Cachly MCP

What cachly-dev/cachly-mcp does

cachly-dev/cachly-mcp gives coding agents a persistent memory layer connected to Cachly. Agents can save context, lessons, session summaries, handoffs, roadmap items, and shared team knowledge, then retrieve that information during later tasks. The service also exposes cache operations for key-value data and a dispatcher for specialist Cachly functions.

The memory is organized around a Brain stored on EU servers. Lessons can record topics, outcomes, successful approaches, failed attempts, severity, file paths, commands, and authors. Shared lessons help teammates learn from one another, while roadmap records preserve planned, active, blocked, and completed work.

How it works

At the beginning of a work period, session_start returns a briefing with the previous session, recent and relevant lessons, open failures, roadmap information, team discoveries, predictive warnings, and older condensed knowledge. session_end records accomplishments, changed files, and lesson counts. session_handoff preserves progress and remaining tasks for the next chat window.

smart_recall searches remembered context by meaning, while brain_search searches more broadly across lessons, context, sessions, knowledge-graph data, and roadmap items. recall_best_solution finds the strongest known solution for a topic. causal_trace follows stored relationships from a symptom toward a root cause and a previously successful fix. brain_predict uses stored knowledge to surface likely failure patterns.

Git-oriented tools can infer lessons from commit history, and session completion can learn from commits made during the session. index_project stores file summaries and paths for later semantic lookup rather than requiring an agent to reread the entire project.

Setup and configuration

Claude Code can install the Cachly plugin through its marketplace commands. Other supported editors can use the autopilot command, which detects editors, writes their MCP configuration and instruction files, and adds a Git hook for learning from commits. A browser sign-in associates the Brain with an account; without a key, the first session can create a test Brain on EU servers.

The npm package can also be run with the @latest tag. The VS Code extension and JetBrains plugin provide editor-specific setup paths. After configuration, restart the editor. claude mcp list can verify the Claude Code connection.

Tools and capabilities

• Save and retrieve project context with remember_context, recall_context, list_remembered, and forget_context.
• Record and recall lessons with learn_from_attempts and recall_best_solution.
• Brief, close, and hand off sessions with session_start, session_end, and session_handoff.
• Share knowledge through team_learn and team_recall.
• Manage persistent work with roadmap_add, roadmap_update, roadmap_list, and roadmap_next.
• Search memory using semantic, BM25-based, or causal queries.
• Inspect Brain health with brain_doctor and related analytics tools.
• Store, retrieve, and search cache values through the cache tools.
• Access less frequently used specialist functions through cachly_tool; calling it with describe:true returns a tool schema before execution.

Limitations and notes

Semantic search and project indexing require an embedding provider. The documented options include OPENAI_API_KEY or a compatible provider configuration; vector search also requires the appropriate Cachly tier and CACHLY_VECTOR_URL. Exact key lookup and context recall are separate from semantic search, so the matching tool depends on whether the agent knows the key or only the meaning.

Some editors receive MCP instructions automatically, while others rely on the rule files created by autopilot. The autopilot command writes configuration files and can overwrite an existing target file. Cached project entries from index_project use a 24-hour TTL and should be refreshed after major refactors. Lessons and other Brain data can be exported with the package’s export command.