Session - PraisonAI

The session command manages conversation sessions, allowing you to save, resume, and organize multi-turn interactions.

Quick Start

# List sessions for current project
praisonai session list

# List sessions across all projects
praisonai session list --all

# Start a new session
praisonai session start my-project

Commands

Start a Session

praisonai session start my-project

Expected Output:

🆕 Starting new session: my-project

Session created successfully!
┌─────────────────────┬────────────────────────────┐
│ Property            │ Value                      │
├─────────────────────┼────────────────────────────┤
│ Session ID          │ my-project                 │
│ Created             │ 2024-12-16 15:30:00        │
│ Status              │ active                     │
│ Messages            │ 0                          │
└─────────────────────┴────────────────────────────┘

You can now run commands with this session context.
Use: praisonai "your prompt" --session my-project

List Sessions

praisonai session list

Expected Output:

ID         Name            Status   Events  Tokens   Cost     Updated
abc12345   research-bot    active   12      12,345   $0.0140  2026-06-29 23:48 UTC
def67890   summariser      paused   3       420      $0.0004  2026-06-29 21:11 UTC

Sessions that have not yet accumulated any usage show - in the Tokens and Cost columns.

JSON output — pass --json to get machine-readable output with usage data per session:

praisonai session list --json

[
  {
    "id": "abc12345",
    "name": "research-bot",
    "status": "active",
    "events": 12,
    "usage": {
      "input_tokens": 10000,
      "output_tokens": 2345,
      "cached_tokens": 0,
      "total_tokens": 12345,
      "cost": 0.014,
      "requests": 3
    },
    "total_tokens": 12345,
    "cost": 0.014,
    "updated": "2026-06-29T23:48:00Z"
  }
]

session list (no --project, no --all) merges the current project’s session store with the global default store — every session --continue/resume could see, in one list, deduped by session id (freshest updated_at wins when the same id lives in both stores). Use --all to include every project’s sessions, or --project <id> to restrict to one project’s store only. Cross-store merge landed with the fix for PraisonAI #2655.

What shows up

Without --all or --project, the default listing surfaces:

Sessions created by praisonai run (project store) ✅
Sessions created by chat, gateway, TUI, API, or a bare Agent(session_id=...) (global default store) ✅ new
Sub-agent / forked child sessions still appear here, but --continue skips them in favour of the last root session.

Passing --project <id> stays project-scoped only; --all widens to every project.

Token / Cost columns

The Tokens and Cost columns show cumulative totals across all runs for each session. Totals are persisted in session-store metadata under usage and updated automatically by praisonai run / direct prompts whenever --session is set or a default project session is active. A - is rendered when no usage has been recorded yet. JSON output (--json):

{
  "sessions": [
    {
      "session_id": "session-abc123",
      "total_tokens": 5220,
      "cost": 0.014,
      "usage": {
        "input_tokens": 1240,
        "output_tokens": 3980,
        "cached_tokens": 0,
        "total_tokens": 5220,
        "cost": 0.014,
        "requests": 2
      }
    }
  ]
}

See Cost Tracking for how per-session totals accumulate across runs. List sessions across all projects:

praisonai session list --all

List sessions for a specific project:

praisonai session list --project a1b2c3d4

Flag	Description
(default — no flag)	Merges the current project’s store with the global default store, deduped by session id
`--all`	Show sessions from all projects
`--project <id>`	Show sessions for a specific project ID — that project’s store only
`--json`	Output as JSON (includes `usage`, `total_tokens`, and `cost` per session)

Resume a Session

session resume restores chat history, model, and agent name from a previous session. History is preserved by default via compact retention — older turns are summarised and archived rather than dropped. See Session Persistence — Retention Policies.

praisonai session resume my-project

Expected Output:

🔄 Session Resumed
┌─────────────────────────────────────────────┐
│ Session: my-project                         │
│ Model:   gpt-4o                             │
│ Messages restored: 12                       │
│ Usage:   10,000 in / 2,345 out · $0.0140   │
└─────────────────────────────────────────────┘

--- Restored Conversation ---
[user] Can you explain the authentication flow?
[assistant] Based on the code...

The Usage: line shows cumulative tokens and cost accumulated across all previous prompts in this session. If no usage has been recorded yet, the line is omitted.

Resume and continue with a prompt

praisonai session resume my-project "Now refactor the auth module"

State is rehydrated, then the prompt runs through the shared praisonai run --session <id> path. The resume panel is suppressed when a prompt is provided — the run pipeline emits the only top-level output.

