diff --git a/docs/INTEGRATIONS.md b/docs/INTEGRATIONS.md new file mode 100644 index 0000000..c62bd8f --- /dev/null +++ b/docs/INTEGRATIONS.md @@ -0,0 +1,198 @@ +# CogniFold Integrations + +CogniFold can plug into external tools as a persistent memory layer. This page +documents the supported integrations and roadmap. + +## MCP (Model Context Protocol) + +CogniFold ships an MCP server that exposes its persistent concept-graph memory +to any MCP client — Claude Code, Claude Desktop, Cursor, and others. The server +wraps CogniFold's existing in-process logic (the ingestion `Pipeline` and the +`MemoryQueryAgent`); it does not reimplement memory. + +### Install + +```bash +pip install 'cognifold[mcp]' +``` + +This pulls in the official `mcp` Python SDK alongside CogniFold. For LLM-based +concept folding you also want an API key and (optionally) the agent extra: + +```bash +pip install 'cognifold[mcp,agent]' +``` + +### Tools + +| Tool | Description | +| --- | --- | +| `cognifold_remember(text, timestamp?)` | Ingest an observation/fact/event into the persistent graph. Returns the graph deltas (nodes/edges added, concepts created). | +| `cognifold_query(question, max_nodes?)` | Retrieve relevant context from memory for a question. Returns assembled context + supporting nodes. | +| `cognifold_graph_stats()` | Node/edge counts by type (events, concepts, intents, time nodes). | +| `cognifold_list_intents()` | Current intents (goals/desires) with id, status, and description. | + +### Running the server + +The server speaks MCP over stdio. Either entry point works: + +```bash +python -m cognifold.mcp +# or +cognifold-mcp +``` + +`cognifold-mcp --help` prints usage and the environment variables it reads. + +### Environment variables + +| Variable | Purpose | Default | +| --- | --- | --- | +| `COGNIFOLD_MCP_GRAPH` | Path where the graph JSON is persisted (so memory survives restarts). | `~/.cognifold/mcp_graph.json` | +| `COGNIFOLD_MODEL__NAME` | LLM model used for concept folding during `remember`. | `gemini-2.5-flash` | +| `GOOGLE_API_KEY` | API key for Gemini models. | — | +| `OPENAI_API_KEY` | API key for OpenAI models. | — | + +If no API key is set, `cognifold_remember` still works — it falls back to a +default plan that stores the raw event as a node (no LLM-based concept +extraction). + +### Claude Desktop + +Add CogniFold to `claude_desktop_config.json` (macOS: +`~/Library/Application Support/Claude/claude_desktop_config.json`): + +```json +{ + "mcpServers": { + "cognifold": { + "command": "cognifold-mcp", + "env": { + "COGNIFOLD_MCP_GRAPH": "/Users/you/.cognifold/mcp_graph.json", + "COGNIFOLD_MODEL__NAME": "gemini-2.5-flash", + "GOOGLE_API_KEY": "your-key-here" + } + } + } +} +``` + +If `cognifold-mcp` is not on Claude Desktop's `PATH`, use the module form with +an explicit interpreter: + +```json +{ + "mcpServers": { + "cognifold": { + "command": "python", + "args": ["-m", "cognifold.mcp"], + "env": { + "COGNIFOLD_MCP_GRAPH": "/Users/you/.cognifold/mcp_graph.json", + "GOOGLE_API_KEY": "your-key-here" + } + } + } +} +``` + +Restart Claude Desktop after editing the config. + +### Claude Code + +Register the server with the CLI: + +```bash +claude mcp add cognifold \ + --env COGNIFOLD_MCP_GRAPH=$HOME/.cognifold/mcp_graph.json \ + --env COGNIFOLD_MODEL__NAME=gemini-2.5-flash \ + --env GOOGLE_API_KEY=your-key-here \ + -- cognifold-mcp +``` + +Or commit a project-scoped `.mcp.json` to the repo root: + +```json +{ + "mcpServers": { + "cognifold": { + "command": "cognifold-mcp", + "env": { + "COGNIFOLD_MCP_GRAPH": ".cognifold/mcp_graph.json", + "COGNIFOLD_MODEL__NAME": "gemini-2.5-flash", + "GOOGLE_API_KEY": "your-key-here" + } + } + } +} +``` + +### Cursor + +Cursor reads the same MCP server