fireweed-mcp
An agent memory server that admits only evidence-backed claims, provides tamper-evident recall and verification, and issues signed certificates for erasure, enabling trustworthy and auditable agent memory.
README
fireweed-mcp
<!-- mcp-name: io.github.Starksood/fireweed-mcp -->
Agent memory where every fact carries a receipt.
remember(claim = "Priya joined Acme in 2019 under duress.",
evidence = "Priya Raman joined Acme in 2019 as a logistics analyst.")
REFUSED (asserts_more_than_evidence) — the claim adds something the evidence does not say.
claim : Priya joined Acme in 2019 under duress.
evidence: Priya Raman joined Acme in 2019 as a logistics analyst.
recall("Priya's salary")
ABSTAINED (unknown_predicate) — no claims ground "salary"; 1 claim about Priya Raman exists
This is a refusal, not an empty result.
forget("Priya")
ERASED Priya Raman — certificate issued
signature : hmac-sha256:f4d0768ef3b0fec624afec12f25bfd91…
nodes in closure : 1
every probe abstains : True
bystanders surviving : 1
That last one is the artifact behind "delete me from your agent's memory — and prove it."
Install
uvx fireweed-mcp # try it
pip install fireweed-mcp # keep it
claude mcp add fireweed -- uvx fireweed-mcp
No dependencies. No API keys. No model — nothing in this server calls an LLM.
What it does
| tool | |
|---|---|
remember |
admits a claim only if the evidence you cite supports it. Refusals are typed and say what to fix. |
recall |
grounded claims with the byte range they came from; abstains and names the term it could not ground |
verify_receipts |
re-hash every source, re-slice every range — tamper-evident |
forget |
erasure with exact closure and a signed certificate; bystanders survive |
export_memory |
the whole substrate as a portable open-format blob |
Why the refusals are the point
Most memory servers store what the model says and return what's nearest. This one adjudicates.
The rule is the model proposes, deterministic code decides. Across an RPC boundary that stops being a slogan: your agent is the proposer, and it cannot talk its way past the gate, because the gate is not a prompt. Pass a claim and the text you're quoting; pure functions check that the evidence names the subject, preserves the relation, invents no numbers, and asserts nothing the span doesn't say. What survives is stored with a byte range into the source.
Then anyone can check it afterwards — including someone who trusts neither your agent nor this server. That is the whole product.
What it does NOT do
Stated up front, because this project's last headline number turned out to be measuring nothing (see the retraction, which ships with a script that proves it):
- It does not extract memories from free text. You supply the claim and the evidence. Automatic extraction needs a perceiver model; this server deliberately has none.
- It does not make an LLM truthful. It governs what enters the record and what can be proven about it. Your model can still say whatever it likes in its own prose.
- Conversational recall is weak, and measured. On a 1,200-item adversarial corpus the retrieval gate abstains on only 40% of questions whose answer is genuinely absent — it checks that the question's topic is grounded, not that the asked-for value exists. The write path, receipts and erasure are unaffected and are the parts to rely on.
Your data
~/.fireweed/mcp/ (FIREWEED_MCP_STORE to change). The substrate is an open format — see
open_format/SPEC.md — and open_format/reference_reader.py reads it with
the standard library alone. Your memory outlives this server, this engine, and any model. A test
asserts that round trip.
Optional: pip install "fireweed-mcp[semantic]" enables paraphrase matching in recall. Without
it the gate refuses more — the safe direction — and memory_stats tells you which mode you're in.
License
FSL-1.1-ALv2 — source-available. Free for everything except building a competing product;
converts to Apache 2.0 on 2028-01-01. Full text in LICENSE.md.
Want to use Fireweed in a commercial product or competing service? → sanyamsood2@gmail.com
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.