Show transcript only (legacy view)

praisonai session resume my-project --transcript

Use --transcript to inspect a session without restoring state. The panel title shows “Session Transcript”.

Cross-store lookup

session resume finds a session whether it was created via praisonai run --continue (project store) or via the gateway/TUI (global store). See Storage Backends.

When you pass a continuation prompt, the resume panel is suppressed — the run pipeline emits the only top-level output. To inspect a session without continuing, use --transcript or omit the prompt.

Cost & Token Tracking

PraisonAI accumulates input/output/cached tokens and dollar cost on every session run, so you can see exactly what a conversation has spent.

Quick Start

Run with a session

praisonai run "Summarise the Q3 report" --session q3-review

After the answer, the CLI prints a one-line footer:

1,240 in / 3,980 out · $0.0140

Check cumulative usage

praisonai session list

ID          Name        Status   Events   Tokens    Cost      Updated
q3-review   q3-review   active   1        12,440    $0.0710   10:14

Continue — totals keep accumulating

praisonai run "Drill into the revenue line" --session q3-review

Totals continue from where you left off — they do not reset.

What gets persisted

Each session stores a usage object in its metadata:

Field	Type	Default	Description
`input_tokens`	`int`	`0`	Cumulative prompt tokens across the session.
`output_tokens`	`int`	`0`	Cumulative completion tokens across the session.
`cached_tokens`	`int`	`0`	Cumulative cached tokens (provider-reported).
`total_tokens`	`int`	`0`	`input + output` total.
`cost`	`float`	`0.0`	Cumulative dollar cost rounded to 6 decimal places.
`requests`	`int`	`0`	Cumulative number of LLM interactions.

{
  "session_id": "q3-review",
  "metadata": {
    "usage": {
      "input_tokens": 1240,
      "output_tokens": 3980,
      "cached_tokens": 0,
      "total_tokens": 5220,
      "cost": 0.014,
      "requests": 1
    },
    "total_tokens": 5220,
    "cost": 0.014
  }
}

After each prompt run with an active session, the CLI prints:

1,240 in / 3,980 out · $0.0140

Format: "{input:,} in / {output:,} out · ${cost:.4f}" — locale-formatted integers, 4-decimal cost. The footer is suppressed in --json mode but usage is still persisted.

Resume behaviour

Totals are rehydrated on --continue / --session <id> and keep accumulating — they do not reset. The resume panel shows a usage summary line:

╭─ Session Resumed: q3-review ──────────────────────────╮
│ Session: q3-review                                    │
│ Model:  gpt-4o-mini                                   │
│ Messages restored: 3                                  │
│ Usage:  12,440 tokens · $0.0710 (3 requests)          │
╰───────────────────────────────────────────────────────╯

How it works

Reading usage programmatically

from praisonai.cli.state.project_sessions import (
    read_session_usage,
    format_usage_footer,
)

usage = read_session_usage("q3-review")
print(format_usage_footer(usage))
# 1,240 in / 3,980 out · $0.0140

print(f"Cumulative requests: {usage['requests']}")
print(f"Cached tokens: {usage['cached_tokens']:,}")

Notes

Best-effort — never breaks a run

Any failure (pricing lookup, persistence error, missing collector) leaves the session untouched. Usage accounting never breaks a run.

Multi-model aware

When a run uses more than one model, each model’s tokens are priced individually with get_pricing(model_name).

Cached tokens tracked separately

Provider-reported cached reads are accumulated in cached_tokens but excluded from cost — the provider already discounts them.

Footer only appears with an active session

One-shot praisonai run "..." without --session runs without footer output.

Show Session Details

praisonai session show my-project

Expected Output:

📊 Session Details: my-project

┌─────────────────────┬────────────────────────────┐
│ Property            │ Value                      │
├─────────────────────┼────────────────────────────┤
│ Session ID          │ my-project                 │
│ Created             │ 2024-12-16 15:30:00        │
│ Last Active         │ 2024-12-16 15:45:00        │
│ Status              │ active                     │
│ Total Messages      │ 12                         │
│ User Messages       │ 6                          │
│ Agent Messages      │ 6                          │
│ Total Tokens        │ 4,523                      │
│ Storage Size        │ 45 KB                      │
└─────────────────────┴────────────────────────────┘

Recent Messages:
────────────────────────────────────────────────────
[User] Can you explain the authentication flow?
[Agent] Based on the code, the authentication...
────────────────────────────────────────────────────
[User] How do I add OAuth support?
[Agent] To add OAuth support, you would need to...
────────────────────────────────────────────────────

Delete a Session

praisonai session delete my-project

Expected Output:

⚠️  Delete session 'my-project'?
This will permanently remove all conversation history.
Are you sure? (y/N): y

🗑️  Session 'my-project' deleted successfully.

