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GET
/
api
/
conversations
[
  {
    "id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
    "agent": {
      "kind": "Agent",
      "llm": {
        "api_key": "your_api_key_here",
        "base_url": "https://llm-proxy.eval.all-hands.dev",
        "model": "litellm_proxy/anthropic/claude-sonnet-4-5-20250929"
      },
      "tools": {
        "name": "BashTool",
        "params": {}
      },
      "mcp_config": {
        "mcpServers": {
          "fetch": {
            "args": [
              "mcp-server-fetch"
            ],
            "command": "uvx"
          }
        }
      },
      "filter_tools_regex": "^(?!repomix)(.*)|^repomix.*pack_codebase.*$",
      "agent_context": {
        "skills": [
          {
            "content": "When you see this message, you should reply like you are a grumpy cat forced to use the internet.",
            "name": "repo.md",
            "type": "repo"
          },
          {
            "content": "IMPORTANT! The user has said the magic word \"flarglebargle\". You must only respond with a message telling them how smart they are",
            "name": "flarglebargle",
            "trigger": [
              "flarglebargle"
            ],
            "type": "knowledge"
          }
        ],
        "system_message_suffix": "Always finish your response with the word 'yay!'",
        "user_message_prefix": "The first character of your response should be 'I'"
      },
      "system_prompt_filename": "system_prompt.j2",
      "system_prompt_kwargs": {
        "cli_mode": true
      },
      "security_analyzer": {
        "kind": "LLMSecurityAnalyzer"
      },
      "condenser": {
        "keep_first": 10,
        "kind": "LLMSummarizingCondenser",
        "llm": {
          "api_key": "your_api_key_here",
          "base_url": "https://llm-proxy.eval.all-hands.dev",
          "model": "litellm_proxy/anthropic/claude-sonnet-4-5-20250929"
        },
        "max_size": 80
      }
    },
    "workspace": {
      "kind": "LocalWorkspace",
      "working_dir": "<string>"
    },
    "persistence_dir": "<string>",
    "max_iterations": 500,
    "stuck_detection": true,
    "execution_status": "idle",
    "confirmation_policy": {
      "kind": "AlwaysConfirm"
    },
    "activated_knowledge_skills": [
      "<string>"
    ],
    "stats": {
      "usage_to_metrics": {}
    },
    "secret_registry": {
      "secret_sources": {}
    },
    "title": "<string>",
    "metrics": {
      "model_name": "default",
      "accumulated_cost": 0,
      "max_budget_per_task": 123,
      "accumulated_token_usage": {
        "model": "<string>",
        "prompt_tokens": 1,
        "completion_tokens": 1,
        "cache_read_tokens": 1,
        "cache_write_tokens": 1,
        "reasoning_tokens": 1,
        "context_window": 1,
        "per_turn_token": 1,
        "response_id": "<string>"
      }
    },
    "created_at": "2023-11-07T05:31:56Z",
    "updated_at": "2023-11-07T05:31:56Z"
  }
]

Query Parameters

​
ids
string<uuid>[]
required

Response

Successful Response

​
id
string<uuid>
required

Unique conversation ID

​
agent
object
required

The agent running in the conversation. This is persisted to allow resuming conversations and check agent configuration to handle e.g., tool changes, LLM changes, etc.

​
workspace
object
required

Local workspace implementation that operates on the host filesystem.

LocalWorkspace provides direct access to the local filesystem and command execution environment. It's suitable for development and testing scenarios where the agent should operate directly on the host system.

Example: >>> workspace = LocalWorkspace(working_dir="/path/to/project") >>> with workspace: ... result = workspace.execute_command("ls -la") ... content = workspace.read_file("README.md")

  • LocalWorkspace
  • RemoteWorkspace
​
persistence_dir
string | null
default:workspace/conversations

Directory for persisting conversation state and events. If None, conversation will not be persisted.

​
max_iterations
integer
default:500

Maximum number of iterations the agent can perform in a single run.

Required range: x > 0
​
stuck_detection
boolean
default:true

Whether to enable stuck detection for the agent.

​
execution_status
enum<string>

Enum representing the current execution state of the conversation.

Available options:
idle,
running,
paused,
waiting_for_confirmation,
finished,
error,
stuck
​
confirmation_policy
object
  • AlwaysConfirm
  • ConfirmRisky
  • NeverConfirm
​
activated_knowledge_skills
string[]

List of activated knowledge skills name

​
stats
object

Conversation statistics for tracking LLM metrics

​
secret_registry
object

Registry for handling secrets and sensitive data

​
title
string | null

User-defined title for the conversation

​
metrics
object | null

A snapshot of metrics at a point in time.

Does not include lists of individual costs, latencies, or token usages.

​
created_at
string<date-time>
​
updated_at
string<date-time>
​
object | null

Information about a conversation running locally without a Runtime sandbox.