) and vote on them to surface the most useful tidbits.
uv pip install -e ".[dev]"
from tidbits_memory.adapters.memory import InMemoryAdapter
from tidbits_memory.store import MemoryStore
store = MemoryStore(InMemoryAdapter())
# Create a voter id for this session
voter_id = store.create_voter_id()
# Create a memory
mem = store.create_memory("Python dicts preserve insertion order since 3.7", voter_id=voter_id)
# Get memories (random order, no vote counts) — ideal for agent consumption
result = store.get_memories(voter_id=voter_id)
print(result["memories"])
# Upvote a useful memory
store.upvote_memory(mem.id, voter_id="another-session-id")
# List memories sorted by votes (most upvoted first)
for m in store.list_memories():
print(f"[{m.votes}] {m.content}")
# Downvote an erroneous memory
store.downvote_memory(mem.id, voter_id="yet-another-session")
# Unvote (remove a prior vote)
store.unvote_memory(mem.id, "another-session-id")
# Remove a memory
store.remove_memory(mem.id)
from tidbits_memory.adapters.json_file import JsonFileAdapter
from tidbits_memory.store import MemoryStore
store = MemoryStore(JsonFileAdapter("memories.json"))
store.create_memory("Use `uv` for fast Python packaging")
When an agent doesn't have a session/conversation ID, use create_voter_id:
voter_id = store.create_voter_id()
# Use this voter_id for all votes in this session
If no voter_id is provided when calling get_memories, one is automatically generated and returned in the response.
from mcp.server import FastMCP
from tidbits_memory.adapters.memory import InMemoryAdapter
from tidbits_memory.store import MemoryStore
from tidbits_memory.tools import register_tools
store = MemoryStore(InMemoryAdapter())
mcp = FastMCP("tidbits")
register_tools(mcp, store)
# Registered tool names:
# create_memory, upvote_memory, downvote_memory, unvote_memory,
# list_memory, get_memories, remove_memory, create_voter_id
uv run pytest
Добавь это в claude_desktop_config.json и перезапусти Claude Desktop.
{
"mcpServers": {
"tidbits-memory": {
"command": "npx",
"args": []
}
}
}Web content fetching and conversion for efficient LLM usage.
Retrieval from AWS Knowledge Base using Bedrock Agent Runtime.
Provides auto-configuration for setting up an MCP server in Spring Boot applications.
A very streamlined mcp client that supports calling and monitoring stdio/sse/streamableHttp, and can also view request responses through the /logs page. It also