Help

praisonai session help

Expected Output:

Session Commands:

  praisonai session start <name>    - Start a new session
  praisonai session list            - List all sessions
  praisonai session resume <id> [prompt]      - Resume a session, optionally with a prompt
                                --transcript  - Show transcript only (legacy view)
  praisonai session show <name>     - Show session details
  praisonai session delete <name>   - Delete a session
  praisonai session help            - Show this help

Using Sessions with Prompts:
  praisonai "prompt" --session <name>   - Run with session context

Token and Cost Tracking

Every session accumulates cumulative token usage and cost across all prompts, visible in session list and the resume panel. After each run, a single-line footer is printed:

$ praisonai run "Summarise yesterday's PRs"
... (agent output) ...
1,240 in / 3,980 out · $0.0140

The middle dot is U+00B7 (·). The footer reflects cumulative totals since session start, not just the last prompt. It is silently suppressed in JSON mode (--json / --output json) — there is no --no-usage flag. When you resume a session, the cumulative totals are rehydrated so subsequent prompts keep accumulating:

$ praisonai run --continue
Session: research-bot
Model:   gpt-4o-mini
Messages restored: 12
Usage:   12,345 in / 38,902 out · $0.0140

Working with `praisonai run`

The same project-scoped store powers --continue and --session on praisonai run. As of PR #1963, every surface restores history from and saves to this store:

`praisonai run` surface	Restores history?	Saves new messages?
Prompt mode (`praisonai run "..."`)	Yes	Yes (unless `--no-save`)
YAML / file mode (`praisonai run agents.yaml`)	Yes	Yes (unless `--no-save`)
Actions mode (`--output actions`)	Yes	Yes (unless `--no-save`)

As of the fix for issue #2700, the actions-mode row is fully honoured for --auto-save, --session, --continue, and --fork. If you hit a TypeError about auto_save on an older praisonai-code build, upgrade and retry — see the run.mdx troubleshooting section. As of PR #2277, --session <id> and --continue now persist model and agent_name into session metadata so a later session resume reproduces the same configuration deterministically. For advanced programmatic use, the rehydrate_session helper in praisonai.cli.session returns a RehydratedSession with session_id, chat_history, model, agent_name, metadata, and found fields — see the SDK reference for details.

praisonai run --continue "Add tests for the new endpoint"

See Run for complete session continuity documentation.

Using Sessions with Prompts

Continue a Conversation

# First message
praisonai "What is Python?" --session learning

# Follow-up (context preserved)
praisonai "How do I install it?" --session learning

# Another follow-up
praisonai "Show me a hello world example" --session learning

Expected Output (third message):

📂 Session: learning (3 messages)

╭────────────────────────────────── Response ──────────────────────────────────╮
│ Based on our conversation about Python, here's a hello world example:        │
│                                                                              │
│ ```python                                                                    │
│ print("Hello, World!")                                                       │
│ ```                                                                          │
│                                                                              │
│ After installing Python as we discussed, save this to a file called         │
│ `hello.py` and run it with `python hello.py`                                │
╰──────────────────────────────────────────────────────────────────────────────╯

Session with Other Features

# Session with memory
praisonai "Remember my preferences" --session project --memory

# Session with knowledge
praisonai "Search the docs" --session project --knowledge

# Session with planning
praisonai "Plan the implementation" --session project --planning

Use Cases

Project-Based Conversations

# Start project session
praisonai session start website-redesign

# Multiple conversations over time
praisonai "What's the current design?" --session website-redesign
praisonai "Suggest improvements" --session website-redesign
praisonai "Create implementation plan" --session website-redesign

Learning Sessions

# Create learning session
praisonai session start learn-rust

# Progressive learning
praisonai "Explain ownership in Rust" --session learn-rust
praisonai "Show me an example" --session learn-rust
praisonai "What about borrowing?" --session learn-rust

