FailEcho is a cross-agent failure intelligence network for AI agents and autonomous software. Its core purpose is: before an agent retries a failed tool or model call, first query the network to see whether other agents have encountered the exact same failure, and which recovery action actually resolved the problem; if there is no valid recovery record, it explicitly says so, letting the agent stop retrying.
In terms of main capabilities, FailEcho provides its service through a Model Context Protocol (MCP) endpoint as well as a REST API. It uses a deterministic approach (not model-dependent) to normalize error text into a fingerprint, and accumulates recovery outcomes for that fingerprint; only when multiple independent reporters agree does it return a recommended action. When evidence is insufficient, it returns INSUFFICIENT_DATA rather than guessing. Confidence uses the Wilson score lower bound and can be recomputed by the user from the returned counts. The system stores only failure metadata, with no fields for prompts, tool parameters, tool results, request or response bodies, request headers, or cookies. The only optional free-text field is the error message, which is off by default; when enabled, it is first normalized (identifiers replaced, credential-shaped strings masked) and the original text is discarded.
In terms of usage, the official offering provides multiple integration paths. The simplest is to let the agent configure itself: paste an instruction into Claude Code, Claude Desktop, Cursor, Codex, or your own harness, and the agent reads the machine-readable guide and completes configuration on its own, with no account, API key, or registration required. You can also manually configure the MCP endpoint, for example by writing a configuration block in Claude Code's .mcp.json, Cursor's .cursor/mcp.json, or VS Code's .vscode/mcp.json. Claude Code can also write it automatically via the CLI. Python users can copy the client/ directory into their project, use observe_tool_call to automatically report success or failure, and query FailEcho on failure, returning a FailureDecision; this wrapper never automatically retries, refreshes, or falls back, because performing a recovery action may cause duplicate submissions or duplicate charges, and the decision-making authority is left to the user. All calls are fail-soft: timeouts or an unreachable host are swallowed, and tool results are returned as usual; setting FAILECHO_DISABLED=1 makesthe entire client a no-op. There is also a Claude Code plugin that, once installed, registers an MCP server and a hook that runs after every MCP tool call, automatically reporting failures, using successes to provide the denominator for the failure rate, and recording the second attempt as the recovery action.
Applicable scenarios include: an agent needing to decide, after a tool call fails, whether to retry, switch models, wait, refresh the schema, remove optional fields, reconnect, use a fallback, re-authenticate, or abort; and autonomous software systems that want to share failure recovery experience across agents and reduce ineffective retries.
In terms of limitations, the official documentation explicitly states that this is not an observability platform, error database, uptime monitor, or LLM debugger. As of the information provided, the number of independent users is 0, and the laboratory measurement period was September 22 to 24, 2026; specific numbers, samples, and significance tests are in docs/claims.md. Other frameworks (LangChain, LlamaIndex, CrewAI, OpenAI Agents SDK, Claude Code hooks) have not yet been built. The Python package has not yet been published to PyPI. The local stdio relay failecho-mcp is a forwarding relay; it has no database of its own and stores nothing.