schema. Add to +`~/.cursor/mcp.json` (global) or `.cursor/mcp.json` (project): + +```json +{ + "mcpServers": { + "cognifold": { + "command": "cognifold-mcp", + "env": { + "COGNIFOLD_MCP_GRAPH": "/Users/you/.cognifold/mcp_graph.json", + "GOOGLE_API_KEY": "your-key-here" + } + } + } +} +``` + +### Example round trip + +Once the server is registered, the client's model can call the tools: + +``` +> cognifold_remember(text="Started using CogniFold as a memory layer for my + research notes on 2026-06-18.") + +{ + "event_id": "evt-1a2b3c4d5e6f", + "success": true, + "nodes_added": 3, + "edges_added": 2, + "concepts_created": ["cognifold-memory-layer", "research-notes"], + "total_nodes": 3, + "total_edges": 2, + "graph_path": "/Users/you/.cognifold/mcp_graph.json" +} + +> cognifold_query(question="What am I using CogniFold for?") + +{ + "question": "What am I using CogniFold for?", + "context": "CONCEPTS:\n- CogniFold memory layer: used for research notes ...", + "supporting_nodes": [ + {"node_id": "...", "type": "concept", "title": "CogniFold memory layer", + "relevance": 0.91, "description": "Memory layer for research notes"} + ], + "nodes_scanned": 3, + "query_time_ms": 4.2 +} +``` + +The memory persists at `COGNIFOLD_MCP_GRAPH`, so a follow-up `cognifold_query` +in a later session still recalls these facts. + +--- + +## Roadmap (Planned) + +The following integrations are **planned** and not yet implemented: + +- **OpenAI-compatible API** — _Planned._ A drop-in `/v1`-style HTTP surface so + CogniFold memory can be used by any OpenAI-compatible client/SDK. +- **LangChain `BaseMemory`** — _Planned._ A `BaseMemory` subclass backed by + CogniFold so LangChain chains/agents can read and write the concept graph. +- **LlamaIndex retriever** — _Planned._ A `BaseRetriever` implementation that + queries the CogniFold graph for use in LlamaIndex query engines. + +Contributions welcome — these wrap the same in-process `Pipeline` / +`MemoryQueryAgent` seams the MCP server already uses. diff --git a/pyproject.toml b/pyproject.toml index 29166c9..fa830b3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,7 +3,7 @@ name = "cognifold" version = "0.1.0" description = "A dynamic concept graph system that processes real-time event streams and maintains an evolving knowledge representation" readme = "README.md" -requires-python = ">=3.9" +requires-python = ">=3.10" dependencies = [ "networkx>=3.2", "numpy>=2.0.2", @@ -17,6 +17,7 @@ dependencies = [ [project.scripts] cognifold = "cognifold.cli:main" +cognifold-mcp = "cognifold.mcp:main" [project.optional-dependencies] dev = [ @@ -37,6 +38,9 @@ viz = [ search = [ "faiss-cpu>=1.7", ] +mcp = [ + "mcp>=1.0", +] service = [ "fastapi>=0.109", "uvicorn[standard]>=0.27", diff --git a/src/cognifold/mcp/__init__.py b/src/cognifold/mcp/__init__.py new file mode 100644 index 0000000..7e72e7f --- /dev/null +++ b/src/cognifold/mcp/__init__.py @@ -0,0 +1,26 @@ +"""Model Context Protocol (MCP) server for CogniFold. + +Exposes CogniFold's persistent concept-graph memory as an MCP server so it +plugs into Claude Code, Claude Desktop, Cursor, and any other MCP client. + +The server wraps the existing in-process logic (``Pipeline`` for ingestion, +``MemoryQueryAgent`` for retrieval, ``ConceptGraph`` metrics for stats). It does +not reimplement memory — it is a thin adapter over the same code paths the +HTTP service uses. + +Run it as a stdio server:: + + python -m cognifold.mcp + # or, if installed with the console script: + cognifold-mcp + +Install the optional dependency first:: + + pip install 'cognifold[mcp]' +""" + +from __future__ import annotations + +from cognifold.mcp.server import build_server, main + +__all__ = ["build_server", "main"] diff --git