Code Review Sessions

# Start review session
praisonai session start pr-review-123

# Review conversation
praisonai "Review this PR" --session pr-review-123 --fast-context ./src
praisonai "What about security concerns?" --session pr-review-123
praisonai "Summarize the review" --session pr-review-123

Auto-Save Sessions

Automatically save sessions after each agent run using the --auto-save flag:

# Auto-save session with each interaction
praisonai "Analyze this code" --auto-save my-project

# Continue the conversation (auto-saved)
praisonai "Now refactor it" --auto-save my-project

Python API

from praisonaiagents import Agent

from praisonaiagents.config.feature_configs import MemoryConfig

agent = Agent(
    name="Assistant",
    memory=MemoryConfig(auto_save="my-project")  # Auto-save session after each run
)

agent.start("Analyze this code")  # Session saved automatically

History in Context

Load conversation history from previous sessions into the current context:

# Load history from last 5 sessions
praisonai "Continue our discussion" --history 5

Python API

from praisonaiagents import Agent

agent = Agent(
    name="Assistant",
    memory=True,
    context=True,  # Enable context management for history
)

# Agent now has context from previous sessions
agent.start("What did we discuss yesterday?")

Workflow Checkpoints

Save and resume workflow execution at any step:

from praisonaiagents import AgentFlowManager

manager = WorkflowManager()

# Execute with checkpoints (saves after each step)
result = manager.execute(
    "deploy-workflow",
    checkpoint="deploy-v1"
)

# Resume from checkpoint if interrupted
result = manager.execute(
    "deploy-workflow",
    resume="deploy-v1"
)

# List all checkpoints
checkpoints = manager.list_checkpoints()

# Delete a checkpoint
manager.delete_checkpoint("deploy-v1")

Checkpoint Storage

.praison/
└── checkpoints/
    ├── deploy-v1.json
    └── build-v2.json

Project-Scoped Sessions

Sessions are automatically scoped to your current project. PraisonAI detects your project by finding the git repository root, or uses the current working directory as a fallback. Project identification:

Project ID: First 8 characters of SHA256 hash of project root path
Git detection: Uses git rev-parse --show-toplevel with 5-second timeout
Fallback: Current working directory if not in a git repository

Storage structure:

~/.praisonai/sessions/
├── projects/
│   ├── abc12345/          # Project sessions
│   │   ├── session-def789.json
│   │   └── session-ghi012.json
│   └── xyz98765/          # Another project
│       └── session-jkl345.json
└── global/                # Legacy global sessions
    ├── old-session.json
    └── another.json

Session Storage

Sessions are stored in a project-scoped layout when using the default behavior:

~/.praisonai/sessions/projects/{project_id}/
└── {session_id}.json

With project-scoped sessions, your sessions are organized by project automatically. Legacy sessions remain accessible via the --all flag:

~/.praisonai/
└── memory/
    └── praison/
        └── sessions/
            ├── my-project.json
            ├── research-task.json
            └── code-review.json

Storage Backend Options

Store sessions in different backends for production deployments:

# List sessions with SQLite backend
praisonai session list --storage-backend sqlite --storage-path ~/.praisonai/sessions.db

# List sessions with Redis backend (for distributed systems)
praisonai session list --storage-backend redis://localhost:6379

# List sessions with file backend (default)
praisonai session list --storage-backend file --storage-path ~/.praisonai/sessions

Backend	Best For
`file`	Development, debugging
`sqlite`	Production, concurrent access
`redis://url`	Distributed systems, shared sessions

See Storage Backends for more details.

Concurrent Sessions

Multiple praisonai processes can safely share the same session — the CLI store reloads, merges, and writes under an exclusive lock so no messages are lost when the TUI, --interactive mode, and praisonai "…" --session all touch the same file.

# Terminal 1: keep the TUI open on a session
praisonai tui launch --session my-project

# Terminal 2: same session, ad-hoc message via --interactive
praisonai "Add a one-line summary" --interactive --session my-project

# Both messages end up in ~/.praisonai/sessions/my-project.json
# in arrival order — no silent drops.

Merge Strategy

When two writers race, the session store merges their changes:

Field	Merge strategy
`messages`	Union, deduped by `(role, content, timestamp)`; on-disk order preserved, new messages appended
`metadata`	Dict merge, incoming wins on key conflict
`total_input_tokens` / `total_output_tokens` / `total_cost` / `request_count`	`max(on_disk, incoming)`
`current_model`	Incoming if set, else on-disk
`updated_at`	`max(on_disk, incoming)`

Lost-Update Prevention

praisonai session show and praisonai session resume always reflect the latest on-disk state — the in-process cache is invalidated automatically when another process writes (mtime-based check). This concurrent-save safety was added in PR #1854. For the equivalent feature in the SDK-level store, see Multi-Process Safety.

