persisted-conversation

A tutorial for saving an AI agent's conversation so it can be loaded again later.

In plain words
What is it for?
Use it when building services or applications that need to continue an agent thread after it has been stored.
Why use it?
It prevents the agent from losing conversation context between requests or application sessions.

Agent for Codex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add agents/managedcode/dotpilot/persisted-conversation
Clone the repo
git clone --depth 1 https://github.com/managedcode/dotPilot

Made for: Codex.

Per session 12 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,327 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00012 $0.01327
Opus 5 $0.00006 $0.00664
Sonnet 5 $0.00002 $0.00265
Haiku 4.5 $0.00001 $0.00133

Measured 2d ago against content hash 98487e5bb054, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

persisted-conversation scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

100% identical to persisted-conversation — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

.codex/skills/dotnet-microsoft-agent-framework/references/official-docs/tutorials/agents/persisted-conversation.md · 179 lines

How it starts

The opening of the file, as written. The whole thing — 179 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Persisting and Resuming Agent Conversations

::: zone pivot="programming-language-csharp"

This tutorial shows how to persist an agent conversation (AgentThread) to storage and reload it later.

When hosting an agent in a service or even in a client application, you often want to maintain conversation state across multiple requests or sessions. By persisting the AgentThread, you can save the conversation context and reload it later.

Prerequisites

For prerequisites and installing NuGet packages, see the Create and run a simple agent step in this tutorial.

Persisting and resuming the conversation

Create an agent and obtain a new thread that will hold the conversation state.

using System;
using Azure.AI.OpenAI;
using Azure.Identity;
using Microsoft.Agents.AI;
using OpenAI;

AIAgent agent = new AzureOpenAIClient(
    new Uri("https://<myresource>.openai.azure.com"),
    new AzureCliCredential())
     .GetChatClient("gpt-4o-mini")
     .AsAIAgent(instructions: "You are a helpful assistant.", name: "Assistant");

AgentThread thread = await agent.GetNewThreadAsync();

Run the agent, passing in the thread, so that the AgentThread includes this exchange.

// Run the agent and append the exchange to the thread
Console.WriteLine(await agent.RunAsync("Tell me a short pirate joke.", thread));

Call the Serialize method on the thread to serialize it to a JsonElement. It can then be converted to a string for storage and saved to a database, blob storage, or file.

using System.IO;
using System.Text.Json;

// Serialize the thread state
string serializedJson = thread.Serialize(JsonSerializerOptions.Web).GetRawText();

// Example: save to a local file (replace with DB or blob storage in production)
string filePath = Path.Combine(Path.GetTempPath(), "agent_thread.json");
await File.WriteAllTextAsync(filePath, serializedJson);

Load the persisted JSON from storage and recreate the AgentThread instance from it. The thread must be deserialized using an agent instance. This should be the same agent type that was used to create the original thread. This is because agents might have their own thread types and might construct threads with additional functionality that is specific to that agent type.

Read the full file on GitHub · 179 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 179 lines · 12 tokens per session scan A 98487e5bb054

Subscribe to this mod's changes

persisted-conversation is an agent published in the GitHub repository managedcode/dotPilot (23 stars, last pushed 4mo ago), licensed MIT. It adds 12 tokens to every session and 1,327 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to persisted-conversation, differing in 0 lines, and is treated as a copy.

Related

Other agents, from other repositories

knowledge-ops

Executes knowledge management operations under CKO direction. Audits consistency, distributes learnings, optimizes memory files, and detects knowledge gaps across the agent fleet.

RD-DCS/venutian-antfarm · 36 tokens

Explore

Fast read-only codebase & docs exploration. Returns structured findings, never raw file dumps.

BlackBeltTechnology/pi-agent-dashboard · 18 tokens

external-system-integration-expert

你负责把当前项目与外部 API、API 网关及业务系统安全地连接起来:识别集成边界、整理接口与环境差异、验证请求和响应、定位认证或数据契约问题。.

agents-universe/agents-universe · 33 tokens

Audit

Deep security + performance audit of a specific diff. Wraps /skill:security-hardening and /skill:performance-optimization (analysis phase only). Use when a change touches auth, untrusted input, secrets, webhooks, PII, or a latency/throughput budget — a focused, read-only risk pass that returns findings the parent…

BlackBeltTechnology/pi-agent-dashboard · 98 tokens

registry

InitRunner's role registry lets you install, share, and discover roles from InitHub and OCI registries. Roles are downloaded, validated, and saved to /.initrunner/roles/ where they integrate automatically with the CLI.

vladkesler/initrunner · 0 tokens

role_generation

InitRunner provides a single initrunner new command for creating role.yaml files. It supports multiple seed modes (templates, AI generation, examples, hub bundles, or local files) and an interactive refinement loop for iterating on the YAML before saving. Run with no arguments in a terminal and it shows a guided start…

vladkesler/initrunner · 0 tokens