a/src/cognifold/mcp/__main__.py b/src/cognifold/mcp/__main__.py new file mode 100644 index 0000000..37d53be --- /dev/null +++ b/src/cognifold/mcp/__main__.py @@ -0,0 +1,16 @@ +"""``python -m cognifold.mcp`` entry point.""" + +from __future__ import annotations + +import sys + +from cognifold.mcp.server import main + +if __name__ == "__main__": + try: + main() + except RuntimeError as exc: + # Surface the "pip install 'cognifold[mcp]'" hint cleanly instead of a + # traceback when the MCP SDK is not installed. + print(str(exc), file=sys.stderr) + sys.exit(1) diff --git a/src/cognifold/mcp/memory.py b/src/cognifold/mcp/memory.py new file mode 100644 index 0000000..f3c32b0 --- /dev/null +++ b/src/cognifold/mcp/memory.py @@ -0,0 +1,240 @@ +"""In-process memory wrapper shared by the MCP tools. + +This module mirrors the minimal flow the HTTP service performs +(``service/processor.py`` + ``service/session.py``) but in a single in-process +object suitable for a stdio MCP server: one persistent graph, ingested via the +existing ``Pipeline``, queried via the existing ``MemoryQueryAgent``, and saved +to disk through the existing ``graph/persistence`` helpers so memory survives +across tool calls and restarts. + +Nothing here changes algorithm behaviour — it only wires the existing classes +together and adds load/save around them. +""" + +from __future__ import annotations + +import logging +import os +import sys +import uuid +from datetime import datetime +from pathlib import Path +from typing import Any + +from cognifold.config import CognifoldConfig +from cognifold.graph.persistence import load_graph, save_graph +from cognifold.graph.store import ConceptGraph +from cognifold.models.event import Event +from cognifold.models.node import NodeType +from cognifold.pipeline.classic import Pipeline + +DEFAULT_GRAPH_PATH = Path.home() / ".cognifold" / "mcp_graph.json" + + +def _redirect_logging_to_stderr() -> None: + """Point CogniFold's log handlers at stderr. + + The MCP stdio transport owns stdout for JSON-RPC framing — any log line on + stdout corrupts the protocol. CogniFold's ``setup_logging`` (invoked inside + ``Pipeline.__init__``) attaches a ``StreamHandler(sys.stdout)`` to the root + logger. We retarget those handlers to stderr after the pipeline is built, + without otherwise changing logging behaviour. + """ + for logger in (logging.getLogger(), logging.getLogger("cognifold")): + for handler in logger.handlers: + if isinstance(handler, logging.StreamHandler) and handler.stream is sys.stdout: + handler.setStream(sys.stderr) + + +def _resolve_graph_path() -> Path: + """Resolve the on-disk graph path from env or the default location.""" + raw = os.environ.get("COGNIFOLD_MCP_GRAPH") + if raw: + return Path(raw).expanduser() + return DEFAULT_GRAPH_PATH + + +class CognifoldMemory: + """A single persistent CogniFold memory backing the MCP tools. + + Holds one ``Pipeline`` (for ingestion) over a ``ConceptGraph`` that is + loaded from / saved to ``graph_path``. A ``MemoryQueryAgent`` is lazily + constructed against the same graph for queries. + + Args: + config: Optional pre-built config. Defaults to ``CognifoldConfig.load()`` + which reads model name + API keys from the environment. + graph_path: Where the graph JSON is persisted. Defaults to + ``$COGNIFOLD_MCP_GRAPH`` or ``~/.cognifold/mcp_graph.json``. + """ + + def __init__( + self, + config: CognifoldConfig | None = None, + graph_path: str | Path | None = None, + ) -> None: + self._config = config or CognifoldConfig.load() + self._graph_path = Path(graph_path).expanduser() if graph_path else _resolve_graph_path() + + # Build the pipeline (creates its own empty ConceptGraph), then swap in + # the persisted graph if one exists on disk. The pipeline reads its + # graph through the private `_graph` attribute; we replace it before any + # event is processed so the ranker/agent operate on the loaded graph. + self._pipeline = Pipeline(self._config) + # Pipeline.