This applies to the default file-backed session store. The sqlite / redis:// backends in the Storage Backend Options table above handle concurrency via the database itself; the CLI does not add its own merge layer there.

Cross-Platform Support

The praisonai session commands work on Windows, macOS, and Linux — file locking is automatic and platform-appropriate.

Platform	File locking	Notes
Linux / macOS	`fcntl.flock()`	Exclusive on write, shared on read
Windows	Whole-file lock (`max(file_size, 1)` bytes) — matches Unix `fcntl.flock` semantics	Blocking exclusive on write, shared (`LK_RLCK`) on read
Other (Pyodide, minimal embedded CPython)	None — logs a one-time warning	Single-process is safe; concurrent writers may corrupt session files

If you see this warning: File locking unavailable on this platform (fcntl not available); concurrent writers may corrupt session files.This means you’re running on an environment without native file locking. Restrict to a single process, or migrate to a DB-backed storage backend (link to the sqlite / redis options in the Storage Backend Options table above).

On Windows, sessions are stored under %USERPROFILE%\.praison\sessions\{session_id}.json following the OS convention via Path.home(). Cross-platform locking was added in PR #1837. Concurrent multi-process writes (e.g. TUI + praisonai --interactive sharing the same session directory) are preserved without message loss as of PR #1885 and PR #1892. For the SDK-level session store with the same cross-platform guarantees, see Session Persistence. Example usage across platforms:

# Windows (PowerShell)
praisonai session start my-project
praisonai "Analyze this file" --session my-project

# Same commands work identically on macOS / Linux

Best Practices

Use descriptive session names that reflect the project or task for easy identification.

Long sessions accumulate tokens. Consider starting fresh sessions for unrelated topics.

Naming

Use descriptive names like project-auth-feature

Organization

Create separate sessions for different projects

Cleanup

Delete old sessions to free up storage

Context

Start new sessions when changing topics significantly

Run Command

Session flags and usage footer for praisonai run

Session Persistence

SDK-level session management

Cost Tracking

Per-session persistence and /cost slash command

Project Sessions

Persisted usage shape and project scoping

​Quick Start

​Commands

​Start a Session

​List Sessions

​What shows up

​Token / Cost columns

​Resume a Session

​Resume and continue with a prompt

​Show transcript only (legacy view)

​Cross-store lookup

​Cost & Token Tracking

​Quick Start

​What gets persisted

​Footer format

​Resume behaviour

​How it works

​Reading usage programmatically

​Notes

​Show Session Details

​Delete a Session

​Help

​Token and Cost Tracking

​Working with praisonai run

​Using Sessions with Prompts

​Continue a Conversation

​Session with Other Features

​Use Cases

​Project-Based Conversations

​Learning Sessions

​Code Review Sessions

​Auto-Save Sessions

​Python API

​History in Context

​Python API

​Workflow Checkpoints

​Checkpoint Storage

​Project-Scoped Sessions

​Session Storage

​Storage Backend Options

​Concurrent Sessions

​Merge Strategy

​Lost-Update Prevention

​Cross-Platform Support

​Best Practices

Naming

Organization

Cleanup

Context

​Related

Run Command

Session Persistence

Cost Tracking

Project Sessions

Quick Start

Commands

Start a Session

List Sessions

What shows up

Token / Cost columns

Resume a Session

Resume and continue with a prompt

Show transcript only (legacy view)

Cross-store lookup

Cost & Token Tracking

Quick Start

What gets persisted

Footer format

Resume behaviour

How it works

Reading usage programmatically

Notes

Show Session Details

Delete a Session

Help

Token and Cost Tracking

Working with `praisonai run`

Using Sessions with Prompts

Continue a Conversation

Session with Other Features

Use Cases

Project-Based Conversations

Learning Sessions

Code Review Sessions

Auto-Save Sessions

Python API

History in Context

Python API

Workflow Checkpoints

Checkpoint Storage

Project-Scoped Sessions

Session Storage

Storage Backend Options

Concurrent Sessions

Merge Strategy

Lost-Update Prevention

Cross-Platform Support

Best Practices

Related