__init__ wired logging to stdout; reclaim stdout for MCP. + _redirect_logging_to_stderr() + if self._graph_path.exists(): + loaded = load_graph(self._graph_path) + # Pipeline has no public graph setter; swap the persisted graph in + # before any event is processed. Deliberate internal write. + self._pipeline._graph = loaded # pyright: ignore[reportPrivateUsage] + + self._query_agent: Any = None + + @property + def graph(self) -> ConceptGraph: + """The live concept graph (same object the pipeline mutates).""" + return self._pipeline.graph + + @property + def graph_path(self) -> Path: + """Path the graph is persisted to.""" + return self._graph_path + + # ------------------------------------------------------------------ + # Ingestion + # ------------------------------------------------------------------ + def remember(self, text: str, timestamp: datetime | None = None) -> dict[str, Any]: + """Ingest a free-text observation into the persistent graph. + + Mirrors ``service/processor.process_event_sync``: build an ``Event``, + run it through the pipeline (LLM agent if an API key is configured, + otherwise a default add-node plan), persist, and report deltas. + + Args: + text: The observation / fact / event to remember. + timestamp: When it happened. Defaults to now. + + Returns: + A dict summarising the graph deltas and resulting totals. + """ + before_nodes = self.graph.node_count + before_edges = self.graph.edge_count + + event = Event( + event_id=f"evt-{uuid.uuid4().hex[:12]}", + timestamp=timestamp or datetime.now(), + source="mcp", + event_type="note", + title=text[:120], + description=text, + ) + + result = self._pipeline.process_event(event) + + # Persist so memory survives across tool calls / restarts. + save_graph(self.graph, self._graph_path, backup=False) + + # The query agent's cached indexes are now stale. + if self._query_agent is not None: + self._query_agent.invalidate_search_cache() + + return { + "event_id": event.event_id, + "success": result.success, + "error": result.error, + "nodes_added": self.graph.node_count - before_nodes, + "edges_added": self.graph.edge_count - before_edges, + "concepts_created": result.concepts_created, + "intents_created": result.actions_created, + "total_nodes": self.graph.node_count, + "total_edges": self.graph.edge_count, + "graph_path": str(self._graph_path), + } + + # ------------------------------------------------------------------ + # Query + # ------------------------------------------------------------------ + def _get_query_agent(self) -> Any: + if self._query_agent is None: + from cognifold.query import MemoryQueryAgent + + self._query_agent = MemoryQueryAgent(self.graph) + return self._query_agent + + def query(self, question: str, max_nodes: int = 10) -> dict[str, Any]: + """Answer a question from memory via ``MemoryQueryAgent``. + + Returns the assembled context plus brief supporting-node summaries. + Note: CogniFold's query layer returns retrieved context (not an + LLM-generated answer); the MCP client's model reads the context to + answer. + + Args: + question: Natural-language question. + max_nodes: Max supporting nodes to retrieve. + + Returns: + A dict with ``context`` and ``supporting_nodes``. + """ + agent = self._get_query_agent() + result = agent.query(question, max_nodes=max_nodes) + + supporting = [ + { + "node_id": n.node_id, + "type": n.node_type, + "title": n.title, + "relevance": round(n.relevance_score, 4), + "description": (n.description or "")[:280] or None, + } + for n in result.nodes + ] + + return { + "question": question, + "context": result.context, + "supporting_nodes": supporting, + "nodes_scanned": result.total_nodes_scanned, + "query_time_ms": round(result.query_time_ms, 2), + } + + # ------------------------------------------------------------------ + # Stats + # ------------------------------------------------------------------ + def graph_stats(self) -> dict[str, Any]: + """Node/edge counts by type (mirrors ``Session.get_graph_stats``).""" + graph = self.graph + return { + "node_count": graph.node_count, + "edge_count": graph.edge_count, + "events": len(graph.get_nodes_by_type(NodeType.EVENT)), + "concepts": len(graph.get_nodes_by_type(NodeType.CONCEPT)), + "intents": len(graph.get_nodes_by_type(NodeType.INTENT)), + "time_nodes": len(graph.get_nodes_by_type(NodeType.TIME)), + "graph_path": str(self._graph_path), + } + + # ------------------------------------------------------------------ + # Intents + # ------------------------------------------------------------------ + def list_intents(self) -> list[dict[str, Any]]: + """List intent nodes (goals/desires) currently in the graph. + + Intents are stored as ``NodeType.INTENT`` nodes; status lives in + ``node.data['status']`` when present. + """ + intents: list[dict[str, Any]] = [] + for node in self.graph.get_nodes_by_type(NodeType.INTENT): + data = node.data or {} + intents.append( + { + "id": node.id, + "status": data.get("status", "pending"), + "title": data.get("title") or data.get("name") or node.id, + "description": data.get("description"), + "reasoning": node.reasoning, + "last_accessed": node.last_accessed.isoformat(), + } + ) + return intents diff --git a/src/cognifold/mcp/server.py b/src/cognifold/mcp/server.py new file mode 100644 index 0000000..35f8044 --- /dev/null +++ b/src/cognifold/mcp/server.py @@ -0,0 +1,159 @@ +"""FastMCP stdio server exposing CogniFold memory tools. + +Tools: + - ``cognifold_remember`` ingest an observation into the persistent graph + - ``cognifold_query`` retrieve context from memory for a question + - ``cognifold_graph_stats`` node/edge counts by type + - ``cognifold_list_intents``current intents (goals/desires) in the graph + +The MCP Python SDK (``mcp``) is an optional dependency. If it is missing we +raise a clear, actionable error rather than a bare ``ImportError`` so users +know to ``pip install 'cognifold[mcp]'``. +""" + +from __future__ import annotations + +import json +from typing import TYPE_CHECKING + +from cognifold.mcp.memory import CognifoldMemory + +if TYPE_CHECKING: + from mcp.server.fastmcp import FastMCP # pyright: ignore[reportMissingImports] + +_MCP_INSTALL_HINT = ( + "The MCP Python SDK is required to run the CogniFold MCP server but is not " + "installed. Install it with:\n\n pip install 'cognifold[mcp]'\n\n" + '(or `pip install "mcp>=1.0"`).' +) + + +def _require_fastmcp() -> type[FastMCP]: + """Import FastMCP, raising a helpful message if the SDK is absent.""" + try: + from mcp.server.fastmcp import FastMCP # pyright: ignore[reportMissingImports] + except ImportError as exc: # pragma: no cover - exercised only without SDK + raise RuntimeError(_MCP_INSTALL_HINT) from exc + return FastMCP + + +def build_server(memory: CognifoldMemory | None = None) -> FastMCP: + """Construct the FastMCP server with CogniFold tools registered. + + Args: + memory: Optional pre-built memory (useful for tests). Defaults to a + fresh ``CognifoldMemory`` reading config + graph path from env. + + Returns: + A configured ``FastMCP`` instance ready to ``.run()``. + """ + fast_mcp_cls = _require_fastmcp() + mem = memory or CognifoldMemory() + + server = fast_mcp_cls( + name="cognifold", + instructions=( + "CogniFold is a persistent concept-graph memory. Use " + "cognifold_remember to store observations/facts as they happen, " + "cognifold_query to recall relevant context before answering, " + "cognifold_graph_stats to inspect memory size, and " + "cognifold_list_intents to see tracked goals." + ), + ) + + @server.tool() + def cognifold_remember(text: str, timestamp: str | None = None) -> str: # pyright: ignore[reportUnusedFunction] + """Store an observation, fact, or event in CogniFold's persistent memory. + + The text is ingested through CogniFold's pipeline, which extracts + concepts/intents and links them into the concept graph. The graph is + persisted to disk so it survives across calls and restarts. + + Args: + text: The observation to remember (a fact, event, or note). + timestamp: Optional ISO-8601 timestamp of when it happened + (e.g. "2026-06-18T14:30:00"). Defaults to now. + + Returns: + JSON summary of graph deltas (nodes/edges added, concepts created). + """ + from datetime import datetime + + ts = datetime.fromisoformat(timestamp) if timestamp else None + result = mem.remember(text, timestamp=ts) + return json.dumps(result, ensure_ascii=False, indent=2) + + @server.tool() + def cognifold_query(question: str, max_nodes: int = 10) -> str: # pyright: ignore[reportUnusedFunction] + """Recall relevant context from CogniFold memory for a question. + + Runs CogniFold's MemoryQueryAgent over the persistent graph and returns + the assembled context plus the supporting nodes. Read the returned + context to answer the user's question. + + Args: + question: The natural-language question to recall context for. + max_nodes: Maximum number of supporting nodes to retrieve (default 10). + + Returns: + JSON with the assembled ``context`` and brief ``supporting_nodes``. + """ + result = mem.query(question, max_nodes=max_nodes) + return json.dumps(result, ensure_ascii=False, indent=2) + + @server.tool() + def cognifold_graph_stats() -> str: # pyright: ignore[reportUnusedFunction] + """Report CogniFold memory size: node and edge counts by type. + + Returns: + JSON with total node/edge counts and per-type counts (events, + concepts, intents, time nodes) plus the on-disk graph path. + """ + return json.dumps(mem.graph_stats(), ensure_ascii=False, indent=2) + + @server.tool() + def cognifold_list_intents() -> str: # pyright: ignore[reportUnusedFunction] + """List the goals/desires (intents) CogniFold is currently tracking. + + Returns: + JSON array of intents, each with id, status, title, and description. + """ + return json.dumps(mem.list_intents(), ensure_ascii=False, indent=2) + + return server + + +def main(argv: list[str] | None = None) -> None: + """Entry point: build and run the stdio MCP server. + + Args: + argv: Optional CLI args. Supports ``--help`` and ``--version``; any + other args are ignored (the server reads config from env). + """ + import sys + + args = list(sys.argv[1:] if argv is None else argv) + + if "--help" in args or "-h" in args: + print( + "cognifold-mcp — CogniFold Model Context Protocol (stdio) server\n\n" + "Usage: cognifold-mcp\n" + " python -m cognifold.mcp\n\n" + "Environment:\n" + " COGNIFOLD_MCP_GRAPH path to persist the graph " + "(default ~/.cognifold/mcp_graph.json)\n" + " COGNIFOLD_MODEL__NAME LLM model name (default gemini-2.5-flash)\n" + " GOOGLE_API_KEY / OPENAI_API_KEY API key for LLM-based folding\n\n" + "Exposes tools: cognifold_remember, cognifold_query, " + "cognifold_graph_stats, cognifold_list_intents.", + ) + return + + if "--version" in args: + from cognifold import __version__ + + print(f"cognifold-mcp {__version__}") + return + + server = build_server() + server.run() diff --git a/uv.lock b/uv.lock index 3aad57e..207345a 100644 --- a/uv.lock +++ b/uv.lock @@ -1,10 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